Key Enabling Technologies for 6G: The Role of UAVs, Terahertz Communication, and Intelligent Reconfigurable Surfaces in Shaping the Future of Wireless Networks

Abstract

Sixth-generation (6G) wireless networks have the potential to transform global connectivity by supporting ultra-high data rates, ultra-reliable low latency communication (uRLLC), and intelligent, adaptive networking. To realize this vision, 6G must incorporate groundbreaking technologies that enhance network efficiency, spectral utilization, and dynamic adaptability. Among them, unmanned aerial vehicles (UAVs), terahertz (THz) communication, and intelligent reconfigurable surfaces (IRSs) are three major enablers in redefining the architecture and performance of next-generation wireless systems. This survey provides a comprehensive review of these transformative technologies, exploring their potential, design challenges, and integration into future 6G ecosystems. UAV-based communication provides flexible, on-demand communication in remote, harsh areas and is a vital solution for disasters, self-driving, and industrial automation. THz communication taking place in the 0.1–10 THz band reveals ultra-high bandwidth capable of a data rate of multi-gigabits per second and can avoid spectrum bottlenecks in conventional bands. IRS technology based on programmable metasurface allows real-time wavefront control, maximizing signal propagation and spectral/energy efficiency in complex settings. The work provides architectural evolution, active current research trends, and practical issues in applying these technologies, including their potential contribution to the creation of intelligent, ultra-connected 6G networks. In addition, it presents open research questions, possible answers, and future directions and provides information for academia, industry, and policymakers.

1. Introduction

Over the past few decades, the global wireless communication network has transitioned from the first-generation (1G) to the fifth-generation (5G). This continuous evolution in cellular wireless networks is driven by the need for higher data rates, increased network capacity, lower latency, and improved connectivity. In late 2017, 3GPP-Release 15 standardized the first phase of 5G cellular networks, offering a flexible network design to accommodate a wide range of applications, including enhanced mobile broadband, basic ultra-reliable low-latency communications (uRLLC), and massive machine-type communications (mMTC) [1]. Currently, the standardization of the 5G mobile communication system has been implemented in many cities worldwide [2]. It promises to enhance network performance and ensure wide-area connectivity. However, the exponential increase in data traffic due to the vast number of connected devices, predicted to be hundreds per cubic meter, poses a significant challenge.

With the rapid advancement of the Internet of Everything (IoE) over the next decade, many new applications and services are emerging. These include augmented reality (AR), virtual reality (VR), facial recognition, and e-health, which are increasingly interconnected and communicating. Such applications demand extremely high data rates while maintaining reliability and low latency. Additionally, transitioning from the current Industry 4.0 to the upcoming Industry 5.0 paradigm will pose significant challenges [3]. The existing 5G technology will struggle to meet these advanced applications’ enormous data rate and ultra-low latency requirements. As a result, academia, industry, and research communities are shifting their attention toward the upcoming generation of mobile communication networks, i.e., the sixth-generation (6G), which is expected to emerge by 2030 to satisfy future requirements and to manage the huge traffic generated by smart devices and applications [4]. In contrast to its predecessors, 6G will be able to effectively unify artificial intelligence (AI), edge computing, and dynamic network reconfiguration, thereby ushering in an era of ubiquitous intelligence, hyper-connectivity, and extreme spectral efficiency [4,5].

By 2025, the number of global mobile users is expected to reach 13.8 billion and 17.1 billion, and the traffic will reach up to five zettabytes by the end of 2030 [6]. The world’s first 6G Wireless Summit was launched in March 2019 to identify the key challenges, and its vision toward 6G can be summarized by the statement “ubiquitous wireless intelligence” [7]. It is expected that 6G will revolutionize the cellular wireless evolution from “connected things” to “connected intelligence” with more stringent key performance indicators (KPIs) [8]. AI will be the foundation of the 6G evolution, allowing self-managing, intelligent, and adaptive wireless networks. AI shall revolutionize the potential of future wireless networks from autonomous network management and intelligent spectrum allocation to quantum-secured communications and brain–machine interfaces. With the 6G evolving, AI-enabled innovations are going to be the basis of a hyper-connected, intelligent digital environment, providing seamless, secure, and ultra-reliable communication across a wide range of applications and sectors [9].

Unmanned aerial vehicles (UAVs), terahertz (THz) communication, and intelligent reconfigurable surfaces (IRSs) are among the most promising technologies driving the 6G revolution [5,10]. UAV-enabled networks will extend coverage in both disconnected and mobile settings, providing flexible, on-demand communications for applications related to, e.g., disaster relief, autonomous mobility, and industrial automation [10]. The THz communication is designed to offer multi-gigabit-per-second data rates, which can help us alleviate the spectrum congestion problem highlighted by the existing wireless systems. In the meantime, reconfigurable intelligent surfaces (RISs) will dynamically control the electromagnetic wave, and the spectral efficiency of signal propagation will be maximized in dense urban and indoor environments [11].

Communication at THz frequencies is expected to become a key of 6G wireless networks, supporting applications that demand extremely high speed, ultralow, and secure communication. Its high bandwidth can accommodate new applications like real-time digital twins, immersive VR, and telemedicine surgery [11]. In addition, combining THz communication with complementary communication systems (e.g., RIS, quantum communication (QC), and AI network optimization) will be the next-generation driving forces toward 6G [12]. The THz band represents a paradigm shift in wireless communication, combining extraordinary capacity, security, and resilience. Although existing challenges (high propagation loss, transceiver design efficiency, and low cost of deployment) need to be addressed, research and standardization activities continue to build the foundation for its inclusion in 6G networks and will drive the evolution of global connectivity in the future [11,13].

This survey presents an exhaustive overview of key enabling technologies of 6G and addresses their architectural improvements, design issues, and implementation into 6G networks. The work considers three main technologies: UAVs, THz, and IRS. Additionally, the proposed work describes recent work in progress, open problems, and future directions in UAV-aided communication, THz spectrum application, and IRS deployment. It discusses how they will contribute to the future of the wireless ecosystem.

Table 1. Key notation.

Notation	Description	Notation	Description
1G	First generation	uMUB	Ubiquitous mobile ultra-broadband
4G	Fourth generation	BCI	Brain–computer interface
5G	Fifth generation	uHDD	Ultra-high data density
6G	Sixth generation	AI	Artificial intelligence
UAV	Unmanned aerial vehicle	IoT	Internet of Things
EB	Exabytes	AR	Augmented reality
3GPP	Third Generation Partnership Project	VR	Virtual reality
3D	Three-dimensional	MR	Mixed reality
KPI	Key performance indicator	RAN	Radio Access Network
mMTC	Massive machine-type communications	uHSLLC	Ultra-high-speed-with-low-latency communications
eMBB	enhanced Mobile Broadband	THz	Terahertz
uRLLC	Ultra-reliable and low-latency communications	CaeC	Contextually Agile eMBB communications
RIS	Reconfigurable intelligent surfaces	UPF	User plane function
RTBC	Real-time broadband communication	DIoE	Device-independent Internet of Everything
UCBC	Uplink-centric broadband communication	ETSI	European Telecommunications Standards Institute
HCS	Harmonized communication and sensing	COC	Computational oriented communication
Gbps	Gigabits-per-second	ZSM	Zero-touch service management
Tbps	Terabit per second	E2E	End-to-end
VLC	Visible light communication	EDuRLLC	Event-defined uRLLC
mmWave	Millimeter-wave	QoS	Quality of service
LOS	Line of sight	MEC	Mobile edge computing
IoE	Internet of everything	MIMO	Multi-input multi-output
RF	Radiofrequency	CL-MIMO	Co-located MIMO
NLOS	Non-line-of-sight	DM-MIMO	Distributed massive MIMO
EI	Edge intelligence	QML	Quantum machine learning
LED	Light emitting diode	QKD	Quantum key distribution
MAC	Medium access control	B-RAN	Blockchain radio access network
IIoT	Industrial Internet of Things	QoE	Quality of experience
BS	Base station	FMV	Full motion video
CR	Cognitive radio	RRS	Reconfigurable radio system
VLB	Visible light band	XAI	Explainable AI
IRS	Intelligent reflecting surfaces	IEC	Intelligence edge computing
ENI ISG	Experiential Networked Intelligence Industry Specification Group	IEEE	Institute of Electrical and Electronics Engineers
DSA	Dynamic spectrum access	WPAN	Wireless personal area networking
ISG	Industry specification group	XR	Extended reality
FPGA	Field-programmable gate array	CAGR	Compound annual growth rate
D2D	Device-to-device	UM-MIMO	Ultra-massive MIMO
V2X	Vehicle-to-everything	CSI	Channel state information
UWOC	Underwater wireless optical communications	ISTAR	Information, surveillance, target acquisition, and reconnaissance
NR	New radio	WLAN	Wireless local area networking
ML	Machine learning	RRS	Reconfigurable radio system
V2V	Vehicle-to-vehicle	ITS	Intelligent transportation system
FL	Federated learning	SSWM	Site-specific weed management
UE	User equipment	DSMs	Digital surface models
LAN	Local area network	ABS	Aerial base station
V2I	Vehicle-to-infrastructure	RL	Reinforcement learning
EM	Electromagnetic	BC	Blockchain
QC	Quantum communication	TC RRS	Technical Committee on RRS
NTN	Non-terrestrial network	SRE	Smart radio environment
MD	Management domain	PoC	Proof of Concept
ITU	International Telecommunication Union	WRC	World Radiocommunications Conference
ITU-T	ITU-Telecommunication Standardization Sector	E2E SMD	End-to-end service management domain
LEO	Low Earth Orbit	ITU-R	ITU-Radiocommunication Sector
AF/DF	Amplify-and-forward/Decode-and-forward	IMT	International Mobile Telecommunications
MBRLLC	Mobile broadband reliable low latency communications	euRLLC	Enhanced ultra-reliable and low-latency communications
Kph	Kilometers per hour	Tbps	Terabits-per-second

provides all abbreviations used throughout the work to better go through the work.

Survey Methodolgy

The following four processes summarize the methodology used to conduct this survey.

• Search strategy and databases
• Selection criteria
• Review and categorization process
• Quality assessment and reproducibility

This literature review systematically searched IEEE Xplore, ACM Digital Library, Elsevier (ScienceDirect), SpringerLink, and arXiv to identify relevant studies on UAVs, THz communication, and IRS in 6G networks. We considered the following keywords: 6G enabling technologies, 6G UAV communications, UAV cellular connectivity, THz communications for 6G, IRS/RIS for next-generation wireless networks, IRS-assisted 6G networks, and 6G standardization. Additional sources were considered based on 3GPP, ITU, ETSI white papers, technical reports and standards, and references from highly cited survey and review papers.

The following inclusion criteria were considered when selecting the articles.

• Peer-reviewed articles and conference papers published in the last five years (2019–2024).
• Studies addressing technical challenges, architecture, and real-world applications of UAVs, THz, and IRS in 6G.
• Papers that present theoretical foundations, experimental validations, or simulation results relevant to the three key technologies.

However, we excluded studies focusing on 5G without discussing 6G adaptability and articles lacking substantial technical discussion. The selected literature was classified into four primary categories: 6G specifications and enabling technologies, UAV-assisted communications, THz spectrum utilization, and IRS-assisted 6G communications. For each category, we identified key research trends, challenges, and open questions. A comparative analysis was conducted to highlight the synergies between these enablers and their potential integration in 6G networks.

2. Sixth-Generation Use-Cases

Numerous new use cases will be supported by 6G, which will cover a wide range of applications. The 6G use cases can be seen as an evolution to the current 5G. According to the 5G, there are three main use cases, including eMBB, mMTC, and uRLLC. Recently, three novel use cases for 5.5G have been added to the main three use cases, which are RTBC, UCBC, and HCS [14]. These use cases are extended to another three dimensions in 6G to include uMUB, uHSLLC, and uHDD [15]. The advancements in 6G technology are anticipated to bring significant performance improvements to various communication technologies. This section considers the potential use cases of 6G networks. Table 2 summarizes the main use cases and introduces the features and requirements of each case.

• Augmented and virtual reality

AR is an emerging and promising communication method that is widely recognized as a primary use case for 6G technology. VR technology creates a simulated environment with a first-person view that users can interact with using specialized equipment, such as a headset. This allows the user to fully immerse themselves in a virtual world and experience it as if they were there. VR is used for a range of purposes, such as gaming, tourism, sport, education, and therapy. VR technology has the potential to facilitate communication and collaboration among people who are not in the exact physical location. By using VR, people can make eye contact and interact with virtual objects as if they were in the same room. This can significantly improve the effectiveness of group communication and benefit businesses with remote or international teams or people who cannot travel [16].

Sixth-generation networks are expected to significantly improve the VR/AR experience by providing faster speeds, lower latency, and more capacity than current 5G networks. This can lead to more realistic and immersive experiences that can be used in various fields, such as 360⁰AR video streaming gaming, entertainment, education, and training, as well as in industries such as healthcare, manufacturing, and retail. Additionally, 6G networks can support more users in accessing VR/AR experiences at the same time, particularly in large-scale events [17]. Furthermore, 6G networks are expected to have advanced AI and machine learning (ML) capabilities, which can optimize the VR/AR experience for different users and devices. Overall, the 6G network has the potential to revolutionize the way we interact with technology and with each other by providing VR/AR experiences that open new possibilities for various industries [16,18].

XR, with its advanced high-definition imaging and 4K/8K resolution, can be utilized in a variety of innovative applications such as entertainment, education, virtual meetings, and work communication [19]. AR applications are known for their uRLLC, as they demand a delay of 5 ms [20]. An approach that can be taken to attain the necessary delay time for AR applications is to implement MEC at the edge of the RAN. In [20], the authors developed a system that utilizes three main levels of diverse edge servers, and as a result, three levels of offloading are achieved, leading to higher latency efficiency. This can meet the E2E latency within the specified requirements for AR applications.

2.

Holographic telepresence (Teleportation)

Holographic telepresence allows people to feel like they are in the same room as others, even if they are physically far away, by creating a 3D holographic representation of them. The delay with this technology will need to be extremely low, in the range of less than a millisecond. It requires the capability to display thousands of synchronized view angles, as opposed to the limited number required for VR and AR [21]. Furthermore, to achieve a fully immersive remote experience, digitizing and transmitting all five human senses through future networks will be necessary, increasing the amount of data that needs to be transferred. The 3D holographic display technology will require an extremely high data transfer rate, around 4.32 Tbps [21]. This presents significant challenges for the current 5G network in terms of its ability to handle the high data transfer rate needed for technology.

3.

Smart healthcare (Healthcare 5.0)

The healthcare industry has undergone significant changes as a result of the advancements in 5G technology and is expected to continue to evolve with the introduction of 6G. The strain on healthcare systems is caused by an increase in the aging population and the challenges posed by the lack of real-time tactile feedback and high costs [22]. In addition, the healthcare industry is working to develop remote surgeries, and accessing remote healthcare necessitates the implementation of strict QoS standards. These standards include maintaining exceptionally and extremely low latency, providing extensive network coverage, ensuring ultra-high data rates, and delivering ultra-high reliability.

As a result, industry and academia are actively researching communication and networking solutions that facilitate remote healthcare access. The features of 6G architecture are expected to be better than 5G, which will facilitate the implementation of brain–computer interface (BCI) systems, which require ultra-reliable communication with extremely minimal lag, measured in microseconds. Healthcare 5.0 is expected to deploy sensor-based technology that utilizes neurological techniques to transmit human senses in real-time. Such kinds of Healthcare 5.0 applications require euRLLC that is expected to be provided by 6G networks [23].

4.

Industrial automation (Industry 5.0)

The manufacturing industry incorporates new technologies into product design and development, including IoT, AI, big data, digital twins, and blockchain. Sixth-generation will bring the Industry 5.0 revolution to fruition through the digital transformation of manufacturing processes using cyber–physical systems and IIoT services. The removal of the boundaries between the physical factory and the virtual computational space will allow for more cost-effective, flexible, and efficient internet-based diagnostics, maintenance, and machine-to-machine communications [24]. Automation brings its own set of requirements in terms of reliable and synchronized communication, which 6G is well equipped to handle through its cutting-edge technologies. For instance, in industrial control, real-time operations with a guaranteed delay jitter in the microseconds range and peak data rates in the Gbps range will be required for AR/VR applications such as training and inspections [25].

Table 2. Specifications of the main use cases of 6G.

Use Cases	Applications/Scenario	Key Requirements	Enabling 6G Technologies
Ubiquitous mobile ultra-broadband (uMUB) [26,27]	Holographic communication	Extremely high data rates, ultra-low latency, high synchronization	THz, VLC, supermassive MIMO, AI, edge intelligence, and digital twins
	Ultra-realistic XR, 16K streaming	Extreme data rates (Tbps), ultra-low latency (<1 ms), high reliability, and global coverage	THz, VLC, supermassive MIMO, AI, edge intelligence, and QC
	Enhanced mobile Internet	High data rates, improved coverage, network resilience	Supermassive MIMO, IRS, edge intelligence, zero-touch network, and AI
	High-quality immersive AR/VR gaming and streaming experiences	Extreme data rates (Tbps), very low latency, high reliability, and global coverage	THz, VLC, supermassive MIMO, AI, edge intelligence, and QC
	Autonomous vehicular systems	Ultra-low latency (<1 ms), high reliability, and high-speed data transfer	THz, VLC, edge intelligence, supermassive MIMO, AI, and blockchain
	Smart cities and IoT ecosystems	Massive device connectivity, low power consumption, and high reliability	IoE, blockchain, IRS, and zero-energy interface
Ultra-high-speed with low-latency communications (uHSLLC) [28,29]	Industrial automation (Industry 5.0)	Ultra-low latency (<1 ms), extremely high reliability (99.9999%), and high security	AI, edge intelligence, time-sensitive networking (TSN), zero-touch networks, and digital twins
	Autonomous driving	Ultra-low latency, high reliability, and high positioning accuracy.	THz, VLC, edge intelligence, supermassive MIMO, AI, and blockchain
	Remote surgery and healthcare	Ultra-low latency, extremely high reliability, high bandwidth for video and sensor data	THz, VLC, edge intelligence, supermassive MIMO, and robot avatar
	Immersive gaming and virtual reality	Low latency, high data rates, and immersive sensory integration	THz, VLC, edge intelligence, AI, supermassive MIMO, new coding techniques, and zero-touch network
	Tactile Internet	Extremely low latency, ultra-reliability, and high security	AI, new coding techniques, and edge intelligence
	Drone swarms	Ultra-reliable navigation, collision avoidance, real-time rerouting	AI, IRS, ISAC, edge intelligence, robot avatar, QC, new multiple access techniques, and zero-touch network
Ultra-high data density (uHDD) [15,28,30]	Precision agriculture	Massive connectivity, long battery life for devices, and reliable data transmission	Supermassive MIMO, NOMA, edge intelligence, zero-energy interface, and blockchain
	Environmental monitoring	Massive connectivity, remote sensing capabilities, and efficient data collection and analysis	Supermassive MIMO, NOMA, edge intelligence, zero-energy interface, blockchain, ISAC, digital twin, and AI
	Smart manufacturing	High reliability, low latency, secure communication, real-time data acquisition and processing	Digital twin, blockchain, AI, IEC, and ISAC
Human-centric services (HCSs) [31,32]	Brain–computer interfaces (BCIs) and neurotechnology	Ultra-low latency, high data precision, security, and biocompatibility	AI, QC, edge intelligence, and ISAC
	Haptics interfaces	Low latency, precise feedback mechanisms, and real-time adaptation.	Edge intelligence, AI, new coding techniques, QC, IEC, and ISAC
	Augmented human capabilities	Ultra-low latency, biocompatible devices, and secure data interfaces	Robot avatar, AI, zero-energy interface, ISAC, digital twin, and blockchain
	Ethical AI governance	Secure data sharing, explainable AI models, and regulatory compliance	Blockchain, AI, and QC
	Empathic/ affective communication	Real-time data analysis, privacy, and low latency	AI, edge intelligence, and new coding techniques

Table 2. Specifications of the main use cases of 6G.

Use Cases	Applications/Scenario	Key Requirements	Enabling 6G Technologies
Ubiquitous mobile ultra-broadband (uMUB) [26,27]	Holographic communication	Extremely high data rates, ultra-low latency, high synchronization	THz, VLC, supermassive MIMO, AI, edge intelligence, and digital twins
	Ultra-realistic XR, 16K streaming	Extreme data rates (Tbps), ultra-low latency (<1 ms), high reliability, and global coverage	THz, VLC, supermassive MIMO, AI, edge intelligence, and QC
	Enhanced mobile Internet	High data rates, improved coverage, network resilience	Supermassive MIMO, IRS, edge intelligence, zero-touch network, and AI
	High-quality immersive AR/VR gaming and streaming experiences	Extreme data rates (Tbps), very low latency, high reliability, and global coverage	THz, VLC, supermassive MIMO, AI, edge intelligence, and QC
	Autonomous vehicular systems	Ultra-low latency (<1 ms), high reliability, and high-speed data transfer	THz, VLC, edge intelligence, supermassive MIMO, AI, and blockchain
	Smart cities and IoT ecosystems	Massive device connectivity, low power consumption, and high reliability	IoE, blockchain, IRS, and zero-energy interface
Ultra-high-speed with low-latency communications (uHSLLC) [28,29]	Industrial automation (Industry 5.0)	Ultra-low latency (<1 ms), extremely high reliability (99.9999%), and high security	AI, edge intelligence, time-sensitive networking (TSN), zero-touch networks, and digital twins
	Autonomous driving	Ultra-low latency, high reliability, and high positioning accuracy.	THz, VLC, edge intelligence, supermassive MIMO, AI, and blockchain
	Remote surgery and healthcare	Ultra-low latency, extremely high reliability, high bandwidth for video and sensor data	THz, VLC, edge intelligence, supermassive MIMO, and robot avatar
	Immersive gaming and virtual reality	Low latency, high data rates, and immersive sensory integration	THz, VLC, edge intelligence, AI, supermassive MIMO, new coding techniques, and zero-touch network
	Tactile Internet	Extremely low latency, ultra-reliability, and high security	AI, new coding techniques, and edge intelligence
	Drone swarms	Ultra-reliable navigation, collision avoidance, real-time rerouting	AI, IRS, ISAC, edge intelligence, robot avatar, QC, new multiple access techniques, and zero-touch network
Ultra-high data density (uHDD) [15,28,30]	Precision agriculture	Massive connectivity, long battery life for devices, and reliable data transmission	Supermassive MIMO, NOMA, edge intelligence, zero-energy interface, and blockchain
	Environmental monitoring	Massive connectivity, remote sensing capabilities, and efficient data collection and analysis	Supermassive MIMO, NOMA, edge intelligence, zero-energy interface, blockchain, ISAC, digital twin, and AI
	Smart manufacturing	High reliability, low latency, secure communication, real-time data acquisition and processing	Digital twin, blockchain, AI, IEC, and ISAC
Human-centric services (HCSs) [31,32]	Brain–computer interfaces (BCIs) and neurotechnology	Ultra-low latency, high data precision, security, and biocompatibility	AI, QC, edge intelligence, and ISAC
	Haptics interfaces	Low latency, precise feedback mechanisms, and real-time adaptation.	Edge intelligence, AI, new coding techniques, QC, IEC, and ISAC
	Augmented human capabilities	Ultra-low latency, biocompatible devices, and secure data interfaces	Robot avatar, AI, zero-energy interface, ISAC, digital twin, and blockchain
	Ethical AI governance	Secure data sharing, explainable AI models, and regulatory compliance	Blockchain, AI, and QC
	Empathic/ affective communication	Real-time data analysis, privacy, and low latency	AI, edge intelligence, and new coding techniques

3. Specifications and Main Requirements of 6G Networks

Sixth-generation is expected to provide ultimate performance improvements to enable novel services and applications. This section provides the characteristics and major technical requirements that define the future of 6G networks. The main requirements of the 6G networks include the following.

1. 

Massive connectivity

The continuous growth in the number of connected users is a significant driving force behind the advancement of wireless technology. The KPI used to assess the usage scenario of mMTC is connection density. In 5G, the minimum number of devices with a lower QoS per square kilometer is ${10}^6$; however, this number is expected to increase tenfold to ${10}^7$ devices per square kilometer in the future [33]. In anticipation of the significant increase in connection density, 6G networks are expected to introduce new technologies, including THz communication, VLC, and blockchain-based spectrum sharing, to meet the demand.

2. 

Latency

The emergence of 6G technology is poised to redefine wireless communication, particularly in relation to latency. Latency, the delay experienced during data transmission, plays a crucial role in the performance of time-sensitive applications. In 6G networks, the aim is to drastically reduce latency to an unprecedented level, reaching mere microseconds.

3. 

Reliability

The upcoming 6G wireless technology is expected to improve network reliability significantly, targeting near-perfect reliability rates of approximately 99.99999%. Sixth-generation networks are anticipated to be designed with advanced security features that will further enhance their overall reliability [1].

4. 

Ubiquitous connectivity

Mobile data usage has been increasing quickly in recent years, and this trend is expected to persist in the future. Based on research findings, it is predicted that the global mobile data traffic will escalate to 282 EB each month by 2027 [34]. The limitations that have been identified with 5G wireless networks include high interference resulting from extensive interconnections, inadequate computing capacity, and a lack of ubiquitous connectivity [35]. As a result of these limitations, there is a growing need for 6G communication, which is expected to offer superior capabilities and features compared to 5G. The upcoming 6G networks are expected to facilitate the connection of various devices, including personal devices, sensors, vehicles, and others [1,35]. It will offer uninterrupted and extensive connectivity across various environments while meeting indoor and outdoor service standards. This will be achieved through a reliable and cost-efficient infrastructure.

5. 

Security

The security of 6G networks is a top priority due to potential challenges. These networks will support diverse applications requiring stricter security measures, including AI and big data technologies. Key security aspects include privacy protection, defense against external and internal threats, and prevention of flooding attacks [1]. Additionally, incorporating localization and sensing, new network architectures, and meeting low latency and high-reliability requirements will necessitate innovative security and privacy approaches.

6. 

Unmanned mobility

Using UAVs as flying BSs has the potential to greatly increase the capacity and coverage of current wireless networks, which is why researchers are interested in exploring this approach for developing 6G networks. Compared to traditional fixed BSs, UAVs have the advantage of being highly agile and mobile, allowing them to be rapidly deployed to support existing cellular networks. This improves network connectivity for distant terminals. Additionally, UAVs have a wide range of other potential applications, such as fire detection, disaster response, and surveillance. As a result of these features, UAVs have garnered significant attention as a defining feature of 6G networks [36].

6G networks are anticipated to exceed the capabilities of 5G and 4G by overcoming their constraints and facilitating transformational applications via improved KPIs. The principal KPIs of 6G encompass ultra-high data speeds (up to 1 Tbps), sub-millisecond latency, exceptional energy efficiency, pervasive coverage, and inherent integration of AI. Moreover, 6G aims to enhance spectrum efficiency, facilitate an unparalleled density of device connections per square kilometer, and accommodate new technologies, including holographic communications, digital twins, and intelligent autonomous systems [1,8]. In contrast to 4G and 5G, 6G networks are designed to revolutionize communication capabilities by achieving a substantially higher throughput and lower latency. Mobile broadband was primarily introduced by 4G, with speeds of up to 1 Gbps and a latency of approximately 50 milliseconds. However, 5G has exceeded these limits, with 10–20 Gbps speeds and latencies of less than 10 milliseconds. Nevertheless, 6G establishes even more ambitious objectives, indicating that it will achieve speeds exceeding 1 Tbps and latencies as low as 100 microseconds [2,4]. These developments will facilitate the development of real-time immersive applications, including Tactile Internet and extended reality.

Additionally, 6G prioritizes sustainable and intelligent networking, which addresses obstacles such as environmental impact and energy consumption. Energy efficiency and AI-driven network management are the fundamental objectives of 6G, in contrast to 4G and 5G, which prioritize quicker connectivity and capacity. Furthermore, 6G sets itself apart from the energy-intensive operations of 5G and 4G by introducing the concept of “zero energy communication” to fuel devices through ambient energy harvesting [37]. This paradigm shift guarantees that 6G is not only more efficient and quicker but also environmentally sustainable.

Table 3. Comparative study of 4G, 5G, and 6G KPIs [1,8,26,29].

Key Parameter	4G	5G	6G
Carrier Bandwidth	20 MHz	400 MHz	up to 100 GHz
Peak Data Rate	1 Gbps	10–20 Gbps	1 Tbps
Latency	100 ms	5–10 ms	10–20 us
Mobility	350 km/h	500 km/h	1000 km/h
Reliability	99.99%	99.999%	99.99999%
Connectivity Density	N/A	${10}^6$ devices/km2	${10}^7$ devices/km2
Security	Medium	Medium	Very High
User Experience Rate	10 Mbps	100 Mbps	10 Gbps

provides a comparative analysis of KPIs of 4G, 5G, and 6G systems. Also,

Table 4. Key requirements of novel 6G applications [3,14,15,17].

Applications	Support 4G/5G	Reliability	Latency	Data Rate
Holographic communication	Not supported	Ultra-high	Extremely low	Extremely high
Ultra-realistic XR, 16K streaming	Limited support in 5G	Ultra-high	Extremely low	Extremely high
Enhanced mobile Internet	Partial support in 5G	High	Low	High
Immersive AR/VR gaming and streaming experiences	Partial support in 5G	Ultra-high	Extremely low	Extremely high
Autonomous vehicular systems	Partial support in 5G	Ultra-high	Extremely low	High
Smart cities and IoT ecosystems	Support in 4G/5G	High	Low	High
Industrial automation (Industry 5.0)	Partial support in 5G	Ultra-high	Extremely low	High
Autonomous driving	Experimental in 5G	Ultra-high	Extremely low	High
Remote surgery/Telesurgery	Not supported	Ultra-high	Extremely low	Extremely high
Tactile Internet	Not supported	Ultra-High	Extremely low	High
Drone swarms	Partial support in 5G	High	Low	High
Precision agriculture	Supported in 4G/5G	High	Low	High
Environmental monitoring	Supported in 4G/5G	High	Low	High
Smart manufacturing	Partial support	Ultra-high	Extremely low	High
Brain–computer interfaces (BCIs) and neurotechnology	Not supported	Ultra-high	Extremely low	High
Haptics interfaces	Not supported	Ultra-high	Extremely low	High
Augmented human capabilities	Partial support	Ultra-high	Extremely low	High
Ethical AI governance	Emerging in 5G	High	Low	High
Empathic/effective communication	Not supported	High	Extremely low	High

provides the key requirements and specifications of the new applications provided by 6G networks. Furthermore, Figure 1 presents the key challenges with the evolution of 6G. The KPIs of 6G networks can be summarized as follows [1,8,26,29].

• Carrier bandwidth: In 6G networks, there is a need to support frequencies up to 100 GHz in the VLC and THz bands, as well as frequencies up to 10 GHz in the mmWave bands.
• Spectral efficiency: The spectral efficiency of 6G is projected to be five times higher than that of 5G.
• Peak data rate: The required speed of 6G networks is ≥1 Tbps, which is significantly faster than 5G by 100–1000 times.
• Mobility: The 6G networks are anticipated to support high-speed trains and UAVs, ensuring a maximum speed of 1000 kph.
• Energy efficiency: The energy efficiency of 6G should be improved by 10 to 100 times compared to 5G.
• Latency: The 6G networks have stricter requirements for many applications, including AR, VR, XR, and holographic communications. Sixth-generation networks aim to support E2E latencies of 10–20 µs.

4. Key Enabling Technologies of 6G Networks

This section discusses a few technologies that are expected to drive the development of 6G. Figure 2 presents the key enabling technologies of 6G applications.

4.1. Supermassive MIMO

Massive multiple input multiple output (MIMO) is a technology used in wireless communication systems that involves the use of a large number of antennas at the transmitter and receiver. Massive MIMO can provide a high bandwidth and energy-efficient network, improve network reliability and coverage, and reduce interference, enabling more users to be supported in a given area. It is a key solution for B5G and 6G applications to enable better performance of communication [38]. The basic idea of massive MIMO is that it works by serving multiple user terminals at the same time. It is the most common configuration of centralizing massive MIMO (CL-MIMO). Typically, using terahertz (THz) signals is a problem due to the high losses of transmission and reflection, which, if not handled properly, could make the communication go to the dogs, so to speak [39].

However, some serious problems, e.g., channel characterization, must be considered during the actual implementation of the massive MIMO systems. There are a multitude of antennas at the BS, so it is hard to make a model and estimate the channels. For the last twenty years, traditional below-the-carrier channel models have been made with far-field conditions and plane wavefronts in mind. Cell-free massive MIMO, which has been the evolution of massive MIMO, has emerged as one of the most promising technologies for next-generation communications systems beyond 5G. Instead of the regular CL-MIMO, which strongly centers the antennas, the cell-free massive MIMO or rather distributed MIMO or distributed massive MIMO, which fully uses the entire airport area, has antennas installed almost anywhere. This set of processes helps to advance various aspects such as spectral efficiency, energy efficiency, and uniform service quality over the coverage area [40].

The spatial distribution of cell-free MIMO makes the technology interactive and enables efficient resource management, making it a perfect solution in dense user environments of future networks [40]. Therefore, student researchers and industry actors are now more focused on creating and improving this technology. Thus, their vision connects with the central systems built so far and the potential to build better communication networks. Yet another variation in massive MIMO, DM-MIMO distributed type, is even simpler and more direct as it places the antennas across the cell area rather than in one place. This distribution is a development technique whereby signal acquisition is expanded over the space of the cells, as opposed to localizing them in one location. It successively gives the effect of gain of spatial diversity, attenuation of path loss, and decrease in blockage and fading. Hence, DM-MIMO stands as the most suitable substitute for the problematic situation of massive MIMO systems; it provides broader coverage and more powerful and efficient usage of the available spectrum, all necessary for future wireless networks [41].

4.2. Artificial Intelligence

AI will serve as a core driver of 6G technology, enabling unprecedented innovation in network intelligence, automation, and optimization. Using AI, wireless systems are able to show human-like cognition, including learning, reasoning, perception, and decision-making, and then dynamically respond to dynamic environment changes. Self-sufficient decision-making with autonomous learning capability and efficient resource optimization to cater to the demands of various applications will be embodied in AI-enabled 6G networks. Unlike 4G and 5G, where AI plays a limited role, 6G will feature full AI automation, leading to the transformation from conventional radio systems to intelligent, self-optimizing networks [42].

Among AI’s enabling functionalities in 6G networks, real-time network automation and optimization plays a leading role. AI will continue to improve, i.e., assist network functions (e.g., traffic management, handover, and spectrum allocation) for real-time decision-making. Based on the combination of deep learning and reinforcement learning (RL), the self-healing features of 6G networks can be realized, i.e., detecting and repairing network errors automatically and without human participation [43]. Moreover, AI will allow for energy efficiency, minimizing the power use of wireless infrastructures via predictive and intelligent analysis of energy use and dynamic management of resources. Furthermore, AI will release the maximum use of radio waves by providing sophisticated cognitive radio (CR) features (e.g., their acquisition and usage).

CR, assisted by AI, will enable adaptive spectrum allocation, interference cancelation, and intelligent beamforming [44]. ML models will compute large spectral datasets to determine the best frequency bands on the fly, providing continuous high connectivity for ultra-dense networks. AI-based algorithms will improve channel estimation and propagation prediction, thus facilitating better resource optimization and higher data rates. AI will change wireless communication at the physical layer (PHY) by enhancing core algorithms such as symbol detection, precoding, antenna selection, and beamforming [44]. Conventional PHY-based methods have limitations in terms of performance for large-bandwidth and high-mobility situations. BCI-based PHY techniques will employ deep learning models to improve signal flow, the suppression of impact, and the correction of errors, resulting in increased spectral efficiency and more robust communication in 6G networks. The creation of BCIs will be among the most impactful AI applications on 6G. BCI systems based on AI will allow natural interaction between human and digital worlds, which will be exploitable for applications in healthcare and augmented and immersive VR. AI will decode and analyze neural signals in real-time, facilitating bidirectional communications between the human brain and machine, thus disrupting the paradigm of human–machine interaction in 6G [45].

Edge intelligence, where AI-based edge computing is used to facilitate low-latency processing and on-the-fly analytics, will be one of the key innovations of 6G [46]. AI will be implemented in edge nodes for decentralized data processing and decreasing reliance on central cloud computing. Federated learning (FL) will transform privacy-preserving AI training on a distributed edge device environment by providing a secure means of data transfer while reducing communication bandwidth. AI-as-a-service (AIaaS) models will also extend AI power by providing intelligent applications for end users in a direct mobile network connection [47]. AI will lead the evolution from smart devices to truly intelligent devices in 6G networks. AI-enabled IoT devices can self-optimize, make autonomous decisions, and share experiences with other devices. AI will improve situation-aware real-time device-to-device communication, predictive maintenance, and intelligent resource allocation, bringing smart cities, healthcare systems, and industrial automation to a new generation of technologies.

Security and privacy are important issues in 6G networks because of the massive data exchange and application complexity. It is proposed that AI will be coupled with a quantum computer to create quantum ML (QML) methods, which can lead to improved encryption and cybersecurity. A quantum key distribution (QKD) security key with encryption at very high levels across access channels shall be used to access a channel. It will provide ultra-secure and tamper-proof key encryption for 6G networks. Using AI and quantum cryptography, 6G will guarantee a strong defense against cyberattacks and data theft [48].

4.3. Terahertz and Visible Light Band

The THz band employs wavelengths ranging from 0.1 THz to 10 THz, and the visible light band (VLB) comprises a wavelength range of 400 THz to 800 THz. The THz band has some key advantages over the mmWave band because it belongs to a high continuous bandwidth and dramatically higher transmission rate range. Due to their shorter wavelength for THz signals, ultra-massive MIMO (UM-MIMO) antenna arrays can be integrated, greatly enhancing spectral efficiency. In addition, the miniature size of the THz transceivers makes the THz transceivers well suited for small, compact, and efficient hardware designs [11]. However, these characteristics position THz communication as an enabling technology to support ultra-high-through-throughput wireless communication, low-latency applications, and mMTC in 6G networks.

However, despite these advantages, THz communication faces critical challenges. These signals are easily distorted by atmospheric absorption, scattering, and blockage by physical barriers, including buildings and the human body. Additionally, molecular absorption becomes a major issue over long distances, particularly in adverse weather conditions. To overcome these constraints, state-of-the-art signal processing methods, RIS, and hybrid networking schemes that use THz in combination with other bands, such as mm-Wave and sub-THz, are under investigation [13].

VLC offers a promising alternative for high-speed indoor data transmission, which exploits the ubiquitous installation of LED lighting units. Data are transmitted efficiently based on the rapid modulation of light intensity [49]. With this method, an efficient and economical way to traditional RF communication could be achieved without the burden of electromagnetic interference (EMI) and security issues. One of the most significant achievements of VLC standardization has been the IEEE 802.15.7 standard, which defines optical wireless communications protocols. Nevertheless, while VLC has its merits, it has not yet been completely standardized into cellular network standards by organizations like 3GPP. There is a significant challenge to the non-coherent nature of VLC, i.e., the transmitter and receiver do not use channel state information (CSI) [50]. Therefore, VLC has increased path loss scaled to the fourth power of distance and thus is highly susceptible to the ambient environment.

In addition, VLC needs a clear line of sight (LOS) and illumination, which constrains its application in a dark environment. It is also vulnerable to interferences by ambient light sources, e.g., sunlight and fluorescent lamps, which cause shot noise and deteriorate the signal quality [49,50]. To overcome these limitations, cutting-edge modulation schemes, adaptive beamforming, and hybrid RF-VLC systems are being developed to increase performance and guarantee continuous connectivity. The combination of THz and VLC in 6G networks will provide a new avenue for applying ultra-fast and low-latency communication. Some of the key applications include the following [50,51,52]:

-

High-speed indoor wireless networks: VLC offers secure, electromagnetically interference-free communication in dwellings, offices, and smart buildings, which can be combined with conventional Wi-Fi and cellular network technologies.

-

Terahertz-based backhaul for ultra-dense networks: THz bands can be exploited as high-throughput wireless backhaul links that can relieve reliance on fiber-optic cabling.

-

Vehicle-to-everything (V2X) communication: VLC and THz can be utilized in intelligent transportation systems (ITSs) for high-speed, secure application of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications.

-

Underwater and space communication: VLC is suitable for indoor and underwater wireless optical communications (UWOC), while THz can be utilized for deep-space and inter-satellite communications at high speeds for data transfer.

-

Secure and private communication: The limited coverage of VLC makes it an ideal choice for applications requiring enhanced security, such as financial institutions and military installations.

4.4. Internet of Everything (IoE)

In the era of 6G technology, there will be a shift in data consumption models and the way businesses operate. The IoE will be a vital component, connecting people, processes, data, and things. This interconnectedness will revolutionize industries and create new opportunities for innovative network services. The IoE relies on gathering information from various sources and emphasizes the involvement of people as connected nodes. Effective processes within the IoE facilitate interactions and deliver value by providing precise information at the right time and in the right manner. The IoE will lead to a connected world where everyone is meaningfully interconnected, transforming the way we live and work [53].

The IoE is the future generation of digital connection, connecting the people, the processes, the data, and the things to a smart and connected ecosystem. Unlike the IoT, which primarily focuses on linking physical devices to the Internet, IoE extends beyond device connectivity to enable real-time data-driven decision-making, automation, and intelligent interaction across diverse domains. IoE is at the state of the art, performing computing at scale, distributed intelligence, and uRLLC for fluid interaction between networked systems. With the coming of 6G networks, IoE is likely to become mature enough to deliver the expected ultra-long-range, intelligent, and ubiquitous connectivity [52]. The key capabilities of 6G, such as AI-native networking, THz spectrum utilization, edge computing, digital twins, and intelligent automation, will be the foundation for large-scale IoE deployments [54]. The flexible and mixed design of 6G will allow continuous processing of large volumes of data streams, which in turn could be used to enable highly efficient, precise, and adaptive operation of an autonomous system. The key advancements of IoE in 6G networks include the following [52,53,54,55]:

• Hyperconnectivity and intelligent networking

  -

  Sixth-generation will allow ultra-high-density device coupling to provide trillions of IoE nodes with millisecond latencies and practically instantaneous data rates.

  -

  AI-enabled network orchestration will provide traffic auto-optimization, improved spectral efficiency, and congestion prediction, thereby supporting seamless device-to-device (D2D) communication.

• Real-time data processing and decision-making

  -

  Using the combination of AI, FL, and edge intelligence, IoE will be able to process and analyze real-time data with huge volumes at the edge, thus minimizing the requirement of cloud processing. This will allow autonomous, adaptive decision-making in powerful applications such as smart cities, industrial automation, precision healthcare, and autonomous transport.

• Heterogeneous network integration and multi-access connectivity

  -

  IoE in 6G will be implemented across a wide range of communication systems, such as Terahertz communication, LiFi, satellite connectivity, and blockchain-based securing technologies for IoT. This multi-access architecture will improve network robustness and coverage, enabling continuous service provision in remote, urban, and industrial areas.

• Enhanced security and trust mechanisms

  -

  The highly distributed nature of IoE requires robust security mechanisms, which 6G will address through quantum-safe encryption, AI-driven anomaly detection, and blockchain-enabled trust models.

  -

  Self-healing networks automatically identify and suppress cyber-attacks, guaranteeing privacy, integrity, and resilience in critical applications.

• Applications across multiple domains

  -

  Smart cities and infrastructure: Autonomous traffic management, AI-powered utilities, and smart grid optimization.

  -

  Healthcare and remote patient monitoring: Wearable health sensors, AI-based diagnosis, and real-time emergency response systems.

  -

  Industrial IoE (IIoE): This includes predictive maintenance, robotics automation, and real-time process control in manufacturing.

  -

  Autonomous transportation and smart mobility: The 6G-based V2X communication enables the development of self-driving cars and intelligent traffic management systems.

  -

  Sustainable development: IoE-based energy-efficient systems, climate monitoring grids, and sustainable agriculture solutions for food security.

4.5. Blockchain

Blockchain technology is emerging as a foundational pillar of security, transparency, and trust in next-generation networks, including 6G. It offers a decentralized, immutable, and tamper-proof ledger that guarantees safe data transfer and does not require centralized intermediaries. Unlike traditional centralized architectures, vulnerable to single points of failure, cyberattacks, and privacy breaches, blockchain leverages distributed consensus mechanisms to authenticate transactions and maintain network integrity. Blockchain networks can be grouped into public, private, and consortium networks, which have different functions in 6G-based applications [56]. The main categories of blockchain that can assist the evolution of 6G applications include the following [56,57]:

(1)

Public blockchains (e.g., Bitcoin, Ethereum)

• Open to anyone and operate on decentralized proof-of-work (PoW) or proof-of-stake (PoS) consensus mechanisms.
• Well suited to handling ordinary transparent and secure IoT payments, decentralized identity management, and cryptographically mediated micropayments.

(2)

Private blockchains (permissioned networks)

• Works in an isolated ecosystem, with access limited to logged-in users.
• It is well suited for enterprise scenarios, such as fully secure MEC, FL-based authentication, and slicing for 6G services.

(3)

Consortium blockchains (hybrid model)

• Controlled by a band of trustworthy individuals, with a tradeoff between security, privacy, and efficiency.
• Can be implemented in collaborative 6G ecosystems shared by several stakeholders (e.g., telecom providers, cloud providers, and IoT vendors).

Blockchain can improve the security, efficiency, and decentralization of 6G-based networks via various disruptive applications.

Table 5. Potential applications of blockchain technology in 6G networks [56,57,58,59].

Main Application	Potential Benefits	Discussion
Decentralized security and privacy protection	Trustless authentication	Blockchain obviates the need for authentication service (vendor) with zero-trust architectures, further opening the novelty of security flaws.
	Data integrity and privacy	Smart contracts can ensure end-to-end encryption, identity authentication, and secure key exchanges in 6G IoT environments.
	Resistant to cyber threats	In uRLLC services, blockchain prevents plain-text data corruption, Sybil attacks, and unauthorized access.
FL and secure AI training	Distributed AI models	Blockchain improves FL by allowing AI model updates on the device to be trusted in the black box without disclosing raw data.
	Reputation management	Participants in FL can be scored based on the quality of their contributions, guaranteeing fairness in collaborative AI training.
Blockchain-based radio access networks (B-RANs)	Decentralized resource allocation	Blockchain enables spectrum management, network slicing, and access control in 6G wireless networks.
	Service-level agreements (SLAs)	Smart contracts can dynamically set SLAs between network providers and users, which can increase service reliability.
Enhancing MEC and IoT security	Trustworthy MEC transactions	Blockchain provides secure and transparent communication among edge nodes in distributed computing systems.
	IoT authentication and access control	Blockchain-enabled identity verification against spoofing, illegitimate device accesses, and data leaks are avoided.
Blockchain in emerging 6G services	Secure autonomous vehicle communication	Blockchain adds to V2X networks by providing communication that is its type of “tamper-proof” as well as safe vehicular navigation.
	Decentralized energy trading	Blockchain can benefit smart grids by facilitating peer-to-peer energy trading and promoting sustainable energy.
	UAV and drone security	Secure, blockchain-based communication protocols protect against hijacking, GPS spoofing, and unauthorized drone access.

provides the potential applications of blockchain technology in 6G. Although 6G has huge potential, blockchain has several technological constraints that need to be overcome for a straightforward integration with 6G. Contemporary blockchain architecture offers inadequate support for the inherently low latency, large-scale connectivity of 6G. Solutions include sharding, layer-2 scaling (e.g., lightning network), and directed acyclic graphs (DAGs) being investigated. Traditional PoW-based blockchains are computationally intensive. The transition to lightweight consensus mechanisms (PoS, delegated PoS, and proof-of-authority) is inevitable for resource-limited IoT devices [58]. For 6G to operate over a range of heterogeneous infrastructures, cross-chain communication protocols must support reliable blockchain communication between different networks.

4.6. Intelligent Reflecting Surfaces (IRSs)

IRSs are flat surfaces consisting of passive reflecting elements that can independently adjust the phase shift in incoming signals. There are two types of IRS structures: antenna-array-based and metasurface-based. By controlling the phase shifts in the reflecting elements, an IRS can redirect reflected signals in desired directions. The use of metamaterials allows for real-time reconfiguration of the reflection coefficient to adapt to changing wireless propagation environments [60]. An IRS has the potential to transform wireless networks with several benefits. These include enhanced signal strength through strategic signal reflection, improving coverage, and reliability. The IRS also optimizes the capacity of wireless networks by minimizing interference and efficiently utilizing the available spectrum. They contribute to reduced latency and improved reliability by carefully manipulating signal reflections to minimize signal loss. Additionally, IRSs enhance the security of wireless networks by making it challenging for potential attackers to intercept and decipher signals, thereby improving overall network security [61]. Section 7 provides a detailed discussion of the IRS and the key deployment for 6G networks.

4.7. Intelligent Edge Computing

Due to the exponentially growing number of interconnected devices, uRLLC demands, and the emergence of AI-enabled applications of 6G networks, distributed intelligent computing paradigms are needed. Edge computing and edge intelligence (EI) represent fundamental technologies that can facilitate run-time, improved security, and optimized resource use by bringing computation to the edge of users and IoT devices. These technologies decrease dependence on centralized cloud environments and enhance next-generation networks’ latency, bandwidth efficiency, and reliability [46]. Edge computing is a distributed computing model that places computational power at the network’s edge to reduce data latency and the burden on centralized cloud servers. This low-latency computing paradigm is fundamental to applications including self-driving cars, smart cities, IIoT, and real-time deep learning inference in 6G networks. Deploying edge computing and EI for 6G networks achieves the following benefits [62,63].

(1)

Works on edge processing, providing real-time responses for latency-constrained scenarios.

(2)

Decreases the requirement to transmit large data bandwidth to the cloud by performing processing locally.

(3)

Offloads computation to several edge nodes so that there are no single points of failure.

(4)

Edge devices analyze local environmental data to make intelligent, contextualized decisions.

(5)

Sensitive data are processed at the source, minimizing exposure to cybersecurity risks.

The 6G edge computing ecosystem comprises multi-tiered architectures that offload processing tasks to devices, edge nodes, and cloud servers. EI is a next-generation evolution of edge computing that deconstructs AI and ML directly into the network edge [62]. This methodology allows smart decision-making at the edge devices without centralized cloud processing. EI is essential in real-time analytics, dynamic resource assignment, FL, and self-adaptive networks in 6G.

Table 6. Key features of EI for 6G networks [46,62,63].

Feature	Description
AI-driven processing	Deploys AI/ML algorithms at the edge for real-time data analysis and decision-making.
FL	Enables distributed AI model training without transmitting raw data to centralized cloud servers.
Adaptive resource allocation	Dynamically optimizes computational and networking resources based on demand.
Energy efficiency	Reduces energy consumption by avoiding continuous cloud communication.
Self-learning and automation	Enables self-optimizing and autonomous networks with minimal human intervention.

provides the key features of the EI. Integrating edge computing and edge intelligence in 6G networks will transform a wide range of fields by allowing low-latency intelligence applications for industries through AI.

Table 7. Key applications of EI in 6G networks [62,63].

Application	Role of EI
Autonomous vehicles	Enables real-time processing for object detection, navigation, and collision avoidance.
Smart cities	Supports intelligent traffic management, surveillance, and environmental monitoring.
IIoT	Facilitates predictive maintenance, automation, and process optimization in factories.
Healthcare and remote surgery	Ensures low-latency AI-powered diagnostics and robotic-assisted surgeries.
AR and VR	It provides a seamless, low-latency gaming experience and immersive applications.
Smart grid and energy optimization	AI-driven demand forecasting and real-time energy distribution.

provides the key applications of EI in 6G networks. Furthermore,

Table 8. Challenges and potential solutions of deploying EI for 6G networks.

Challenge	Potential Solution
Scalability issues	Hierarchical edge computing with dynamic resource allocation.
Security and privacy	Blockchain-based authentication and FL for secure AI.
Interoperability issues	Development of standardized APIs and protocols for seamless edge–cloud interaction.
Energy consumption	Use lightweight AI models and energy-efficient hardware accelerators (e.g., TinyML).
Data synchronization and consistency	AI-driven edge orchestration for real-time data consistency across nodes.

provides the challenges and potential solutions for deploying EI for 6G networks.

4.8. Digital Twin

A digital twin (DT) is a high-level concept referring to a virtual model of a real-world object that is updated in real-time using data exchange. Such technology allows dynamic monitoring, simulation, prediction, and optimization of physical assets, processes, and/or systems. As 6G technology promises ultra-low latency, high bandwidth, and omnipresent intelligence, DT will play a significant role in a wide range of applications, from smart cities to industrial automation and healthcare [64]. A digital twin consists of three main elements: a physical entity, virtual twin, and data connectivity. The physical entity is the real-world object or system being monitored; however, the virtual twin is the digital replica that mirrors the physical entity in real time. The data connectivity component is the seamless link that enables bidirectional data exchange for analysis and optimization [65].

DT technology will provide a groundbreaking performance for 6G networks, allowing intelligence in the observation, predictive analytics, and real-time optimization for a wide array of industries. Combining the integration of AI, blockchain, edge computing, and extremely high-speed 6G connectivity, digital twins will move from static representations toward learning-by-doing, self-improving intelligent avatars of the real-world processes their physical counterparts represent. Scalability, interoperability, and security issues will be deciding factors in realizing the full potential of this revolutionary technology during the 6G period [66].

Table 9. Potential applications and use cases of DT in 6G networks [66,67].

Application Domain	Use Cases	Benefits
Smart manufacturing (Industry 5.0)	Predictive maintenance, real-time process optimization, automation of supply chains	Reduced downtime, increased efficiency, lower costs
Healthcare and precision medicine	Personalized treatment models, virtual testing of drugs, digital avatars for patients	Improved diagnostics, better patient care, reduced trial costs
Smart cities	Traffic flow optimization, energy management, environmental monitoring	Improved urban planning, efficient resource utilization
Autonomous vehicles and intelligent transportation	V2X communication, digital twins for roads, simulation-based AI training	Safer, more efficient transportation systems
Core networks	Network optimization, spectrum allocation, real-time troubleshooting	Enhanced performance, minimal service disruption
Aerospace and defense	Flight simulations, predictive maintenance, performance analysis	Increased operational reliability, cost savings
Energy and utilities	Smart grid management, renewable energy integration, real-time power distribution	Enhanced efficiency, better sustainability

provides the potential use cases and applications of DT in 6G networks. However, deploying DT for 6G applications faces many challenges that should be addressed.

Table 10. Challenges and potential solutions of deploying DT for 6G networks [64,65,66,67,68].

Challenge	Description	Potential Solution
Scalability	Managing billions of IoT devices and their digital twins is computationally demanding.	Distributed computing, edge intelligence, and hierarchical DT frameworks.
Security and privacy	Continuous data exchange between physical and virtual twins raises privacy concerns.	Blockchain-based secure data management, zero-trust architectures.
Interoperability	Different industries and applications require unique DT models, leading to compatibility issues.	Development of standardized DT frameworks and open APIs.
AI model reliability	AI-driven DT models must be highly accurate for predictive simulations.	FL and real-time model retraining.
Energy efficiency	DT applications require high computational power, increasing energy consumption.	Green AI techniques, optimized resource allocation.

provides the main challenges and potential solutions for deploying DT for 6G applications. Furthermore, the following research areas should be further investigated for the complete utilization of digital twin technologies in 6G networks [64,67]:

• Creation of AI-based self-evolving digital twins for real-time adaptive modeling.
• Discussion of the potential of neuromorphic computing for optimizing energy performance.
• Standardization frameworks to enable seamless DT integration across industries.
• Quantum cryptography to improve data security in DT applications.

4.9. Robot Avatar

A robot avatar is a physical or virtual representation of a person or entity that can be remotely controlled or programmed to mimic human actions, speech, and gestures. These avatars are based on advances in robotics, AI, haptics, and immersive technologies, e.g., extended reality (XR), to ensure seamless human–machine interaction. In the environment of 6G networks, robot avatars would make real-time telepresence, remote teamwork, and intelligent automation possible across many fields, including medicine, learning, space exploration, industrial work, and entertainment [69]. The key features of robot avatars in 6G networks include the following [69,70].

• Enables immersive and synchronized communication between human users and robot avatars.
• Allows avatars to learn, predict, and adapt to user preferences using deep learning models.
• Enhances user experience by providing real-time force, motion, and touch feedback.
• Merges physical and virtual worlds for more immersive telepresence.
• Ensures data privacy and secure transactions in avatar interactions.
• Reduces latency in avatar response time by processing data closer to the user.

Robot avatars are extensively applied in many industries, and each can take advantage of the high-standard, high-speed, low-delay, intelligent communication capabilities of 6G networks.

Table 11. Potential applications and use cases of robot avatar in 6G networks [70,71,72].

Domain	Use Case	Benefits
Healthcare and telemedicine	Remote robotic surgery, patient monitoring, AI-assisted therapy	Enables precise, real-time surgeries, improves healthcare access
Education and training	Virtual classrooms, skill-based learning, AI tutors	Enhances remote learning, facilitates hands-on training
Industrial automation	Smart factories, remote equipment handling, predictive maintenance	Increases efficiency, reduces human exposure to hazardous conditions
Space and deep-sea exploration	Robotic space missions, deep-sea research, disaster recovery	Enables human-like exploration in extreme environments
Entertainment and social interaction	AI-driven virtual assistants, gaming, companionship robots	Enhances immersive experiences, provides emotional support
Retail and customer service	AI-driven shopping assistants, virtual sales representatives	Improves customer engagement, offers personalized recommendations

provides the potential use cases and applications of robot avatars in 6G networks. Despite their immense potential, several challenges must be addressed for seamless robot avatar integration in 6G networks.

Table 12. Challenges and potential solutions of deploying robot avatar for 6G networks [69,70].

Challenge	Description	Potential Solution
Human-like realism	Creating avatars that realistically mimic human movements and expressions.	AI-driven motion prediction and real-time facial tracking.
Latency reduction	Ensuring real-time responsiveness in remote control applications.	6G-enabled edge computing and uRLLC.
Security and privacy	Preventing unauthorized access, data breaches, and identity theft.	Blockchain-based authentication and zero-trust architecture.
Interoperability	Ensuring seamless integration with existing digital ecosystems.	Standardization of APIs and communication protocols.
User experience	Enhancing immersion, natural communication, and emotional intelligence.	AI-powered speech recognition and sentiment analysis.

provides the main challenges and potential solutions for deploying robot avatars for 6G applications.

AI is the backbone of intelligent robot avatars. AI enables avatars to understand and respond to human voice commands and gestures and learn from past interactions to personalize experiences. Furthermore, avatars enable navigating environments autonomously with real-time decision-making and detecting and adapting to emotions using sentiment analysis.

Table 13. Capabilities of AI-based robot avatar for 6G networks [73,74].

AI Capability	Function in Robot Avatars	Applications
Natural language processing (NLP)	Enables speech recognition and conversation understanding.	AI tutors, virtual assistants.
Computer vision	Allows avatars to recognize faces, gestures, and objects.	Remote customer service, healthcare assistance.
Reinforcement learning	Helps avatars learn from experiences for better decision-making.	Industrial automation and robotics research.
Predictive analytics	Anticipates user needs and responses.	Personalized AI companions, smart retail.

provides the AI capabilities that assist robot avatars for 6G networks. Blockchain is another critical technology in enhancing security, trust, and transparency in robot avatar applications.

Table 14. Capabilities of blockchain-based robot avatar for 6G networks [75,76].

Blockchain Feature	Impact on Robot Avatars	Example Use Case
Decentralized identity	Prevents impersonation and identity fraud.	Secure login for virtual assistants.
Smart contracts	Automates service agreements.	Robot-as-a-Service (RaaS) deployment.
Data integrity and traceability	Ensures recorded interactions are immutable.	Healthcare avatars managing patient history.
Tokenized economy	Supports microtransactions for digital avatars.	AI-powered digital artists, avatar-based e-commerce.

summarizes the impact of the main features of blockchain on robot avatars.

4.10. Zero-Touch Network

Network automation in 6G networks is the application of AI, ML, and advanced software for the autonomous management and optimization of network services with limited human interaction [77]. With the growing network complexity due to ultra-dense deployments, massive IoT connectivity, and tight QoS requirements, automation is becoming a must for a reliable, whole-life long, cost-effective, and excellent service operation. Network automation in 6G through the utilization of the zero-touch (ZT) network management (ZTM) and the ZT service management (ZSM) framework can deliver self-regulating, self-healing, and self-configurable networks that are able to work in a dynamic and heterogeneous context [78]. The key benefits of network automation for 6G applications include the following [78,79].

• Automates resource allocation, minimizing manual intervention and reducing maintenance expenses.
• Ensures uninterrupted service by dynamically adjusting network parameters in real-time.
• Uses AI-driven analytics to maximize throughput, minimize latency, and balance network loads.
• Enables faster deployment of new services and features through automated orchestration.
• Supports intelligent power management to reduce energy consumption in 6G networks.
• Automates threat detection and mitigation, reducing vulnerabilities.

ZTM is the core concept of 6G network automation, focusing on fully autonomous networks with minimal human intervention. ZTM integrates AI, ML, and intent-based networking (IBN) to enable the following [77,78,79].

• Self-planning: AI-driven forecasting for proactive resource allocation.
• Self-configuration: Automatic deployment and configuration of network functions.
• Self-optimization: Continuous performance tuning based on real-time traffic patterns.
• Self-healing: Automatic fault detection and recovery to prevent service disruptions.

The ZSM framework, developed by the ETSI, is designed to automate all stages of network operations in 6G.

Table 15. Use cases of network automation in 6G networks [77,78,79,80].

Use Case	Function	Expected Impact
Autonomous resource scheduling	AI-driven allocation of spectrum and computing resources.	Improves efficiency and reduces congestion.
Proactive caching	Predictive content caching in edge servers.	It enhances user experience and reduces latency.
Service orchestration and slicing	Automated slicing of resources based on user demands.	Enables flexible and dynamic service provisioning.
Energy-aware RAN optimization	Adaptive power management for base stations.	Minimizes energy consumption and lowers costs.
End-to-end security management	AI-powered anomaly detection and threat response.	Strengthens network security.

provides the ZSM-based network automation use cases in 6G networks. The main objectives of the ZSM framework include the following [78,79,80].

• End-to-end network automation, covering RAN, core, transport, and edge computing.
• AI and data-driven automation, leveraging ML models for predictive decision-making.
• Cross-domain orchestration, automating multi-domain services in hybrid cloud environments.
• Energy-efficient network operations, optimizing power consumption across all network layers.
• Security and compliance, implementing real-time monitoring and threat mitigation.

AI and ML play a critical role in enhancing 6G network automation by enabling real-time data analytics for network performance monitoring and predictive maintenance to prevent failures before they occur.

Table 16. Applications of common AI and ML techniques in automating 6G operations [77,81].

AI/ML Technique	Application for 6G Automation	Impact
Deep RL (DRL)	Adaptive optimization of network functions.	Reduces latency and enhances resource utilization.
FL	Distributed AI model training across multiple network nodes.	Enhances privacy and reduces data transfer overhead.
Neural networks (NNs)	Intelligent fault detection and predictive maintenance.	Improves network reliability.
Supervised learning	Traffic classification and QoS prediction.	Enhances network efficiency.
Unsupervised learning	Anomaly detection in network traffic.	Strengthens cybersecurity.

summarizes the main applications of common AI and ML techniques in automating 6G operations. Summing up,

Table 17. Summary of recent studies on key enabling technologies of 6G.

Ref	Key Enabling Technology	Major Contributions
[82]	Supermassive MIMO	This work introduces a new antenna design with promising features for future 6G devices, with plans to expand its capabilities.
[83]	AI, ultra-massive-MIMO, THz frequency	This study investigates the prospective advancements of 6G wireless and emphasizes the transformative impact 6G may have on communication systems by implementing smart network infrastructures.
[84]	Massive MIMO	This work explores the potential of M-MIMO systems in B5G networks.
[85]	ML, mmWave, massive MIMO	This work explores the crucial role of ML algorithms in optimizing mmWave massive MIMO systems.
[86]	XL-MIMO	This paper dives deep into a key technology for 6G networks—extremely large-scale MIMO (XL-MIMO).
[87]	Massive MIMO	This paper provides an overview of Massive MIMO.
[88]	THz, MIMO	This article dives into the technical aspects of achieving extreme connectivity in future 6G networks, specifically focusing on the concept of “TKµ”.
[89]	MIMO	This paper comprehensively overviews massive MIMO systems and their role as key enabling technologies.
[90]	Massive MIMO	Recent studies on massive MIMO technologies.
[91]	Holographic MIMO	This article explores promising new technology for future 6G networks: holographic MIMO surfaces (HMIMOSs).
[92]	AI	This paper delves into the potential of AI to revolutionize 6G networks through knowledge engineering.
[93]	AI, VR	This study explores the potential of integrating AI, VR, and high-speed 6G networks into classroom environments to enhance the learning experience.
[94]	AI	This article takes a pioneering approach to quantifying trust in AI for future wireless communication networks.
[95]	AI	This review highlights XAI as a crucial research area for developing trustworthy 6G networks.
[96]	AI	This paper presents a vision for 6G that focuses on achieving significant cost reductions to enable near-universal connectivity.
[73]	AI, AI-computing, metaverse	This survey explores the potential of integrating 6G edge intelligence into the metaverse, a future immersive virtual world.
[97]	AR, Blockchain	Discusses the adoption of blockchain for ensuring data privacy in AR applications within 6G networks.
[98]	Blockchain, AR/VR	This article explores the potential of combining BC technology with 6G networks to support secure and reliable AR/VR applications in Industry 4.0.
[99]	VLC	This paper explores the potential of VLC as a key technology for future 6G networks.
[100]	VLC, IoT	Surveys VLC in 6G IoT architecture, focusing on security aspects and implementation.
[101]	VLC	This research paper explores a novel approach to improve security in VLC systems for future wireless networks.
[102]	VLC, ML	This paper explores the potential of integrating FL into VLC systems.
[103]	Robot avatar, XR	This paper proposes a novel approach for programming robots using a combination of collaborative reality (CR) and XR technologies.
[104]	Metaverse	This paper explores the network infrastructure needed to support the metaverse in future 6G networks.
[105]	AI/ML, zero-touch network	This paper explores using AI and ML to improve network integration between satellite and terrestrial communication systems.
[106]	Zero-touch network	This paper discusses a new approach to network management called ZSM, which aims to automate network operations in 5G and future 6G networks.
[107]	Zero-touch network, IoT	Proposes a zero-touch network-based router management scheme for 6G IoT ecosystems.
[108]	Zero-touch network, IIoT	This paper proposes a new framework (AdaptSDN) to address security challenges in IIoT applications on future 6G networks.
[109]	Blockchain	This paper explores the potential of blockchain technology to address trust-related challenges and pave the way for secure and efficient 6G networks.
[110]	Blockchain	This article explores the potential of blockchain technology to manage resources and enable new applications in future 6G networks.
[111]	Blockchain	Explores challenges and opportunities related to blockchain integration in 6G networks.
[112]	Blockchain	This paper explores the promising integration of blockchain technology into future 6G wireless networks.
[113]	Blockchain, IoT	This paper examines the potential of combining 6G-enabled IoT with blockchain technology.
[114]	Blockchain, AI	Demonstrates the effectiveness of blockchain for data security in AI applications within 6G networks.
[115]	Blockchain, UAVs	Proposes a blockchain-envisioned security solution for UAV communication in 6G networks.
[116]	Blockchain, IoT, MEC	This paper explores the challenges and opportunities of integrating blockchain technology with 6G-enabled IoT networks.
[117]	THz, MIMO	This paper introduces a new channel model designed specifically for THz communication and MIMO systems.
[118]	THz	This paper explores the potential of THz technology as a key enabler for future 6G networks.
[119]	THz, mmWave	This paper explores the role of mmWave and THz frequencies in supporting the growing demands of wireless communication systems.
[120]	Digital Twin	This paper proposes using digital twins as a key enabling technology for future 6G networks.
[121]	Digital Twin	This article explores using digital twins for future wireless communication and sensing systems.
[122]	Digital Twin	This article highlights the potential of DT networks (DTNs) as a revolutionary tool for designing and managing future 6G networks
[123]	IoE	This article explores the potential of near-field radiating WPT as a solution for powering devices in future 6G networks, particularly IoE.
[124]	IoE, UAV	This article explores the challenges and opportunities of using UAVs for wireless energy transfer (WET) in future 6G networks, particularly in IoE.
[125]	IoE	This article proposes a novel hybrid algorithm for optimizing resource allocation in 6G networks designed for the IoE.
[126]	MEC	This paper explores the role of MEC in enabling metaverse over future 6G communication networks.
[127]	MEC	This paper focuses on edge computing in the context of future 6G networks and the IoT.
[128]	UAV, IRS, MEC, THz	Optimizes resource allocation and phase-shift for MEC-enabled UAVs in IRS-assisted 6G THz networks.
[129]	MIMO, MEC	This paper investigates energy consumption in 6G networks that utilize three key technologies: NOMA, MIMO, and MEC.
[130]	MEC, IRS	This paper addresses the challenge of minimizing delays in MEC systems for future 6G networks.

summarizes the recent proposed studies and their contributions toward 6G development based on the previously introduced key technologies.

5. Unmanned Aerial Vehicles (UAVs)

UAVs (e.g., drones), a developing field, are useful for a variety of purposes and can assist several industries. For about 30 years, the military has mostly used UAVs for reconnaissance, surveillance, and border strikes. UAVs are also utilized by civilian entities such as law enforcement agencies, crisis response teams, and traffic management. During natural calamities or emergencies where communication lines are cut off, UAVs can be extremely helpful as they can deliver lifesaving equipment and medications quickly and can survey dangerous zones rapidly. They are great for assessing remote areas while minimizing the risk to human management in crisis situations involving chemical fumes release, forest fires, or wildlife tracking altogether [131].

The goal of upcoming mobile networks (B5G/6G) is to expand coverage areas, support more devices, and increase data transfer rates in order to meet the growing demand for data from users. UAVs provide promising solutions for such challenges and introduce multiple ways of supporting 6G requirements and applications. The difficulties faced by end users who reside near the limits of current cellular infrastructures sparked researchers’ interest in utilizing autonomous UAVs as relocatable cell sites. Since drones exhibit impressive nimbleness and adaptability, integrating them as part of wireless systems could lead to noticeable improvements in service availability and proficiency for individuals dwelling beyond main access points. This concept has gained traction as it presents a promising methodology to complement or replace ground-based nodes whenever necessary. UAVs can not only aid ground BSs in offloading data traffic, but they can also improve the channel conditions of edge users by flying close to them to provide LOS links [10].

According to recent research conducted by the 3GPP, there is a need for new strategies to manage interference caused by the increased line-of-sight interactions between aerial UE and ground BSs. This challenge is due to the elevated position of aerial UE and their closer proximity to ground BSs, which highlights the importance of developing innovative tactics to integrate both aerial and ground UEs smoothly within the same cellular framework. While the 3GPP primarily focuses on connecting UAVs to existing cellular networks, other academic and industrial efforts are advancing toward a more sophisticated research stage to utilize the full potential of UAV communication systems. By exploring the possibilities presented by UAV-carried relay devices and mobile BSs that can be relocated to improve network coverage, capacity, and QoE for end-users, experts aim to transform the way future communication networks operate.

5.1. Features and Specifications of Available Market UAVs

Recent advancements in UAV technology have led to significant improvements across various industries. Research highlights efficient cooperation among learning agents in autonomous systems, optimizing communication resources and enhancing UAV performance. Innovations like color-agnostic disparity estimation methods in multi-camera setups improve visual data capture and processing. Additionally, integrating IoT sensors, drones, Wi-Fi, and cloud technologies revolutionizes agricultural practices, enabling precise soil data extraction and crop recommendations, thereby boosting efficiency and productivity. These developments underscore the rapid evolution and expanding capabilities of UAV technology. In 2023, the worldwide market size for anti-drone technology was estimated to be USD 1872.1 million, and it is anticipated to experience a CAGR of 28.1% from 2023 to 2030. The increasing popularity of UAVs for both commercial and recreational purposes has contributed significantly to this growth [132].

The global UAVs market is segmented by region, with North America holding the largest market share driven by strategic contracts and deals, particularly with the U.S. Department of Defense’s significant investment in drone technology. This includes a recent multi-billion-dollar contract with major aerospace companies to develop next-generation UAVs for enhanced reconnaissance and surveillance and commercial agreements in logistics and agriculture employing drones. While the drone industry itself is dominated by DJI, which commands a substantial 70–80% market share as of 2024 and is known for its extensive range of high-quality, other innovative consumer and professional drones play major roles in UAV applications [132,133].

These major players in the UAV industry focus on both consumer and commercial drones, emphasizing racing drones and industrial applications. Each company brings unique innovations and contributions to the diverse and dynamic drone industry [133,134].

Table 18. Main features of common market available drones.

Manufacturer	Vision	Model	Specifications	Main Applications	Communication Interface	Communication Range	Recommended Uses	Ref.
Parrot	Parrot is known for its versatile and cutting-edge drones serving both consumer and commercial markets.	Anafi USA	32x zoom 4K HDR video Thermal imaging	Inspection Surveillance Mapping	Wi-Fi	Up to 4 km	Professional use in security and inspection	[135]
		Anafi AI	4K HDR video FPV mode 3-axis stabilization	Immersive flight experience	Wi-Fi	Up to 4 km	Enthusiast-level FPV flying	[136]
Autel Robotics	Autel Robotics is recognized for its advanced EVO series featuring high-end camera capabilities and robust designs.	EVO Lite+	1-inch CMOS sensor 6K video 40 min flight time	Aerial photography Videography	Autel SkyLink	Up to 12 km	Professional and enthusiast photography	[137]
		EVO II Pro	6K video 1-inch sensor 40 min flight time	High-end aerial imaging	Autel SkyLink	Up to 9 km	Professional-grade videography	[138]
Skydio	Skydio is gaining prominence with its autonomous drone technology, particularly in defense applications.	Skydio 2+	4K60 HDR video Autonomous flight 360° obstacle avoidance	Autonomous filming Inspection	5 GHz Wi-Fi	Up to 6 km	Autonomous tracking and filming	[139]
		Skydio X2D	Dual sensor 4K60 HDR video Thermal imaging	Defense Inspection Public Safety	5 GHz Wi-Fi	Up to 6 km	Military and industrial applications	[140]
Yuneec	Yuneec caters to both consumer and commercial markets with notable models.	Typhoon H Plus	1-inch sensor camera 20MP stills 4K video	Photography Videography	2.4 GHz and 5.8 GHz	Up to 1.6 km	Professional aerial imaging	[141]
		Mantis Q	4K video Voice control 33 min flight time	Recreational flying Photography	2.4 GHz	Up to 1.5 km	Hobbyists and casual users	[142]
Hubsan	Hubsan offers a wide range of affordable, innovative drones for all user levels	Zino Mini Pro	48MP camera 4K video 40 min flight time	Consumer photography Videography	Syncleas 3.0	Up to 10 km	Beginner to intermediate aerial photography	[143]
		ACE 2	4K UHD camera Foldable design	Beginner-level aerial photography	5.8 GHz Wi-Fi	Up to 1 km	Entry-level users	[144]
Holy Stone	Holy Stone is known for its affordable yet high-quality drones that are suitable for beginners and experienced pilots.	HS720G	4K EIS camera GPS 26 min flight time	Recreational photography Learning	2.4 GHz	Up to 1 km	Beginners and hobbyists	[145]
		HS710	4K UHD camera GPS Foldable design	Travel photography Videography	5 GHz Wi-Fi	Up to 0.8 km	Travelers and casual users	[146]
Syma	Syma specializes in hobby-grade remote-controlled drones and helicopters.	X500	4K UHD camera GPS 26 min flight time	Learning Recreational use	2.4 GHz	Up to 0.8 km	Entry-level users	[147]
		X8 Pro	720p HD camera GPS 9 min flight time	Recreational flying Learning	2.4 GHz	Up to 0.2 km	Beginners	[147]
JJRC	JJRC produces budget-friendly drones popular among hobbyists.	X12 Aurora	4K camera GPS 25 min flight time	Hobbyist photography	5 GHz Wi-Fi	Up to 1.2 km	Hobbyists	[148]
		X17	4K camera GPS 30 min flight time	Aerial photography Videography	5 GHz Wi-Fi	Up to 1 km	Hobbyists	[148]
Walkera	Walkera offers a range of drones catering to various needs, from entry-level to advanced users	Vit Drone	4K camera Modular payloads	Professional aerial photography	5.8 GHz	Up to 2 km	Industrial applications	[149]
		F210 3D	FPV racing drone 700TVL camera	Racing FPV flying	5.8 GHz	Up to 0.8 km	Drone racing enthusiasts	[149]

summarizes the main features and specifications of the currently available drones.

UAVs are classified based on various attributes such as maximum altitude, weight, payload capacity, range, fuel type, operational complexity, coverage range, and applications. These attributes help categorize UAVs and provide information about their capabilities and potential uses [131]. UAVs can have different numbers of propellers, ranging from three to eight. They are categorized into fixed-wing, fixed-wing hybrid, single-rotor, and multirotor types. Fixed-wing UAVs have wings and a forward-flight motor but lack hovering and rotating abilities. Fixed-wing hybrid UAVs combine automated gliding and manual operation. Single-rotor UAVs are mechanically complex, expensive, and require specialized training. Multirotor UAVs, such as quadcopters, hexacopters, and octocopters, are affordable and widely used [150]. Furthermore, UAVs can be categorized, based on their size, into nano, micro, mini, medium, and large.

Table 19. Main features of UAVs categories [150,151].

Category	Description	Size	Specifications	Typical Altitude	Applications	Examples
Nano	Nano UAVs are extremely small, with a wingspan of less than 7.5 cm	<15 cm	Weight: <250g Battery: 5–10 min Camera: 720p or none	<50 m	Indoor flying Beginner training	DJI Tello Hubsan Q4 Nano Holy Stone HS190 Robobee X-wing
Micro	Mico UAVs have wingspans ranging from 7.5 cm to 15 cm	15–30 cm	Weight: 250–500 g Battery: 5–15 min Camera: 720p to 1080p	50–100 m	Recreational flying Basic photography	Syma X20 JJRC H36 Cheerson CX-10 DelFly Micro
Mini	Mini-UAVs have dimensions ranging from 15 cm to 30 cm	30–60 cm	Weight: 500–900 g Battery: 20–30 min Camera: 1080p to 4K	100–500 m	Aerial photography Videography Hobbyist flying	DJI Mini 3 Pro Autel EVO Nano Hubsan Zino Mini WASP AE
Medium	Medium UAVs have wingspans between 30 and 75 cm	60–150 cm	Weight: 1–5 kg Battery: 20–40 min Camera: 4K, thermal imaging	500–3000 m	Professional photography Inspection Mapping	DJI Mavic 3 Yuneec Typhoon H Plus Parrot Anafi USA
Large	Large UAVs are designed for long-endurance flights for surveillance and targeted delivery	>150 cm	Weight: >5 kg Battery: 30–60 min High-end sensors Payloads	>3000 m	Industrial Inspection Agricultural Mapping Military applications	DJI Matrice 300 RTK Skydio X2D Walkera Vit Drone Global Hawk

introduces these five categories of UAVs and provides their specifications [150,151]. Furthermore,

Table 20. Specifications of common market available UAVs.

Ref.	UAV Model	Features
[152]	RoboBee X-Wing (Nano UAVs)	Is 5 cm long and weighs 259 mg. Limited altitude due to low altitudes due to its small size and power constraints. Limited range, primarily for short-distance operations. Flapping wings. Powered by solar cells. Extremely limited coverage. Designed for indoor or very localized outdoor use.
[153]	DELFLY (Mico UAVs)	It is about 29 g and has 33 cm wingspan robot. Can fly for over 5 min on a fully charged battery. Has 14 cm wings. Can reach a top speed of 7 m/s (25 km/h). Powered by small rechargeable batteries. Designed for indoor flight or short outdoor distances.
[154]	WASP AE (Mini UAVs)	Weight: Approximately 1.3 kg. Endurance: Up to 50 min. Range: Up to 5 km. Length: 0.76 m. Fixed wing with electric motor and pusher propeller. Hand-launched in a confined area with remote launch capability. Powered by a rechargeable battery. Used for intelligence, surveillance, and reconnaissance (ISR).
[155]	NASA SIERRA (Medium UAVS)	Length of approximately 3.7 m. Wingspan of about 6 m. Can fly for 9.0 h (payload and weather dependent) Max altitude: 3962.4 ft. Range: 963 m. Speed: 30.86. Can carry up to 49.89 kg. Used for atmospheric sampling, vegetation studies, geological surveys, and oceanographic research.
[156]	Global Hawk (Larag UAVs)	Wingspan: 35.4 m; take-off gross weight (max): 11,611.96 kg. Payload: 680.38. Range: 20,372 km. Duration: 31 h (maximum). True airspeed @ 1.6km altitude: 620 km/h airspeed. DC (engine-driven generator): 28 VDC, hydraulic-powered generator. Engine: Allison AE-3007H turbofan. Applications: military, critical ISR support, disaster relief, and border surveillance.
[157]	Parrot Disco	Wingspan: 1.15 m; length: 0.58 m; height: 0.12 m; weight: 750 g. Powered by lithium polymer battery (2700 mAh/25A 3-cell). Maximum speed: 93 km/h. Suitable to carry cellular UEs. Fixed wing with a brushless motor and propeller for propulsion. Up to 2 km range. Used for aerial photography and videography.
[158]	Kogan	Its size varies by model, typically ranging from small palm-sized drones to larger models ranging from 0.2 to 0.5 m. Weight: 0.16–1.2 kg. Powered by rechargeable lithium polymer batteries 3.7 V/160 mAh. Flight time: 11–13 min. Used for aerial photography and videography.
[159]	Scout B-330 Helicopter	Weight: 85 kg Payload capacity of up to 50 kg. Typical mission scenario: at least three hours. Multirotors (two rotors used). Powered by gasoline. Max. speed: 100 km/h. Applications: surveillance, mapping, and surveying.
[160]	MQ-9A “Reaper”	Wingspan: 20m; length: 11m; Weight: 2223 kg. Max payload capacity: internal: 386kg; external: 1361 kg. Max endurance: 27h. Fixed-wing. Maximum altitude:15,240 m. Maximum speed: 482 km/h. Used for armed reconnaissance, airborne surveillance, and target acquisition.
[161]	DJI Spreading Wings S900	Diagonal wheelbase: 0.9 m; length: 0.358 m; width: 0.043 m; weight: 3.3 kg. Can carry up to 5 kg, suitable for various camera and gimbal setups. Electric (LiPo batteries). Multirotors Hexacopter (six rotors). Maximum speed: 57.6km/h. Flight time varies based on payload, typically 15–18 min. Suitable to carry cellular BSs or UEs.

provides detailed specifications on some of the common markets available UAVs of each category.

The payload of a UAV refers to its capacity to carry a load, which encompasses all the equipment carried by the UAV, including cameras, sensors, radars, LIDARs, communication equipment, weaponry, and other relevant devices. These payloads serve various purposes, such as capturing imagery, collecting data, facilitating communication, and enabling specific mission objectives. In drone technology, there is a tradeoff between payload capacity and flight duration [162]. Drones often use lightweight lithium-ion batteries for power, but these batteries lack backup power compared to other types. As the payload increases, the drone’s endurance decreases, potentially leading to incomplete missions due to limited flight time. UAV payload is an important characteristic that plays a critical role in estimating the applications, mainly when deploying UAVs to assist coverage and computing resources in 6G networks.

Table 21. Common payload models of UAVs [162,163].

Payload Type	Functionality	Applications
Electro-optical (EO) cameras	High-resolution visible light imaging	Aerial photography Surveillance Inspection
Multispectral cameras	Capture images in specific wavelengths beyond human vision	Agriculture (monitoring crop health, identifying pests) Environmental monitoring Resource exploration
Thermal imaging cameras	Capture heat signatures, useful in low-light conditions	Search and rescue Infrastructure inspection Wildlife monitoring Nighttime security
Hyperspectral cameras	Capture images in hundreds of wavelengths, providing very detailed spectral information	Precision agriculture Mineral exploration Pollution detection
Light detection and ranging (LiDAR)	3D mapping, object detection	Precision mapping Surveying Construction planning Stockpile measurement
Synthetic aperture radar (SAR)	Creates high-resolution radar images, independent of weather conditions, penetrates clouds/smoke	Flood mapping Ice monitoring Disaster response Military reconnaissance
Communication relays	Extend communication range	Disaster response Remote area communication Temporary comms networks
GNSS receivers	Precise positioning and timing	Surveying Mapping Navigation
Laser designators	Target marking	Military operations Guided munitions
Radiofrequency (RF) sensors	Monitoring and analyzing radio frequencies	Communications Signal intelligence Spectrum management
Infrared sensors	Detecting infrared radiation	Heat mapping Energy audits Search and rescue
Atmospheric sensors	Collecting weather data	Meteorology Climate research Disaster response
Delivery containers and cargo hooks	Carry and deliver goods autonomously	Package delivery Construction External load-carrying

provides the common payload models of UAVs and the estimated applications for each model.

5.2. UAVs Applications in the 6G Era

UAVs have seen significant advancements and widespread adoption in diverse industries and sectors, from military applications to commercial use cases, due to their convenient deployment, cost-effectiveness, maneuverability, and hovering capabilities. UAVs have many applications in diverse areas, such as crop monitoring, water quality assessment, tree species identification, disease detection, and mine detection. Additionally, UAVs can be utilized in firefighting scenarios to minimize fire disaster evaluation risks. Firefighter UAVs can act as scouts, carrying cameras with thermal imaging technology to aid rescue operations [131]. This section provides the possible applications of UAVs in the era of 6G networks.

a. 

UAV for cellular coverage

UAVs have been employed as aerial BSs to provide wireless coverage when terrestrial BSs cannot be set up [164]. Especially in emergency situations, such as military operations or disaster rescue, it is very useful. In order to prolong the service life of UAVs, energy-saving methods are practical and particularly important, especially considering their limited onboard energy. Current work aims at minimizing energy consumption and optimizing the trajectory and power of UAVs in order to enhance performance. UAVs can be used as flying BSs, relay stations, or LAs, upgrading conventional wireless communication systems by adding benefits to the end-user experience, spectral efficiency, and coverage. The idea of Cellular-Assisted UAV Communication has attracted attention as a means to ensure reliable wireless access between UAVs and ground cellular stations. Cellular-based UAVs have much wider-ranging coverage and perform better than traditional ground-to-UAV communications [10]. They can also serve as aerial BSs under cell network disruption scenarios and improve ground BS coverage [165]. These UAVs can further interface with communication infrastructure and satellites to offer high data rates.

However, deploying UAVs for cellular coverage faces many challenges, including path loss models, deployment strategies, and interference mitigation techniques. Current research in this direction includes exploring various use cases for these BSs, providing widespread coverage, acting as network gateways, functioning as relay nodes, and collecting data. Current research emphasizes the advantages of cellular-connected UAVs over traditional ground-to-UAV communications, such as improved performance, easy monitoring, and cost-effectiveness. Furthermore, an important direction is to address the unique communication and spectrum requirements of cellular-connected UAV systems and propose a design for heterogeneous networks using UAVs. Exploiting already available frequency bands and utilizing techniques such as millimeter waves and free space optics have been reported as ways of enlarging UAVs’ data link capabilities [10]. The contribution of control links for safe UAV operations and the requirements for low latency and high reliability of control links are critical challenges that need to be solved.

b. 

Precision agriculture

Precision agriculture is essentially about applying the appropriate inputs at the right time and place in a way that will lead to the best possible product outcomes. Some of the key activities that are included in precision farming are collecting data and mapping the variability of agricultural lands, analyzing the data in order to inform management decisions, and the use of controlled application techniques such as targeted pesticide spraying and fertilizer usage. The main aim is to enhance agricultural practices using data-driven decision-making processes [131]. One of the popular applications of UAVs in precision agriculture is weed mapping. Weeds might be the cause of severe problems in agricultural crops because they compete for resources and reduce crop yields.

Herbicides are frequently used for the weed issue, but their excessive use can result in the appearance of herbicide-resistant weeds, increased environmental pollution, and a higher level of costs. UAVs are crucial in making precise weed cover maps for the exact herbicide application. Other applications of UAVs include monitoring vegetation, yield prediction, and plant and crop disease assessment. They produce important information about biomass, nitrogen educational process, crop parameters, and the detection of diseases, which helps farmers decide on crop management, inputs, and disease control. In addition, UAVs are also employed for precision irrigation as they identify the areas that are in need of water; hence, water usage efficiency is increased, crop productivity is enhanced, and specialized soil morphology maps are created for efficient irrigation planning [166].

c. 

Surveillance

Surveillance involves monitoring and collecting information on people, behaviors, activities, infrastructure, or buildings. Surveillance comprises several techniques aimed at seeing and collecting information in diverse environments. Principal methodologies include the following [131,167].

• Camera surveillance: Employing video cameras to record real-time visuals for the oversight of public areas, private properties, or essential infrastructure. This technique is frequently employed for security, crime deterrence, and the assurance of public safety.
• GPS tracking: Utilizing GPS technology to monitor the location and movement of cars, individuals, or assets. This technique is extensively employed in fleet management, personal protection, and logistics.
• Radio surveillance: Entails the interception and monitoring of radio frequency signals, essential for intelligence acquisition, communication security, and the oversight of unauthorized transmissions.
• Biometric surveillance: Employing biometric data, including facial recognition, fingerprint scanning, and iris recognition, to identify and monitor persons. This approach improves security by offering dependable identity verification in access control systems and public surveillance.

Surveillance activities encompass diverse sectors and fulfill numerous objectives.

Table 22. Main surveillance applications in different fields [131,167].

Field	UAV Application
Traffic surveillance	Camera systems and sensors are deployed on roadways to regulate traffic flow, identify accidents, and enforce traffic laws.
Border patrol	Surveillance technologies are utilized to oversee and secure national borders, identify unlawful crossings, and thwart smuggling operations.
Power grid inspection	Surveillance techniques, such as drones outfitted with cameras and thermal imaging, are utilized to examine power lines and substations, detect defects, and avert outages.
Construction management	Surveillance via cameras and drones is employed to monitor construction sites, guaranteeing adherence to safety requirements, project schedules, and quality benchmarks.
Environmental monitoring	Surveillance technologies are employed to assess environmental conditions, including air and water quality, deforestation, and wildlife movements, facilitating conservation initiatives and disaster response.

provides the main sectors that cover surveillance activities. Traditional surveillance methods are labor-intensive and inefficient for large-scale and scattered environments. UAVs offer a cost-effective and efficient solution for surveillance and monitoring tasks.

d. 

UAV-enabled edge computing (aerial edge computing server)

MEC is a distributed computing model that facilitates task computing at the network edge to reduce latency and boost the computing capabilities of UE. Since MEC servers are located close to the UE, tasks can be processed at the edge, resulting in less workload being transferred to the cloud and a decrease in latency [168]. Edge computing and UAV systems have a connection that allows UAVs to provide edge computing services to ground user equipment (UE). This means that UAVs can have edge servers installed on them to perform computing tasks for users on the ground. Alternatively, UAVs can also act as users themselves and delegate tasks to edge servers. In traditional cloud computing, tasks would be sent between UAVs and a centralized server located far away.

However, with edge computing, the computational services are brought closer to the UAVs and users at the network’s edge. This eliminates the need for data to travel long distances to reach remote centralized servers. The computational services can be either on the UAVs themselves or at nearby edge locations [169]. In [170], the authors discussed the role of UAV-enabled MECs network architecture applications based on AI for IoT applications. Another framework is introduced in [171], which uses UAV-enabled MEC for energy-efficient resource management approaches in smart IoT device networks. S. A. Huda et al. [172] provided a comprehensive survey of computation offloading in a UAV-enabled MEC environment.

e. 

UAV-assisted backscatter communication

UAVs have emerged as versatile assets in backscatter communication systems, offering multiple roles to enhance the efficiency and reliability of data transmission. Most importantly, UAVs have been used as carrier transmitters, radiating RF signals that are then backscattered by devices that can be modulated to carry data without the need for active RF generation. This method creates substantial energy savings in a backscatter device, and therefore, it is suited to Internet of Things applications, where energy savings are essential. UAVs can extend network coverage to extremely distant and difficult-to-reach areas. In addition, they would serve as aerial relay stations in situations where traditional infrastructure cannot be used. The altitude and position of the UAVs would also improve the LOS conditions between transmitting backscatter devices and receiving nodes when aerial relay stations are used. This will further reduce path loss and interference and create a more reliable communication link [10,131].

Backscatter communications is an emerging technology that offers low-energy communication systems by utilizing ambient RF signals from sources like TV towers, FM towers, and Wi-Fi access points [173]. Backscatter communication, aided by UAVs, would automatically be energy-efficient due to the reduced active RF transmission. The UAVs would harvest and backscatter the RF signals to ensure that IoT devices in the network operate on low power. Such technologies would be used in many applications concerning 6G networks, including smart agriculture and disaster recovery. Backscatter UAVs will dominate vast agricultural fields, transmitting data from soil sensors to centralized servers. Furthermore, UAVs can quickly set up a disaster-focused network in short order.

In situations where a direct channel between the backscatter device and receiver is not possible, UAVs can be used successfully as data relays. Specifically, by hovering or flying between the backscatter devices and ground (or space-based) receivers, UAVs can collect the weak backscattered signal and relay it to the intended receivers while overcoming the communication gap in complex environments such as urban canyons, rural areas, and disasters [174]. In addition, UAVs can also function as mobile receivers in backscatter wireless sensor networks. In this role, UAVs fly over a designated area, collecting data from dispersed backscatter sensors. This mobility permits UAVs to collect data from a variety of sensors across the flight, providing flexibility and scalability to the network.

To maximize the reliability and effectiveness of these operations, the flight legs and sensor activations of UAVs can be effectively managed. Conjoint optimization of UAV trajectories and backscatter device operations guarantees that UAVs are always at the optimum location for signal quality and data acquisition efficiency with minimal energy consumption. For instance, machine learning algorithms can be deployed to predict optimal flight routes based on sensor locations and environmental factors, dynamically adjusting UAV movements to maintain strong communication links. This cooperative effort enhances the performance of backscatter communication systems and minimizes the network’s lifetime by efficiently utilizing the finite power capacities available in the backscatter devices. With these breakthroughs, UAV-based backscatter communication systems promise powerful and energy-efficient communication solutions for a wide range of applications in 6G networks [131].

5.3. Challenges with UAVs

The deployment of UAV/swarm UAVs in diverse environments presents challenges in decision-making, control, path planning, communication, monitoring, and more. Safety, privacy, security, and power are significant concerns. Safety risks arise from the lack of GPS alerts and privacy issues related to data collection. Security risks include signal vulnerability and data hijacking. Power-related challenges involve longer flight times and the need for standardization. Wireless sensor limitations affect performance, and resource allocation is crucial. Regulatory changes and power constraints need to be addressed. Social perception, privacy, safety, and environmental concerns are additional challenges. UAVs offer notable features but face various open issues. Categorizations include operability, technology, regulations, safety, privacy, and security, but alternative perspectives may exist. Figure 3 presents the main challenges associated with deploying UAVs for different applications.

Existing research on UAVs in complex environments often overlooks important environmental factors, leaving room for further exploration. Factors such as wind patterns and solar angles significantly impact UAV performance. Wind patterns can disrupt a UAV’s orientation, location, and speed, necessitating the integration of wind-based elements and mathematical models to study their influence on trajectory and energy transmission. The study of control modalities in human–drone interaction focuses on the challenging task of effectively controlling drones. This requires users to undergo extensive training and dedication to safely and accurately operate drones [175].

5.4. Dynamic Challenges and Adaptive Control Mechanisms of UAV-Based Networks

The high mobility of UAVs presents challenges for maintaining network stability. Unlike traditional static networks, UAV-based networks undergo rapid changes in topology, necessitating adaptive control mechanisms to ensure smooth operation [169]. UAV networks function differently from ground-based networks due to several key challenges, including the following aspects.

• UAV networks are high/ultra-high mobility networks since UAVs frequently shift their positions, resulting in a highly dynamic network topology. As UAVs move, the network topology is constantly in flux. This evolution impacts routing, handoff management, and connectivity maintenance [169]. Traditional routing protocols often struggle to keep pace with these changes, resulting in frequent link failures and increased latency. Adaptive control mechanisms are crucial for tackling such challenges. These systems adjust network parameters in real-time based on current conditions. Routing in UAV networks must adapt to swift dynamic changes in topology [176]. Key strategies include deploying position-based routing, AI/reinforcement learning-based routing, and swarm intelligence approaches. Position-based routing approaches utilize GPS for real-time updates to routing paths. Other approaches employ AI to make informed decisions about the best paths using historical data. Furthermore, other methods apply bio-inspired algorithms, such as ant colony optimization, to optimize routes on the fly [131].
• Air networks have variable link quality. Several factors, including altitude, weather conditions, and interference, can influence wireless communication between UAVs. The quality of communication links between UAVs can vary due to several factors, including Doppler shifts resulting from UAV movement, multipath fading caused by reflections from buildings or terrain, and weather-related interference, such as rain and fog, which can disrupt signal propagation [177]. UAV networks frequently operate in congested frequency bands, necessitating adaptive spectrum allocation to minimize interference. Dynamic spectrum management approaches, such as CR and frequency hopping, can be used to maintain network stability in UAV networks. UAVs detect available spectrum and switch to less crowded channels using CR [178]. However, using frequency hopping, UAVs quickly alternate between frequencies to reduce interference and counter jamming threats. To enhance communication stability, UAVs utilize link adaptation methods, including adaptive modulation and coding, and beamforming. Adaptive modulation and coding methods adjust transmission rates dynamically based on channel conditions, while beamforming directs signals toward specific areas to improve link reliability [179].
• UAVs are energy constraints, operating on limited battery power. With limited battery capacity, UAVs must prioritize energy-efficient networking. Extended communication and computation tasks can quickly deplete power, reducing operational time. Considering the limited battery life of UAVs, energy-efficient strategies include sleep scheduling, energy-efficient routing, and solar-powered UAVs. Using sleep scheduling, UAVs enter low-power modes when not actively transmitting data, thus reducing energy consumption [164].
• UAVs often operate autonomously or in semi-autonomous modes, which demands robust coordination. AI-driven adaptive control mechanisms allow UAV networks to effectively predict and respond to changing conditions. Predictive analytics, deep learning-based channel estimation, and autonomous flight path optimization techniques are commonly used for network adaptation. Predictive analytics leverages past mobility patterns to forecast future UAV positions and adjust network parameters. Deep learning-based channel estimation can be used to enhance link quality prediction and refine adaptive modulation techniques. Furthermore, AI approaches can be used to optimize UAV routes, balancing network coverage with energy consumption.
• UAV networks are frequently employed for critical missions, including search-and-rescue operations, surveillance, and traffic monitoring. These applications demand low-latency communication, which is challenging given the frequent link disruptions and high mobility. UAV networks play a crucial role in providing emergency communication during natural disasters [131]. Adaptive routing ensures stable connectivity, even as UAVs adjust their positions in response to changing ground conditions. Furthermore, UAV networks can be used to monitor traffic, identify anomalies, and support law enforcement efforts. Adaptive control mechanisms enhance coverage and help alleviate network congestion.

6. Terahertz Communications

The high demand for mobile data traffic is growing rapidly, and data rates are predicted to reach multiple tens of Gbit/s. This raises concerns about having enough available spectrum to meet these demands. Achieving spectral efficiencies of tens of bit/s/Hz is challenging under practical constraints, so the only option is to increase the available bandwidth to several tens of GHz. However, it is not feasible to identify such a large amount of spectrum in the regulated frequency bands below 300 GHz [11]. In order to meet the increasing traffic demands for wireless data, millimeter-wave (mmWave) technology, as the subsystem in the 30–100 GHz RF band, has become an indispensable tool for indoor and outdoor wireless communications. MmWave technology exploits the large, unused spectrum in the millimeter-wave bands, resulting in higher bandwidth, which allows for significantly faster data rates and a better capacity of the network in comparison to standard sub-6 GHz frequencies [13].

This promise has spurred the creation of a number of standards as a way to maximize mmWave communications in specific applications, such as IEEE 802.11ad and IEEE 802.11ay for fast wireless local area networking (WLANs), IEEE 802.15.3c for wireless personal area networking (WPANs), and the 5G new radio (NR) access networks specified by 3GPP [45]. The integration of mmWave technology into 5G networks has already demonstrated its ability to deliver ultra-high-speed wireless connectivity, low latency, and massive capacity, making it a cornerstone of modern communication systems. Applications including AR, VR, and real-time 4K/8K video streaming have highly leveraged the high throughput of mmWave. Therefore, mmWave is also essential to support dense urban deployments and the mass of device connectivity in IoT environments.

Nevertheless, while it has its advantages, mmWave technology also has inherent limitations that can restrict its performance in addressing the future requirements of wireless data traffic. As the high-frequency signals in the mmWave band are very sensitive to the propagation impairments of path loss, atmospheric absorption, and blockage from physical objects, like walls, trees, and even human bodies, they are very demanding. These problems lead to insufficient coverage and require the use of highly dense small-cell networks, sophisticated beamforming, and smart resource management in order to maintain stable communication links. Furthermore, as data traffic continues to grow exponentially, driven by emerging technologies such as holographic communication, autonomous vehicles, and industrial automation, even mmWave networks may struggle to provide the required performance levels. This deficiency highlights the demand for ad hoc solutions, such as THz communication, RIS, and new network structures (e.g., cell-free massive MIMO cell-to-cell/cell-free vs. integral satellite-terrestrial loops on cellular scales) [84].

The IEEE has developed a communication standard for the THz frequency range by establishing the IEEE 802.15.x standard. This standard is being developed to provide a framework for high-speed wireless communication using frequencies in the terahertz range [180]. The THz spectrum band is considered one of the most promising options for enabling ultra-high-speed communications beyond 5G. THz communication offers much higher data rates, ranging from tens of Gbps to several Tbps, compared to mmWave band communication. Additionally, THz communication systems are less affected by atmospheric conditions in outdoor wireless communications compared to wireless optical communication. In indoor wireless communications, the THz frequency band is easier to track and maintain beam alignment compared to the optical frequency band, which greatly enhances the mobility of wireless communication systems. Furthermore, THz communication systems have the advantage of utilizing reflection paths to improve link gains in indoor applications. Given these advantages, further research and development of THz communications are crucial for future advancements in this field [11].

6.1. Main Features and Specifications of THz Communications

The THz frequency band, spanning from 0.1 to 10 THz, offers an unprecedented amount of bandwidth, potentially reaching several terahertz. This huge spectral capacity can be used for very high data rates of terabits per second (Tbps), which is much better than that possible with mmWave technologies. With such ultra-high-speed communication, THz technology is considered a crucial facilitator for the evolution of next-generation wireless networks, especially to better satisfy the increasing need for data-intensive applications with holographic communications, real-time AI, and large-scale IoT ecosystems. The THz band has the following features that make it preferable for some communication situations and applications [11,83,117,118].

• High directionality and security
  The directionality of THz signals is superior to mmWave signals because of the shorter wavelengths resulting in highly focused beams. Not only is spectral efficiency enhanced, but also the eavesdropping risk is decreased, making the whole communication secure.
• Non-LOS (NLOS) propagation
  In contrast to optical frequencies, THz waves can support NLOS propagation due to scattering and diffraction, suitable for uplink communication or highly populated urban areas.
• Resilience to weather conditions
  When THz signals work well under adverse weather conditions (fog, dust, and turbulence), then optical signals typically fail. This robustness ensures reliable communication in diverse environments.
• Low ambient noise and health safety
  The THz band is, in principle, very free from ambient noise interference due to optical sources. Moreover, THz radiation is non-ionizing and thus safer than higher-energy frequency radiation and is not under the restrictive health and safety guidelines imposed on higher-energy frequencies.

6.2. Standardization Efforts in THz Communication

Standardization of THz technology has been a major thrust in order to facilitate global adoption and interoperability. The career started in 2008 with the establishment of the IEEE 802.15 Terahertz Interest Group and ended with the dissemination of IEEE Std 802.15.3d-2017, which introduced the first worldwide standard of wireless communications in the 252–321 GHz band. This milestone provided the basis for ultrawideband wireless links in the THz range and, therefore, addressed the increasing need for communication systems for ultra-broadband applications. Attempts are still underway to extend the range of THz technology. Active efforts are currently underway to increase the frequency range covered by the standard up to 450 GHz. At the same time, the European Telecommunications Standards Institute (ETSI) formed the Industry Specification Group on Terahertz (ISG THz) to provide a platform for coordinating pre-standardization R&D activities. This effort is a prelude to the planned 6G standardization, which will commence in 2025. The ISG THz has planned to integrate international development efforts in THz technology by providing solutions to the related challenges, including transceiver design efficiency, spectrum allocation, and integration with existing networks.

The International Telecommunication Union (ITU) has played a key role in the global spectrum coordination and allocation for terahertz communication. The ITU’s actions are directed toward regulating and standardizing THz frequencies across a broad range to facilitate the seamless incorporation of future wireless communication systems. The key milestones toward THz standardization include the following.

(1)

World Radiocommunication Conferences (WRCs)

During the 2019 WRC (WRC-19), the ITU provisioned 160 GHz in the 252–450 GHz band for fixed and mobile services, with additional restrictions and security measures for passive services such as radio astronomy and Earth exploration satellite service providers. These assignments are intended to support high-speed wireless data transmission and industrial services without causing congestion to current services.

(2)

Spectrum Management Framework

The ITU Radiocommunication Sector (ITU-R) has conducted detailed studies to identify potential bands in the THz range that can be used without interfering with critical passive applications. ITU-R study groups have been intensively investigating the propagation nature of THz frequencies to best use them in different environments.

(3)

Coordination for 6G Integration

ITU is developing environments to embed THz communication into the developing IMT-2030/6G environment. These activities aim to specify use cases, performance requirements, and technical standards to be compatible with the global 6G visions.

The 3rd Generation Partnership Project (3GPP), the standardization body for cellular technology, is at the advent of basic work for integrating THz frequencies into 5G Advanced and future 6G networks. Their contributions include the following:

(1)

5G advanced and release 20

In the current 3GPP Release 20 (6G forerunner), the standardization organization is exploring the use of frequencies above 100 GHz, such as THz bands. This includes examining their ability to deliver high data rates and ultra-low latency communication needed by high-speed communication and sensing, such as holographic and high-speed sensing.

(2)

Study on high-frequency spectrum

The 3GPP is conducting technical feasibility studies on how to address THz communication issues. This includes the following.

• Developing strategies to avoid high propagation loss, such as beamforming, beam tracking, and ultra-dense networks.
• Overcoming hardware limitations by improving the efficiency of transceivers and antennas used at THz frequencies.
• Interference management and ensuring coexistence with other systems.

(3)

Support for new use cases

The 3GPP is coordinating its work on THz-based communication with the new applications for future 5G and 6G (e.g., immersive AR, VR, autonomous vehicle network, and smart city applications).

(4)

Channel modeling and performance evaluation

As part of the 3GPP study agenda, channel models are being developed for THz frequencies, which include diffraction, scattering, and atmospheric absorption. These models play an important role in designing practical THz communication systems.

The ITU and 3GPP are working with other global organizations, including the ETSI ISG THz, and IEEE, to achieve global consistency of Terahertz communication standards. These efforts aim to promote efficient spectrum usage, develop scalable hardware and software solutions, and contribute to the smooth introduction of THz communication into the 6G world. The planned 6G standardization, starting as early as 2025, will extend these foundational activities and become a sediment in the future of wireless communication, with THz transmission playing a leading role.

6.3. Applications of THz Communications

THz offers various applications and benefits across different domains. It enables Terabit cellular hotspots, providing high-throughput connectivity for densely populated areas and specific locations. Terabit campus/private networks leverage THz frequencies for ultra-high-rate, low-latency connectivity within private networks, supporting Industry 4.0 and Tactile Internet applications. THz communications enable direct Tbps links between devices in close proximity, facilitating device-to-device connections and V2X scenarios. Security in THz communications is enhanced through large-scale antenna arrays and spread spectrum techniques, although unique challenges need to be addressed. THz wireless backhaul reduces installation costs and improves coverage in areas where fiber connections are challenging. Finally, THz nano-communications enable wireless connections among nanoscale devices and have applications in health monitoring, defense, Internet-of-Nano-Things, and on-chip communication [11].

THz communications have the potential to be used in various applications. One of the initial applications could be a fixed wireless link that enhances the capacity of wireless network backbones in outdoor environments. Another application is the use of THz nano cells in a hierarchical cellular network, providing high capacity in specific hotspots. However, these applications require significant technical requirements. THz also has potential for WLAN/WPAN applications. It can also be used for high-capacity wireless links between computer peripherals on a desktop or for kiosk downloading, enabling quick content transfer from a dedicated kiosk to a mobile device. These applications have less demanding propagation conditions [181]. THz radiation, which is non-ionizing and safe for humans, has gained interest in biological and medical applications. It is highly sensitive to water content and has been used for THz spectroscopy and imaging in cancer detection, including skin cancer and various other tumor types. THz imaging offers complementary spectral information and is considered safe for widespread deployment, aiding surgical procedures and decision-making.

Table 23. Preliminary applications of THz in the 6G era [11,83,117,118,181].

Application	Description	Benefits
Ultra-high-speed wireless data transfer	Enabling Tbps wireless communication for data-intensive applications.	Unprecedented bandwidth for real-time streaming, AR/VR, and holographic communications.
Holographic communication	Supporting immersive 3D content and real-time holographic transmissions.	Enhanced user experience for remote collaboration, telepresence, and entertainment.
High-resolution imaging and sensing	Enabling THz radar and imaging systems for industrial, security, and healthcare applications.	High-resolution imaging for non-invasive diagnostics, security scans, and environmental monitoring.
Wireless backhauls	Providing ultra-fast links for backhaul in dense urban and rural deployments.	Cost-effective and high-capacity backhaul for 5G and 6G networks.
Terahertz IoT	Supporting high-speed connectivity for IoT devices in smart environments.	Seamless integration of massive IoT networks with low latency and high reliability.
Industrial automation	Enabling THz connectivity for real-time control in Industry 4.0 settings.	Enhanced operational efficiency and precision in automated manufacturing and robotics.
Satellite communications	Utilizing THz frequencies for inter-satellite and satellite-to-ground links.	High-speed and secure communication for space exploration and global connectivity.
Autonomous vehicles	Supporting high-speed V2X communication and advanced navigation systems.	Improved safety and efficiency in autonomous driving and traffic management.
Biomedical applications	Non-invasive diagnostics and imaging using THz frequencies.	Accurate disease detection and imaging without harmful radiation.
Secure communication	Leveraging directional and low-interference THz links for secure data exchange.	Reduced risk of eavesdropping and enhanced confidentiality in sensitive applications.
WLANs	Enabling high-speed local connectivity for office and home networks.	Faster data transfer for streaming, gaming, and remote work applications.
Environmental monitoring	Using THz sensing for real-time analysis of gases, pollutants, and aerosols.	Improved accuracy in climate studies, pollution control, and disaster response.
Quantum communication support	Enhancing QC networks with THz links.	High-speed and low-error transmission for quantum key distribution and computing.

provides preliminary 6G applications that can use THz benefits.

6.4. Challenges with THz

THz communication is going to play a key role in 6G networks, which can provide unprecedented bandwidth and a high data transfer rate that can satisfy the new generation’s applications, such as holographic communications, real-time digital twins, and super large MTC. However, despite their high potential, THz communications are still subjected to several critical issues that need to be addressed before their successful application. Collaborative efforts from the research community are required to tackle these obstacles and successfully implement THz communications. Designing the THz MAC system presents additional challenges due to the unique characteristics of THz communications. These challenges include addressing the “deafness” problem, which refers to the difficulty of receiving signals in THz frequency ranges. Additionally, complex operations such as network discovery and coupling need to be managed, and efficient concurrent transmission scheduling techniques must be developed [11].

Due to the absence of THz transceivers, hardware design for THz communications is a significant challenge. The THz band has been relatively unexplored in the electromagnetic spectrum, but technological advancements are making THz communication a reality. Generating THz signals poses unique challenges as the frequency range is too high for electronics-based devices and too low for photonics-based devices. Currently, THz signals are generated through frequency multiplication or photomixing. Graphene-based technology shows promise for generating THz signals due to the exceptional properties of graphene. THz signals can be pulsed or continuous, with pulsed signals being extensively studied in existing research. However, continuous signals require more complex and larger transmitters/antennas, requiring further research for future THz communications [83]. The key challenges with THz communications for 6G include the following.

• Path loss and absorption:
  Path loss remains a primary challenge in THz communication. The free-space path loss (FSPL) at THz frequencies is much worse than during the microwave and mmWave ranges due to the raised carrier frequency and the high inverse-square law during propagation [118]. The loss is defined by Friis’ equation as follows:

  $$FSPL\left(dB\right)=20{\log }_{10}\left(4\pi df/C\right),$$

  (1)

  where d stands for distance, f is the carrier frequency, and C represents the speed of light. The path loss worsens significantly with frequencies moving toward the THz domain (0.1–10 THz). This renders long-range communication virtually impossible unless measures are implemented. Furthermore,

  Table 24. Path loss characteristics at different frequencies in the THz range.

  Frequency (THz)	Distance (m)	Path Loss (dB)
  0.1	1	92.4
  0.3	1	101.6
  0.5	1	107.9
  1.0	1	114.8
  1.0	10	134.8
  1.0	100	154.8

  provides the typical path loss at different frequencies in the THz range. The table shows that there is unequivocal evidence suggesting the growth of device path loss with an increase in frequency and distance, which results in ineffective traditional wireless communication methods for THz implementations.
  Due to their relatively short wavelengths and higher frequencies, THz waves are hindered by severe propagation loss. Atmospheric attenuation caused by molecular absorption, particularly water vapor and oxygen, further exacerbates this issue, limiting the effective communication range. Unlike lower frequency bands, THz waves experience a high degree of attenuation owing to absorption from atmospheric gases, especially water vapor (H₂O), oxygen (O₂), and carbon dioxide (CO₂). The communication range is further narrowed by molecular absorption in lower effectiveness. Different frequencies that have specific absorption peaks also restrict the available spectral windows, resulting in little reliable THz communication [119]. For instance, at approximately 1 THz, the absorption level can reach tens and, at times, hundreds of dB/km, making long-range communication impractical without some form of relay or amplification strategies.
  Absorption changes immensely with frequency, which implies that water vapor is predominant for many of them. Certain frequencies possessing high absorption coefficients must be chosen cautiously, which makes these spectral planning windows for practical THz communication extremely limited. To address this challenge, next-generation applications need ultra-directional antennas, high-gain beamforming, multi-hop relaying, and tunable intelligent surfaces to efficiently direct and steer THz beams.
• Limited communication range: Due to the high attenuation of THz signals, long-distance communication is difficult. This limits their utility in scenarios requiring extensive coverage, such as rural and remote areas. Hybrid network structures combining THz communication with established mmWave and sub-6 GHz technologies have the potential to provide extended coverage and continue to support high-speed links in saturated-situated areas.
• Complex hardware requirements: THz signal generation, modulation, and detection are possible with special-purpose tools, such as photonic and electronic transceivers. These devices need to be highly accurate and efficient simultaneously, which adds complexity and expense. Continuous advancements in semiconductor materials (e.g., graphene and indium phosphide) and the development of compact, energy-efficient transceivers are essential to address this limitation.
• Power consumption and energy efficiency: Due to the high-power demands of THz production and amplification, energy consumption can be quite large. This particularly concerns mobile and battery-powered devices. Developing energy-efficient THz devices and low-power modulation is of the utmost importance to make THz communications feasible.
• Interference management: Large-scale deployment of THz communication nodes in 6G networks may cause interference, especially in urban scenes. High-performance interference mitigation, such as AI-assisted network control and dynamic spectrum assignment, is required to guarantee effective communication.
• Integration with 6G ecosystem: The ubiquitous integration of THz communication with other 6G technologies, including AI, QC, and massive IoT, presents technical and standardization issues. International standardization bodies, research establishments, and industry partners must create a common set of frameworks that allow THz to be introduced uniformly.

To address these challenges, some current research and development work has been directed toward innovations in THz devices, adaptive beamforming algorithms, and a framework for cooperation in communication. Furthermore, the integration of THz communication with other means, such as optical wireless communication and satellite systems, can overcome such limitations and make light of its potential. By addressing these obstacles, THz communications can become a critical enabler of the high-speed, ultra-reliable, and low-latency capabilities envisioned for 6G networks.

6.5. Multi-Hop Relaying in THz Networks

Multi-hop relaying has been proposed as a practical solution to mitigate the challenges posed by path loss and absorption. This method sends data from a source to a destination through one or more sequentially connected relay nodes. Each relay unit reduces the level of signal attenuation, processes and forwards the signal, and thus guarantees complete terminal connectivity. This distance reduction helps cover a lower transmission distance, which lowers overall path loss and absorption. THz networks have three common relaying methods: amplify-and-forward (AF), decode-and-forward (DF), and hybrid relaying. In AF, the relay accepts the information signal, amplifies it, and sends it to the next node [182]. Although the operation is simple, AF can also amplify noise, which can lead to a loss in quality. While in DF, the signal is decoded, processed, and then sent out as a clean signal at the second node. This ensures better quality signals, but a processing delay occurs. Hybrid relaying employs a mix of AF and DF that implements the most efficient method dynamically depending on the state of the channels.

• Multi-hop relaying offers several benefits for THz communications, including the following [182,183].
• Reducing path loss effects by breaking the communication link into shorter segments; each transmission experiences less attenuation, which enhances overall signal strength.
• Overcoming molecular absorption peaks since relays can be strategically positioned to help signals bypass high absorption frequencies, thus selecting the best THz windows.
• Multi-hop relaying can greatly increase the communication range, making THz technology more applicable in real-world scenarios.
• Shortening the distance between nodes improves signal reception, which lowers bit error rates and boosts overall link robustness.

The placement of relay nodes must be optimized to ensure the effectiveness of multi-hop relaying. Key factors that influence placement include distance between relays, environmental factors, and energy constraints. Shorter distances can reduce path loss but may require more relays, raising deployment costs and complexity. Also, relays should be located in areas with minimal obstructions and lower atmospheric absorption. THz communication demands high power, making it essential to use energy-efficient relays or integrate them with energy-harvesting technologies. Furthermore, advanced routing algorithms and beamforming techniques can further improve the efficiency of multi-hop relaying in THz networks [119]. AI-driven algorithms can adaptively modify relay positions and routes based on real-time network conditions. Furthermore, highly directional antennas can concentrate energy in specific directions, reducing interference and enhancing transmission efficiency. Another effective strategy for making THz communication feasible is to combine it with mmWave systems. A hybrid model that utilizes THz for ultra-high-speed, short-range connections while mmWave manages longer distances can establish a more practical framework for wireless communication.

7. Intelligent Reflection Surface

Current wireless technologies struggle to meet the demands posed by upcoming technological advancements such as AI-powered communication, IoT, VR, autonomous vehicles, connected health, and smart cities. To support data-intensive applications, future wireless systems, including 5G and beyond, must significantly enhance their capacity. The evolution of 5G and its successors necessitates fundamental shifts in communication methodologies to address requirements like high-speed data transfer, increased device density, and the integration of multiple antennas at BSs [60]. The varied necessities of IoT and MTC call for reduced latency, lower power consumption, and the provision of diverse service types. Fifth-generation does not rely on a single technology to sustain all its applications, and the rapid proliferation of mobile IoT devices presents a challenge in efficiently meeting their demands. Hence, efforts focused on optimizing energy and spectral efficiency become critical for improved data rates and enhanced UE quality.

At present, wireless communication functions within sub-6 GHz bands. To fully exploit the capabilities of 5G and beyond, the ITU has sanctioned frequencies in the millimeter wave band (10 GHz to 70 GHz) [61]. This change is expected to increase bandwidth by a hundredfold. Previously, these frequencies were deemed inadequate for mobile communications due to obstacles like path loss, atmospheric disruption, and high expenses. However, progress in semiconductor technology has alleviated these constraints, enabling improved transmission despite obstacles such as object obstruction and human interference. Specific frequencies in this range encounter absorption issues from gases (e.g., oxygen) at 60 GHz [60]. Unforeseeable radio environments impede wireless network optimization, resulting in signal problems like reflection and fading, limiting the efficiency of wireless networks. Implementing large antenna arrays with beamforming techniques, particularly in mmWave bands, helps address these hurdles by directing energy more efficiently. Despite advancements, coverage from outdoors to indoors in mmWave bands remains limited, and signal attenuation intensifies with higher frequencies, especially during rain.

IRS is a cutting-edge technology that shows promise in mitigating wireless channel propagation challenges. Recent advancements in metamaterials have propelled IRS into the spotlight within academia and industry. The terminology related to IRS includes various terms such as RIS, passive intelligent surface (PIS), software-defined metasurface (SDMS), large intelligent metasurface (LIMS), large intelligent surface (LIS), smart reflect arrays, and passive holographic MIMO (HMIMO). These terms essentially refer to the same technology [184]. IRS is a created surface made of electromagnetic material called a metasurface. It is a two-dimensional structure composed of many passive elements that scatter signals. Each scattering element has a unique physical structure. Through software control, these scattering elements can be manipulated to change the electromagnetic properties. By collectively controlling the phase of all the scattering components, the phases and angles of the incoming RF signals can be adjusted to create a beneficial multipath effect. This means that the reflected RF signals can be combined in a way that increases the power of the received signal, or they can be combined in a way that reduces interference.

IRS is a multi-layered structure designed to enhance wireless communication. It consists of three layers: an exterior interaction layer, a signal integrity layer, and a control and phase adjustment layer. The exterior interaction layer is the outermost part of the IRS and can comprise single or multiple layers. It contains reflective elements embedded in a dielectric material, which interact with incoming electromagnetic signals. The signal integrity layer in the middle is made of a copper sheet that acts as a shield to prevent signal or energy leakage. It ensures that the signal remains intact and directed without any loss. At the core of the IRS, the control and phase adjustment layer is a circuit board responsible for precise adjustments of the reflective elements’ phase. An intelligent controller, such as an FPGA, is used to modify the phase of the components based on received signals. This level of control allows the IRS to effectively manipulate the propagation of the signal [185].

Integrating IRS into wireless communication systems offers a wide range of benefits, transforming the landscape of modern networks. One of the primary advantages is the ease of deployment and sustainable operation. IRS devices, due to their small form factor, being passive and modular, can be easily deployed in a variety of environments (urban, suburban, remote) without the need for extensive infrastructure changes. This renders them a potentially useful option for the development of green communication systems to reduce environmental footprint. In addition, IRS technologies offer unprecedented degrees of freedom to restructure the wireless propagation environment by means of passive beamforming. Through freely altering the phase shifts in the received signals, IRS is able to intelligently steer the direction and quality of wireless links, improving the robustness and reconfigurability of the wireless network to varying user needs. This ability increases system-wide efficiency and thus supports the utilization of existing spectrum; thereby, IRS serves as a major building block for energy- and spectral-efficient communications systems.

Along with these main advantages, IRS-based systems dramatically decrease power consumption and improve network performance by effectively utilizing available power, making them an important aspect of the emergence of sustainable and energy-efficient wireless networks. That decrease in power consumption not only reduces operational costs but also fits in with global initiatives to become carbon neutral. Additionally, the IRS’s special characteristics have also drawn some new research avenues in wireless communication. These include the development of state-of-the-art applications (such as wireless power transfer, secure communication, and uRLLC). The IRS is in a leading position to investigate new technologies such as holographic communications, autonomous networks, and intelligent roadways.

7.1. IRS-Assisted 6G Communication System

IRSs have been developed to address the diverse and demanding requirements of upcoming 6G wireless communication scenarios. In immersive communication, these surfaces provide state-of-the-art solutions for long-reality (XR) and ultra-high-definition (UHD) visual streaming. Due to the high-beamforming gain and the introduction of additional propagation paths, IRSs can effectively compensate for path loss and overcome physical constraints, leading to a clutter-free user experience for latency-sensitive and bandwidth-heavy applications. IRSs have proven very relevant for critical applications in hyper-reliable and low-latency communication applications, from industry automation to telemedicine to emergency response services. Developing dominant LOS paths and filtering out unwanted multipath fading improve channel quality and transmission robustness. Reliability and low latency are paramount in highly dynamic environments, such as fully autonomous driving. IRSs can be strategically deployed by the roadside to amplify signal strength for vehicles and passengers or mounted directly on vehicles to counteract rapid channel fading caused by high mobility. This versatility renders IRSs the key component for developing robust vehicular communication [10].

Besides these applications, the IRS plays a critical role in mMTC for smart cities, environmental monitoring systems, and the IoT. Through intelligently designed reflections and the use of state-of-the-art multiple access schemes, such as non-orthogonal multiple access (NOMA), the IRSs contribute to higher system throughput and fair resource allocation between a large number of densely deployed IoT devices [184]. Refraction-based IRSs also provide signal-extending capabilities, allowing more device connectivity and, thus, ubiquitous connectivity in 6G networks. The IRS technology also presents new horizons in three emerging 6G scenes, which expand beyond their existing applications. Large-aperture IRSs enable high data rates and QoS, which in turn cater to emerging communication and computing applications such as distributed AI, edge computing, and digital twins. IRSs with AI-based optimized approaches, e.g., deep learning algorithms, enhance performance compared to the classic techniques used for beamforming, user association, and channel estimation.

To achieve ubiquity, IRSs can be mounted on mobile platforms, such as UAVs, high-altitude balloons, or satellites, providing coverage to remote and less-served areas. This deployment strategy is useful for maintaining a reliable and uninterrupted communication link in complex environments, e.g., rural areas, maritime areas, or disaster zones. In cell-edge conditions, IRSs suppress intra-cell interference and enhance signal-to-noise ratio in low-network-coverage regions. This is particularly useful in THz and mmWave communication bands, where signal jams and path loss are major problems. With the deployment of the IRSs at the cell edges or BS locations, the networks can decrease path loss and maintain good connectivity across more extensive regions.

IRS deployments are immensely flexible and can be configured according to application/environment requirements. In indoor settings, IRSs can be strategically placed on walls, ceilings, or furniture to enhance signal coverage and network capacity. In the case of multi-platform outdoor scenarios, IRSs can be mounted on high-speed cars, UAVs, satellites, or building surfaces to fully exploit the high spectral efficiency and reliable communication capability. At the BS end, in some cases, IRSs can be combined to maximize the beamforming and reduce the effect of distance-dependent path loss [186]. The deployment strategy of IRSs depends on various important issues, such as channel conditions, network coverage requirements, passive beamforming ability, and signaling overhead. By leveraging these factors, IRS technology can optimize wireless communication networks to meet the stringent requirements of 6G, including ultra-reliability, high data rates, low latency, and massive connectivity. As a result, IRSs are poised to play a transformative role in shaping the future of wireless communication systems.

7.2. Challenges with IRS

One of the main challenges in implementing IRS-assisted 6G networks is the development of adjustable elements. Continuously modifying the reflection coefficients of each element can significantly enhance network performance. However, this approach is often cost-prohibitive due to the intricate design and expensive hardware required for the large, high-precision components. A further difficulty in implementing IRS-assisted 6G networks is the creation of a productive control mechanism for connecting and communicating with the numerous tunable elements, allowing for flexible and coordinated manipulation of their EM properties when required. Despite numerous proposed control mechanisms thus far, the problem still persists [184]. IRS without radio frequency chains cannot perform baseband processing. As a result, traditional training-based methods cannot separately estimate the channels between the transmitter and IRS, as well as the IRS and receiver.

Several studies have proposed alternative methods to estimate these channels more efficiently. However, the sparsity and other properties of IRS-reflected channels in real-world environments remain largely unknown, indicating a need for a comprehensive channel estimation framework. Moreover, the close relationship between transmit power and reflection coefficients complicates the optimization of channel assignment, power allocation, and reflection coefficients in IRS systems, making efficient resource allocation a significant challenge [185]. The current research on how the IRS can provide widespread connectivity is still in the early stages of theory. To prove the practical efficiency of this technology, more prototypes must be created to demonstrate how it works. The development of 6G holographic communication with the help of the IRS also has key issues that need to be resolved, including 3D holographic imaging and the mechanism for reconstructing EM waves. Additionally, installing IRS on buildings in urban areas involves coordinating with various units and property managers, leading to potential conflicts of interest between network operators and equipment providers. These factors present significant challenges for the commercialization of this technology. Summing up,

Table 25. Summary of recent studies on IRS and UAV as key enabling technologies of 6G.

Ref.	Topic	IRS Installation	Optimization Variable	Optimization Algorithms	Objectives
[187]	IRS-assisted MEC-enabled UAV system in THz 6G networks	Buildings	Phase shift Computation resource allocation THz sub-band allocation	Hungarian whale optimization	Minimizing the overall network latency.
[188]	Multi-IRS and multi-UAV-assisted MEC system for 5G/6G networks	Buildings	Number of UAVs Phase shift Stop point (SP) Association optimization among UEs	TPaPBA Designing POP1, POP2, and POP3 DBC Ignore redundant SPs Greedy	Reduce the overall cost, including energy consumption, completion time, and maintenance cost of UAVs.
[176]	FL network architecture via AirComp in IRS-assisted UAV communications	UAVs	Transmission power UAV trajectory	SCA Iterative	Achieves high-quality and ubiquitous network coverage under data privacy and low latency requirements.
[189]	Joint UAV trajectory, frequency association, and power optimization	N/A	UAV trajectory Frequency association Transmission power	PPO Alternating optimization	Minimize the sum delay in the terahertz band.
[190]	THz-enabled UAVs to facilitate ubiquitous 6G mobile communication networks	N/A	UAVs location Transmit power UAV trajectory	BKMC SCA PPO-DRL	Maximize overall throughput between the UAVs and the GU and minimize the transmitting power of the UAVs.
[191]	IRS assisted UAV framework to provide stable communication services for high-speed trains	UAVs	UAV trajectory IRS phase shift	Optimal strategy for UAV with IRS	Maximize the minimum achievable data rates of HSTs.
[192]	IRS phase shift design and UAV trajectory optimization of IRS-empowered UAV communication network.	Buildings	IRS phase shift Transmit power Initial location and destination of the UAV	BCD Prox-linear IRM-based Enhanced RL	Maximize the sum rate of all users and improve system gains.
[193]	IRS-assisted secure UAV wireless networks	Buildings	UAV trajectory Transmit beamforming Phase shift	Iterative	To guarantee secure communication between the UAV and the legitimate user.
[194]	Joint optimization of aerial-IRS trajectory and passive beamforming via IRS phase shifts	UAVs	UAV trajectory Passive beamforming through phase shift	DDPG	Maximize the transmission rate and energy efficiency.
[195]	An optimal trajectory for UAVs-IRS algorithm assisted THz wireless network	UAVs	UAVs-IRS trajectory	DQN Heuristic	Minimize mission completion time for UAVs-IRS, maximize users’ average throughput data rate, and minimize total energy consumption under various practical constraints such as flying time, hovering time, and UAVs-IRS coverage area.
[196]	Flying IRS algorithm named a (Fly-IRS) aided-THz wireless network	UAVs	IRS phase shift UAV’s location	DDPG	Optimize the system data rate and improve the system’s performance.
[197]	FL in RIS-assisted UAV-enabled networks	Buildings	UAV’s trajectory FL aggregation weights	DDPG	Maximizing the SNR.
[198]	Integrating IRS and FL within drones	UAVs	UAVs trajectory Data throughput IRS phase	Processing flowchart	Offering a compelling avenue for enhancing 6G communication network performance.
[199]	A novel multi-IRS-aided multi-stream DM network	UAVs	Power on IRS (WMMSE-PC) PSMs of all IRSs	MM WMMSE-PC	Achieve a point-to-point multi-stream.

summarizes recent studies on IRS and UAV as key enabling technologies of 6G.

7.3. Real-World Implementation Constraints of IRS Phase Control

IRS comprises numerous reconfigurable passive elements, usually built from meta-materials, which can alter the phase and amplitude of incoming electromagnetic waves. The overall phase shift introduced by an IRS element can be expressed as follows.

$${\theta }_i=e^{j{\varnothing }_i}, { \varnothing }_i\in \left[\mathrm{0,2}\pi \right],$$

(2)

where θ_i is the complex reflection coefficient of the ith IRS element, and ϕ_i is the applied phase shift. However, in practical applications, the phase shifts are limited by hardware constraints, resulting in quantized phase shift models. Rather than allowing for continuous phase shifts, practical IRS implementations can only accommodate a limited number of discrete phase levels due to hardware limitations [184]. The number of quantization bits (b) determines the number of phase levels as follows: N = 2^b. Thus, increasing the number of quantization bits enhances IRS performance but also raises hardware complexity and power consumption.

Despite its theoretical benefits, IRS technology faces several real-world implementation challenges, including hardware limitations, control latency and power consumption, environmental factors, and channel estimation and optimization complexity.

(a)

Hardware limitations

Several hardware limitations affect the performance of the IRS, including the following [184,185].

• Most IRS hardware can only manage a small number of discrete phase shift levels (for instance, 2-bit or 3-bit phase control), which can reduce performance.
• Real-world IRS elements often introduce amplitude attenuation alongside phase modulation, which diminishes overall efficiency.
• Variations in manufacturing processes can lead to deviations from the ideal phase shifts, impacting the effectiveness of beamforming.

(b)

Control latency and power consumption

Switching between different phase states in diodes or MEMS-based IRS elements creates delays, which can limit adaptability in rapidly changing wireless environments. Although IRS is primarily passive, active components like controllers and tuning circuits require power, particularly in designs with high-bit quantization.

(c)

Environmental factors

The performance of phase-shifting components can fluctuate with temperature changes, resulting in phase drifting over time. Furthermore, IRS systems depend on LoS or near-line-of-sight paths; obstacles such as buildings and moving people can negatively impact performance.

(d)

Complexity in channel estimation and optimization

As the IRS does not have active transmission capabilities, accurately estimating CSI can be quite challenging. Also, the IRS is limited by scalability issues. Optimizing phase shifts for large-scale IRS deployments that involve thousands of elements demands considerable computational resources [185].

Table 26. Comparison of IRS phase control technologies.

Technology	Phase Control Method	Advantages	Challenges
PIN diodes	Discrete phase tuning	Fast switching Low cost	High power consumption Limited resolution
Varactor diodes	Continuous phase tuning	Fine control Low power	Nonlinear response Temperature sensitive
MEMS switches	Discrete phase tuning	Low loss Compact size	Slow switching speed Complex fabrication
Liquid crystal	Continuous phase tuning	High resolution Low power	Slow response time Temperature sensitive
Graphene-based	Continuous phase tuning	High-speed tuning Compact design	Emerging technology High fabrication cost

compares the key technologies for IRS phase control. Several strategies can help address phase control limitations in practical IRS implementations. Deploying a hybrid active–passive IRS can provide a key solution. This can be achieved by adding a few active components to an IRS array, which can enhance flexibility in phase control. Creating new materials with better tunability and lower losses is another solution that can improve IRS performance. Furthermore, innovative algorithms, e.g., compressive sensing, can minimize the burden of channel estimation. Also, AI-driven methods can assist in optimizing discrete phase settings in real-world scenarios.

8. Swarm Drones

Swarm drones, which involve the coordinated operation of multiple UAVs are emerging as a critical technology for 6G networks, playing a pivotal role in enabling ultra-reliable, low-latency communication, and intelligent network management. While a single UAV is effective for small-scale missions, its limitations in sensing capabilities, and potential as a single point of failure highlight the advantages of using UAV swarms. Swarms offer enhanced intelligence, improved coordination, increased flexibility, survivability, and reconfigurability, making them more suitable for large-scale missions. In a swarm, UAVs operate autonomously, communicating and making decisions based on information from neighboring drones. This complex system requires the integration of several subsystems, including optimal trajectory planning, localization, and task coordination, to ensure seamless operation and achieve mission objectives efficiently [200]. As 6G networks evolve, swarm drones are expected to become indispensable, driving the future of wireless communication and network infrastructure through their transformative capabilities.

Two primary strategies are employed in managing UAV swarm formations: centralized and distributed control. Centralized control involves a single authoritative entity, such as a ground station or a powerful UAV. The swarm control component acts as the intelligence or brain of the swarm. It collects information from all agents in the field and determines the direction the system should take and the formation it should adopt [200]. This approach offers optimized performance due to its global perspective but is vulnerable to failure if the central controller is compromised. Within centralized control, the leader–follower method is commonly used, where one UAV leads while others maintain specific distances. This can be static, with a consistent leader, or dynamic, where leadership changes based on the situation. While effective, it risks mission failure if the leader is lost and may suffer from communication overhead. Conversely, distributed control eliminates the need for a central authority. Each UAV optimizes control commands for itself and its nearest neighbor, but it executes its own controls independently at each time step [201]. This strategy enhances resilience and adaptability in dynamic environments but presents challenges in implementation.

Effective communication is vital for the success of swarm drone networks, involving both intra-swarm (i.e., drone-to-drone) and inter-swarm (i.e., drone-to-ground control) interactions. Various communication topologies (e.g., ring, star, mesh, and fully connected) are used, each with unique strengths and weaknesses. For example, ring and star topologies are simpler but prone to single points of failure, while mesh and fully connected topologies provide greater resilience. Routing protocols are critical for efficient data transmission in dynamic environments. Conventional protocols (e.g., static, proactive, reactive, and hybrid) are designed for static networks and often fall short in UAV applications. Modified protocols (e.g., LCAD routing and ML-OLSR) address these limitations by incorporating mobility and load factors. Position-based routing, such as GPSR, relies on geographical data but struggles with environmental obstacles. Intelligence-based routing, leveraging AI, offers adaptive solutions but demands significant computational resources [202].

8.1. Swarm Drones as a Key Technology of 6G

A significant challenge in managing a swarm of drones is ensuring effective guidance—coordinating the movements of multiple drones to achieve a specific objective. Swarm systems often include mechanisms to prevent collisions with other agents and obstacles while navigating toward a target or maintaining a designated group formation [202]. Recently, bio-inspired algorithms have gained prominence as a promising approach for controlling drone swarms. These algorithms draw inspiration from the collective behaviors observed in social animals, such as insects, fish, and bird flocks. Their advantage lies in their ability to adapt rapidly to changing environments and self-organize into complex patterns.

Furthermore, RL integration enables UAV swarms to optimize behavior by using surrounding information to determine appropriate actions. Each UAV learns to recognize the situation required to achieve the swarm’s goal and takes the most optimal action. By leveraging the efficient self-UAV swarm network (ESUSN) and multi-agent RL (MARL), this approach improves mission performance, communication efficiency, and collision avoidance, combining RL and optimized network algorithms to enhance overall swarm operations [200]. Swarm drones can be deployed for 6G networks to achieve the following.

(a)

Enhanced connectivity and coverage

Swarm drones act as mobile base stations, extending network coverage in areas where traditional infrastructure is unavailable or inefficient. This is particularly beneficial in remote, disaster-struck, or densely populated urban regions. Swarm drones offer dynamic coverage control, which involves designing strategies for mobile sensing agents equipped with limited-range sensors to explore a specific area and ensure that every point within the domain achieves a predefined level of coverage. They enable LOS communication in the sub-THz band, which is a cornerstone of 6G for high-speed data transmission.

(b)

Dynamic disaster monitoring

In disaster scenarios, single-UAV systems often encounter challenges like unstable communication links and insufficient coverage of large, complex areas. Swarm drone systems provide a more effective alternative by utilizing collaborative networks to improve redundancy, reliability, and scalability. These systems implement distributed architectures, including multi-star, mesh, and hierarchical mesh topologies, allowing UAVs to communicate, exchange information, and adjust strategies in real-time. Mesh networks stand out due to their self-forming and self-healing properties, ensuring high scalability and adaptability in dynamic disaster conditions. Hierarchical and clustered mesh networks enhance coordination by structuring UAVs into layers or clusters, with specific leaders managing communication within and between clusters. Supported by 6G connectivity, these systems enable efficient disaster response through enhanced monitoring, search and rescue operations, and real-time decision-making, ensuring a robust and flexible approach to emergency management.

(c)

Transformative applications

Swarm drones have transformative potential across various sectors, including civilian and military domains.

• Civilian applications: Swarm drones are poised to revolutionize industries such as agriculture through precision farming and logistics via efficient package delivery and infrastructure inspection. In public safety, they are invaluable for search and rescue operations, especially in disaster zones where they can provide real-time image and video data. Additionally, drones are increasingly utilized for wireless connectivity and traffic management, further expanding their scope in civilian life.
• Military applications: In military contexts, swarm drones are instrumental in reducing risks to human pilots, as they can effectively handle surveillance, reconnaissance, and battlefield communication tasks. They also support advanced defense strategies, such as swarm-based systems, that enhance operational effectiveness.

(d)

Energy efficiency

Integrating small UAVs into 6G networks presents significant challenges due to their limited onboard resources, including battery life, storage capacity, and computational capabilities. Given the impracticality of mid-flight battery replacements, effectively managing these resources to meet the demands of resource-intensive applications is crucial. Furthermore, the substantial data collected by these UAVs during monitoring tasks may exceed their processing and storage capacities, highlighting the need for efficient data management strategies. A key focus in UAV network design is energy efficiency, as energy leaks during communication can degrade network throughput. Traditional configurations, where a UAV transmits at a fixed power level to all nearby UAVs, ensure stability but are inefficient, leading to unnecessary energy consumption. To address this, researchers are exploring strategies like dynamic power adjustment and adaptive communication protocols to reduce energy waste while maintaining performance.

UAV networks can be managed through centralized or distributed approaches. In a centralized system, a global controller manages transmission power, reducing routing overhead and power consumption but potentially compromising stability due to limited routing options. In contrast, a distributed topology allows each UAV to independently regulate its power, optimizing energy use and ensuring robustness.

8.2. Integration with 6G Technologies

A.

Terahertz communication

Sixth-generation technology necessitates advanced applications to manage massive data volumes, delivering extremely high throughput per device (ranging from Gbps to Tbps) and exceptional area efficiency (measured in bps/km²). To meet these requirements, THz technology is anticipated to bridge the gap between mmWave and optical communication bands. The IEEE 802.15.3d-2017 standard, established in 2017, marked a milestone as the first wireless communication standard operating at carrier frequencies near 300 GHz. Furthermore, the growing number of mobile and fixed users across private, industrial, and service sectors will drive the demand for communication speeds reaching hundreds of Gbps for both backhaul and fronthaul. Achieving ultra-high data rates (targeting 1 Tbps) over distances of several kilometers will be a critical requirement in such scenarios [11].

Swarm drones can leverage the THz spectrum’s high bandwidth for ultra-fast data transmission, particularly at altitudes above 16 km, where environmental moisture has minimal impact. THz technology is well suited for high-altitude platforms (HAPs) and high-mobility UAV environments, as its high-frequency signals are less affected by the Doppler effect. By optimizing beam patterns, THz communications enable high-speed links. At the same time, THz MIMO-OFDM systems can achieve millimeter-level accuracy in UAV position and orientation estimation at close transmitter-receiver distances. For secure operations, the narrow beams of THz signals reduce eavesdropping risks, and the extensive bandwidth offers resilience against jamming and other attacks. Additionally, THz links can facilitate communication between UAVs and airplanes and support HAPs as relay nodes connecting ground stations to aircraft, ensuring reliable and secure short-range and long-range data exchange.

B.

AI and ML

AI algorithms play a pivotal role in enabling swarm drones to autonomously navigate, communicate, and collaborate, making them integral to intelligent 6G networks. By mimicking human intelligence, AI enhances UAV swarm systems’ ability to operate effectively in dynamic and complex environments, facilitating real-time decision-making and autonomous operations. ML, a key subset of AI, empowers UAVs to learn from their surroundings and adapt over time. Techniques such as RL, neural networks, clustering, and support vector machines are crucial for optimizing UAV trajectories, coordination, and efficiency. These methods enable precise path planning, real-time navigation, and obstacle avoidance, ensuring drones can adapt to unforeseen challenges [203].

AI/ML further enhances collaborative sensing by improving data acquisition and processing, supporting efficient task distribution among drones, and maximizing resource utilization. Recent advancements demonstrate how AI/ML techniques drive innovation in UAV swarm systems, fostering significant improvements across diverse applications, from disaster management to surveillance and beyond.

C.

Integrated sensing and communication (ISAC)

To improve efficiency and lower costs in 6G, ISAC is being merged with drone swarms. Unlike conventional systems that treat sensing and communication separately, ISAC combines these functions, allowing communication signals to also serve sensing needs. This integration is especially important for applications like autonomous vehicle positioning, which is a fundamental component of the advancement of 6G [204].

D.

Blockchain

Integrating blockchain technology with 6G communication in UAV networks significantly enhances cybersecurity and performance. Blockchain’s core features, immutability, decentralization, and transparency provide critical benefits for UAV operations on 5G and 6G networks. Immutability ensures that data remain unaltered through irreversible hash functions, offering a secure and reliable method for storing and exchanging large data volumes. Decentralization eliminates reliance on central authorities by using consensus algorithms (e.g., PoW), enhancing security, reducing single points of failure, and enabling a robust, low-latency database platform. Transparency fosters trust by allowing network participants to view and validate transaction records, improving node cooperation and ensuring data integrity.

These blockchain properties further enhance UAV-6G communication networks by enabling secure spectrum sharing between network operators and UAV service providers, ensuring privacy through distributed information sharing, and mitigating risks posed by malicious nodes. In addition, blockchain-enabled 6G UAV networks can leverage edge computing platforms and core networks to bolster security and adaptability.

E.

Edge computing

Swarm drones are becoming essential components of MEC networks, particularly in enhancing the capabilities of 6G systems. These UAVs can function as backup MEC nodes in scenarios where traditional base stations are damaged or inaccessible, such as during natural disasters. Controlled by ground stations, drones leverage dynamic trajectories to optimize computation offloading and caching services, making them highly effective for critical applications in crowded areas and complex terrains like deserts, oceans, and wilderness. MEC integration with UAVs focuses on two primary strategies: path planning for computation offloading and resource allocation. Path planning strategies optimize UAV trajectories to minimize task latency and energy consumption, ensuring efficient operations in dynamic environments. Resource allocation strategies, on the other hand, focus on minimizing latency by effectively distributing communication and computing resources to connected devices. While resource allocation works well in static environments, it faces challenges in dynamic settings with high user mobility.

By integrating MEC platforms, UAVs bring cloud services closer to end users, reducing latency for compute-intensive tasks such as augmented reality and real-time analytics. This capability is especially critical during emergencies when traditional infrastructure is unavailable. The advancements position UAV-assisted MEC as a transformative solution for disaster response, resource management, and real-time decision-making in next-generation communication systems.

8.3. Handover Management for Swarm Drones

Handover management is essential for maintaining seamless communication and coordination in swarm drone networks, especially as drones transition between coverage areas or communication nodes, such as base stations, satellites, or other drones. The dynamic nature of drone flight and the need for continuous connectivity and scalability in large-scale operations make effective handovers critical to ensuring uninterrupted task execution, minimal latency, and reliable swarm performance. A handover, or handoff, is a core mobility technique in mobile networks, enabling UE to maintain connectivity while switching between BSs. In drone networks, this process faces additional complexity due to the rapid and dynamic movement of aerial vehicles, high altitudes, and differing operational requirements compared to ground-based users. Drones may serve dual roles: providing network access to terrestrial users or acting as UEs themselves, receiving services from BSs or satellites.

The performance of handover management in drone networks is influenced by factors such as drone mobility, limited power, coverage constraints, and network traffic demands. Traditional handover techniques, such as those used in MANETs and VANETs, are insufficient for drone networks and require adaptation. In MANETs, frequent node separation and merging pose challenges, while drone networks require solutions tailored to their high mobility and dynamic radio environments. The handover procedure in 6G is a critical advancement designed to ensure seamless connectivity for the UE as it transitions between cells or communication nodes. Building on 5G principles, 6G introduces advanced mechanisms to address challenges like ultra-high mobility, ultra-dense networks, and the integration of terrestrial and non-terrestrial systems (e.g., satellites, drones, and high-altitude platforms). These innovations are essential to meet the demands of next-generation networks, which require dynamic, low latency, and highly reliable communication. The 6G handover process involves the following key steps [205].

• Measurement report: The UE continuously monitors network metrics (e.g., signal strength, latency, interference) and sends detailed reports to the serving BS (S-BS). AI models predict handover needs based on UE mobility and environmental changes.
• Handover decision: The S-BS uses machine learning to evaluate the need for a handover to the target BS (T-BS), considering factors like network load, service quality, and UE trajectory.
• Handover request: The S-BS sends a request to the T-BS, including UE state, resource requirements, and movement predictions.
• Acknowledgment: The T-BS assesses its resources using AI-driven allocation and confirms its ability to accommodate the UE, ensuring optimal load distribution.
• Handover initiation: The T-BS provides the UE with configuration details (e.g., frequency band, timing adjustments) to establish a connection.
• Uplink allocation: The T-BS allocates uplink resources and sends synchronization information to the UE for uninterrupted communication.
• Path update: The T-BS updates core network functions (e.g., NG-AMF) and adjusts the data routing path via the user plane function (UPF). AI and edge computing minimize latency during this process.
• Completion notification: The T-BS notifies the S-BS of the handover’s completion, and the S-BS releases the UE’s resources, ensuring a seamless transition.

8.4. Challenges with Swarm Drones

Swarm drones in 6G networks face several critical challenges that demand innovative solutions for efficient and reliable performance. High mobility and dynamic environments, such as disaster zones, make maintaining stable, low-latency communication difficult. Scalability further complicates operations, as managing large swarms requires advanced resource allocation and network coordination. Limited energy resources in drones exacerbate these challenges, as continuous communication, computation, and mobility drain battery life rapidly. Reliable real-time decision-making, essential for collective intelligence and instant data sharing, hinges on ultra-low latency and high reliability. Network resilience is another critical factor, particularly in disaster-stricken or remote areas lacking infrastructure, where drones must maintain autonomous operations and connectivity.

Security and privacy also pose significant concerns, requiring robust defenses against cyberattacks, data breaches, and privacy violations, especially in sensitive missions. Integrating swarm drones with non-terrestrial networks, like satellites, adds complexity, necessitating seamless handovers and coordination for uninterrupted operations. Moreover, the computational demands of advanced AI algorithms for swarm coordination, path optimization, and adaptive decision-making must align with the limited onboard resources of drones. Standardization and interoperability are crucial for widespread adoption, demanding unified protocols and compatibility across diverse drone models, communication technologies, and network architectures. Addressing these challenges will rely on advancements in 6G technologies, such as AI-driven network management, edge computing, and adaptive communication protocols, enabling efficient, secure, and scalable swarm drone operations in dynamic and critical environments.

9. Real-World Deployment and Standardization Efforts

Moving from 5G to 6G represents a major evolution in wireless communication, integrating cutting-edge technologies, including IRS, THz communications, and UAVs. These innovations are set to deliver improved network capacity, extremely low latency, and smarter automation. However, the successful implementation in real-world scenarios hinges on comprehensive standardization efforts by global organizations such as 3GPP, ITU, ETSI, and IEEE, which are essential for ensuring smooth integration with current infrastructure and adherence to regulations. International standardization groups and societies play a critical role in shaping the future of 6G. This section highlights standardization roadmaps, commercial opportunities, and regulatory frameworks that will shape the adoption of 6G.

Table 27. Standardization efforts for 6G technologies.

Organization	Key Standardization Efforts
3GPP [206]	6G architecture (Release 18 and beyond) UAV-based networking THz spectrum standardization IRS deployment guidelines
ITU [207]	IMT-2030 framework Spectrum allocation for THz UAV-assisted networks Network automation policies
ETSI [208]	Edge intelligence AI-driven network management IRS integration into Open RAN
IEEE	THz communication standards (IEEE 802.15.3d) IRS-enabled smart environments UAV connectivity protocols

provides an overview of the standardization priorities of key organizations. The section mainly focuses on the efforts of ITU and ETSI.

The 3GPP’s Release 18, often viewed as the initial phase of 5G-Advanced, lays the groundwork for 6G by introducing AI-native networks, expanded UAV communication capabilities, and IRS-enhanced radio environments. Subsequent releases (19 and beyond) are expected to further refine these technologies.

Table 28. The 3GPP standardization roadmap for 6G [206].

Release	Key Features
Release 18 (2024–2025)	AI-native 5G, UAV connectivity optimization, IRS-assisted wireless networks
Release 19 (2026–2027)	THz spectrum utilization, massive MIMO for IRS, real-time UAV-assisted networking
Release 20 (2028–2030)	Full 6G standardization, QC integration, holographic communications

provides the 3GPP standardization roadmap for 6G. The ITU is responsible for defining global policies and technical frameworks for wireless networks. The IMT-2030 framework highlights essential technology enablers for 6G, focusing on THz spectrum allocation (100 GHz–10 THz), UAV-assisted connectivity policies, and IRS as a smart radio environment enabler. Moreover, the IEEE is at the forefront of developing THz communications and integrating IRS. The IEEE 802.15.3d standard outlines the use of THz communication in wireless networks, while IRS applications are being investigated under the IEEE 802.11bf standard.

9.1. ITU Efforts

The ITU is essential in shaping global policies for wireless communication. As a specialized agency of the United Nations, the ITU establishes standards and regulatory frameworks that direct spectrum allocation, network interoperability, and the development of new technologies. For 6G networks, the ITU has introduced the IMT-2030 framework, which outlines the technical enablers and spectrum policies necessary for the next generation of wireless systems. This section delves into the ITU’s role, key policies, and the technology enablers of IMT-2030, focusing on THz spectrum allocation, UAV-assisted connectivity policies, and IRS as a smart radio environment (SRE) component.

The ITU regulates the IMT/2030 spectrum through the WRC. The WRC-23 and WRC-27 meetings are anticipated to finalize allocations for 6G, highlighting the THz spectrum for ultra-high-speed communication, LEO and NTN policies, and dynamic spectrum sharing between terrestrial and satellite networks [209]. The ITU works with the FCC, ETSI, and 3GPP to establish global THz spectrum allocation. The ITU identified THz communications as a key enabler for 6G/IMT2030 due to their ultra-wide bandwidth, which enables Tbps-level data rates and ultra-low latency. ITU study groups have identified the 100 GHz–10 THz range for 6G applications. This range will cover a wide range of applications, including holographic communications, high-resolution imaging, and quantum networking.

Table 29. ITU announced key applications for THz bands.

Frequency Band	Bandwidth	Key Applications
100 GHz–300 GHz	200 GHz	Ultra-high-speed data links
300 GHz–1 THz	700 GHz	Wireless backhaul, AI-driven connectivity
1 THz–10 THz	9 THz	Holographic and immersive communications

provides the ITU-announced key applications for the three main THz bands. Furthermore, common ITU-R recommendations for the THz spectrum are ITU-R M.2412/M.2516 and ITU-R SM.2352. ITU-R M.2412/M.2516 has been introduced to define the technical parameters for THz band deployment; however, ITU-R SM.2352 highlights spectrum sharing between THz and existing wireless technologies [207,209].

A. 

ITU-R M.2412/ M.2516

The ITU-R M.2412/ M.2516 recommendation outlines the technical specifications for utilizing the THz spectrum (100 GHz—10 THz) in future wireless communications, especially within 6G networks. This standard offers guidance on channel models, propagation characteristics, and system performance requirements to promote the effective use of THz frequencies. Implementing THz band communication is vital for achieving extremely high data rates (Tbps), very low latency (<1 ms), and enhanced spectral efficiency, making it a fundamental component for immersive applications, ITS, and smart environments. The ITU-R M.2412/ M.2516 recommendation aims to achieve the following [210,211].

• Standardize the use of the THz spectrum for 6G and future technologies.
• Define propagation models that consider the specific atmospheric absorption at THz frequencies.
• Create channel models suitable for both indoor and outdoor settings.
• Ensure that THz-based systems can work seamlessly with current wireless networks.
• Refine link budget calculations to enhance coverage and reliability.

ITU-R M.2412 defines propagation models for the THz spectrum using three main models: the LOS dominant model, NLOS model, and hybrid model for dynamic environments. THz waves suffer from significant attenuation due to molecular absorption, particularly water vapor and oxygen molecules. The report identified the atmospheric absorption peaks for THz bands. To mitigate this, ITU-R M.2412 suggested using frequency windows with minimal absorption, such as 275–320 GHz and 400–700 GHz, for optimal performance. ITU-R M.2412 also outlines deployment guidelines for maximizing THz communication efficiency. For reliable THz communication, link budget calculations must consider the following [210].

$$P_{Rx}=P_{Tx}+G_{Tx}+G_{Rx}-FSPL-{\gamma }_{atm}-{\gamma }_{other}$$

(3)

where P_Rx is the received power, P_Tx is the transmitted power, G_Tx is the gain of the transmitting antenna, G_Rx is the gain of the receiving antenna, γ_atm is the atmospheric absorption attenuation, and γ_other is the other losses, e.g., penetration losses, scattering, and hardware impairments. Given the high path loss, ITU-R M.2412 recommended using the following solutions for THz communications.

• Ultra-directional antennas (e.g., plasmonic antennas, leaky-wave antennas) for beamforming.
• Massive MIMO techniques to improve gain and efficiency.
• IRS to enhance signal propagation through passive beamforming.

ITU-R M.2412/ M.2516 defines four optimal deployment scenarios for THz networks.

Table 30. ITU-R M.2412 deployment scenarios for THz communications.

Deployment Scenario	Distance	Application
Indoor wireless	1–10 m	Wireless Terabit LANs Holographic communications
Short-range outdoor	10–100 m	6G small cells D2D
Backhaul links	100–1000 m	Ultra-fast fiber replacement
Satellite communications	>1 km	Space-based THz networking

introduces these four deployment scenarios and the main applications associated with each deployment scenario. Despite ITU-R M.2412/ M.2516’s efforts to standardize THz deployment, several challenges remain. This includes the following.

• The challenges associated with the efficient THz transceiver design. This includes efficient THz power amplifiers, oscillators, and graphene-based antennas are needed.
• THz circuits suffer from high power consumption and thermal instability.
• Security challenges associated with THz networks. Novel encryption techniques are required due to short-range eavesdropping risks.
• THz spectrum allocation and optimization remain challenges. AI/ML can be used to dynamically optimize spectrum usage in real-time.

B. 

ITU-R SM.2352

ITU-R SM.2352 offers important spectrum-sharing guidelines between THz-band and current wireless technologies, aiming for efficient coexistence without interference. As 6G networks incorporate THz communication for ultra-high-speed data transfer, it is vital to tackle potential conflicts with existing spectrum users, such as satellites, radar systems, and mmWave networks. The THz spectrum overlaps with various existing communication technologies, raising potential concerns about interference.

Table 31. Potential spectrum-sharing challenges in different frequency ranges.

Frequency Range	Existing Wireless Technologies	Potential Interference Issues
100 GHz–275 GHz	Satellite links Weather radar Earth exploration satellites (EESS)	High-power radar emissions may cause interference with THz systems.
275 GHz–400 GHz	6G backhaul Fixed services Scientific research	Spectrum congestion due to emerging THz backhaul applications.
400 GHz–1 THz	Deep-space communication IoT	Cross-band interference from IoT devices operating in sub-THz bands.
1 THz–10 THz	Optical wireless communication Ultrafast photonics	Optical-THz interference affecting ultra-high-speed data links.

provides the potential spectrum-sharing challenges in different frequency ranges. This recommendation outlines regulatory frameworks, interference mitigation techniques, and dynamic spectrum access strategies to facilitate the smooth deployment of THz communication alongside current services. The main objectives of the ITU-R SM.2352 recommendation are as follows [212].

• Establishing mechanisms for spectrum sharing between THz and existing wireless technologies.
• Developing techniques for managing interference to safeguard essential services like satellite communications, radar systems, and backhaul links.
• Enhancing spectrum utilization through dynamic spectrum access (DSA) and CR methods.
• Ensuring regulatory compliance for THz networks operating with 5G, Wi-Fi 7, and satellite networks.

THz signals overlapping with mmWave frequencies (30–100 GHz) may face spillover effects, disrupting satellite and radar operations. Another issue is that high-frequency signals are prone to strong reflections, resulting in interference in indoor settings and densely populated urban areas. Also, THz communication depends on highly directional beams, which can unintentionally interfere with nearby receivers.

To overcome such challenges and enable effective coexistence of THz networks with existing wireless systems, ITU-R SM.2352 outlines several spectrum-sharing mechanisms that include the following.

(1)

DSA and CR

CRN facilitates intelligent spectrum sensing to identify and avoid interference with current wireless services. Spectrum sensing techniques include energy detection, matched filtering, and cyclostationary feature detection approaches. Energy detection is used to detect occupied spectrum bands; however, matched filtering is deployed to ensure precise detection of licensed signals. Furthermore, cyclostationary feature detection can distinguish THz signals from noise and interference. Moreover, opportunistic spectrum sharing can permit THz transmitters to dynamically utilize the underused spectrum, alleviating congestion.

(2)

Interference mitigation techniques

Power control algorithms can dynamically adjust transmission power to minimize interference with neighboring bands. This is mainly considered for minimizing cross-band interference. However, beamforming can enhance signal directionality. Adaptive beamforming employs AI-driven smart antennas to direct THz beams away from sensitive receivers. Furthermore, guard band allocation is another approach that establishes buffer frequency ranges between THz and legacy systems to lessen spectral leakage and thus mitigate adjacent channel interference.

(3)

Spectrum reallocation strategies

ITU-R SM.2352 suggests reallocating parts of the THz band for specific applications while minimizing the impact on existing systems.

ITU-R SM.2352 recommends several regulatory and technical strategies to facilitate effective THz spectrum sharing. Also, it details deployment strategies to balance THz network performance with the coexistence of existing services. While ITU-R SM.2352 has made strides in outlining spectrum-sharing guidelines, additional research is necessary for developing AI-driven spectrum allocation, achieving enhanced secure spectrum access, improving energy-efficient THz communication, and integrating QC with THz networks

ITU identified three main scenarios for UAV-assisted connectivity: an aerial base station (ABS), relays for backhaul connectivity, and LEO-satellite interworking. UAVs can act as flying base stations for emergency and rural connectivity. This scenario can also be used for supporting dense deployment in 6G networks. The second scenario uses UAVs to connect remote IoT devices with terrestrial networks. In the third scenario, UAVs act as intermediaries between ground users and LEO satellite constellations.

9.2. ETSI Efforts

The ETSI is dedicated to advancing network intelligence and edge computing. Its projects aim to facilitate the real-world implementation of 6G technologies, which include IRS and connectivity through UAVs. ETSI launched the following three main working groups for 6G technologies.

• ETSI ENI ISG: AI-driven network automation.
• ETSI ZSM: Zero-touch network management.
• ETSI RRS: Reconfigurable radio systems for IRS-based networks.

(a) 

ENI ISG

The ENI ISG is leading the way in transforming network management by integrating AI. By developing a cognitive network management architecture, it seeks to improve network operations by adapting services in real-time to meet changing user needs, environmental factors, and business objectives. The main aim of ENI ISG is to revolutionize network management through the use of AI-driven, context-aware policies. This strategy allows for automated service provisioning, efficient operations, and strong assurance across various network types. The key objectives of this ESTI group are as follows [213].

• Using AI to automate service provisioning, operations, and assurance, which reduces the need for manual interventions and lowers operational costs.
• Applying AI techniques for dynamic slice management and resource orchestration to boost network efficiency.
• Implementing context-aware policies to modify services based on real-time shifts in user requirements, environmental conditions, and business goals.

ENI ISG is dedicated to enhancing the operator experience by utilizing closed-loop AI mechanisms based on context-aware, metadata-driven policies. This allows the ENI system to identify and integrate new and evolving knowledge, resulting in actionable decisions. The model offers recommendations to decision-making systems, such as network control, and interacts with management systems to adjust services and resources in response to changes in user needs, environmental conditions, and business objectives. The key activities of the group include the following [213].

• Implementing AI-based control loops that continuously monitor network performance and make real-time adjustments to optimize operations.
• Developing policies that take into account user context, environmental factors, and business objectives to guide network behavior.
• Ensuring the ENI system recognizes and incorporates new and updated knowledge, facilitating adaptive and intelligent decision-making.
• Offering actionable recommendations to network control and management systems to adjust services and resources in response to changing conditions.

The ENI ISG has released the first version of the system architecture with context-aware policy management, covering various aspects such as categorization on networks using AI, intent-aware network architecture, data mechanisms, evaluation of categorization, functional concepts, prominent control loop architectures, and AI mechanisms. Additionally, two versions of the PoC framework and three versions of the use cases, requirements, and terminology in Release 2 have been published. A second version of system architecture was recently released. ENI ISG has initiated PoCs to demonstrate how AI techniques can enhance network operations. These PoCs are designed to validate the practical use of ENI’s frameworks and gather insights for future standardization efforts [213]. The PoC framework, as outlined in ETSI GS ENI 006, specifies the objectives, scope, and evaluation criteria for PoCs. It acts as a guideline for industry stakeholders to create and evaluate AI-driven network management solutions. Several PoCs are currently in progress, each focusing on different aspects of AI integration into network management. The ENI Wiki has more information about ongoing PoCs.

The ENI system architecture features two control loops that utilize AI modeling. Data collected are transmitted through an optional API, normalized, and processed within various AI analysis functional blocks, which can be recursive and interactive through an inner loop. The actionable decisions are then de-normalized and sent back to the network via the same optional API in reverse. This architecture uses control-loop AI/ML mechanisms to enhance operator experiences by relying on context-aware and metadata-driven policies. This approach enables effective network management by adjusting services and resources according to user needs and environmental factors. Data collected from the network are normalized and processed through AI analysis functional blocks. These blocks can function recursively and interactively, creating an inner loop that sharpens decision-making processes. The resulting actionable decisions are then de-normalized and relayed to the network for execution.

(b) 

ZSM

Traditional management methods are becoming less effective in dealing with current and upcoming networks’ complexity, scale, and dynamic nature. In response to this challenge, the ETSI formed the ZSM ISG in December 2017. The main goal of the ZSM ISG is to create comprehensive end-to-end architecture and solutions for automated network and service management, aiming to minimize or completely eliminate the need for human intervention. The ZSM ISG focuses on several key challenges in modern network management that include the following [214].

• As networks transition to programmable, software-driven, and service-oriented architectures, their complexity grows. ZSM aims to tackle this complexity through automation, ensuring efficient operation and maintenance.
• Networks must exhibit exceptional operational agility to take advantage of new business opportunities through technological advancements like network slicing. ZSM seeks to enable this by allowing quick adjustments to evolving service requirements.
• The ultimate aim is to achieve complete end-to-end network and service management automation. This encompasses automated delivery, deployment, configuration, assurance, and optimization processes.
• ZSM envisions networks that can self-configure, self-monitor, self-heal, and self-optimize, guided by high-level policies and rules, thus reducing the need for human involvement.

The ZSM reference architecture aims to achieve automation and autonomy goals. It consists of several management domains (MDs), each tasked with overseeing a specific infrastructure segment, along with an end-to-end service management domain (E2E SMD) that coordinates the management services offered by individual MDs for comprehensive cross-domain management. Each management domain, including the E2E SMD, provides a range of management services delivered by the management functions within that domain. MDs are responsible for managing particular segments of the network or service infrastructure. Each MD functions independently while collaborating with other MDs to ensure unified management throughout the network. The E2E SMD orchestrates and manages services that extend across multiple MDs, guaranteeing smooth service delivery and performance across the entire network. Both MDs and the E2E SMD offer management services that enable interactions among various management functions. These services are designed for interoperability, promoting flexible and dynamic management processes [214].

The ZSM framework outlines a collection of management services organized according to their functionalities, which promote automation and interoperability throughout the network.

Table 32. Service categories in network management [214,215,216].

Service Category	Description	Service Implementation	Existing Solutions	Main Use Case
Data collection	Aggregates data from various network elements and services for analysis and decision-making.	Telemetry agents, sensors	Prometheus OpenTelemetry	Network monitoring
Analytics	Processes collected data to generate insights, detect anomalies, and predict future network behavior.	AI/ML models, big data analytics	Splunk IBM Watson	Predictive maintenance
Policy management	Defines and manages policies that govern network behavior.	Policy engines, intent-based networking	ONAP Cisco DNA	Automated network configuration
Decision-making	Utilizes analytics and policies for network decisions.	AI inference engines	Juniper AI-driven networks	Fault detection
Execution	Implements network configuration and optimization.	SDN controllers	Open Daylight ONOS	Dynamic resource allocation
Assurance	Monitors performance and ensures SLAs.	SLA monitoring tools	ServiceNow Zabbix	Service quality enforcement
Security management	Protects network integrity and data.	AI-based threat detection	Palo Alto Cortex Fortinet AI	Cybersecurity enforcement
Resource orchestration	Manages resource allocation.	NFV, cloud orchestrators	Kubernetes OpenStack	Dynamic service scaling
Service orchestration	Manages end-to-end service deployment.	Cross-domain orchestrators	ETSI OSM Akraino	5G and 6G service rollout
Catalog management	Maintains service inventories.	Service catalog platforms	TM Forum APIs	Standardized service provisioning

provides a description of the main service categories.

Security is a vital component of the ZSM framework. The ETSI GS ZSM 014 specification outlines the security reference architecture for ZSM, detailing a range of security capabilities as management services. These capabilities encompass adaptive trust relationships between management domains, dynamic access control, the resilience of AI/ML models, and the automatic enforcement of security policies. To validate and demonstrate the practical use of the ZSM framework, the ISG ZSM promotes the creation of PoCs. These PoCs are designed to illustrate the feasibility of ZSM implementations in real-world situations, offering valuable insights and lessons that can guide ongoing specification efforts.

For example, the ServoCloud project was initiated as the first PoC, concentrating on developing a reference architecture for dynamic services and a novel method for automating operations, beginning with 5G network slicing. The ISG ZSM also works closely with relevant standardization organizations, open-source initiatives, and forums to encourage the adoption and alignment of the ZSM architecture and its solutions. This collaborative strategy is essential for achieving automated end-to-end network and service management across the industry, promoting interoperability and innovation.

(c) 

RRS

RRS represents cutting-edge wireless communication technologies that utilize SDR and CR to adapt to real-time changing conditions. This flexibility improves spectrum efficiency, enhances network performance, and facilitates the easy integration of new services. SDR enables radio devices to change their operating parameters, e.g., frequency range, modulation type, and output power, through software updates, which means no hardware changes are necessary [217]. CR allows radio systems to monitor their environment, learn from it, and adjust their parameters and protocols accordingly. This results in better spectrum utilization and enhanced service quality. RRS can take advantage of underutilized frequency bands by adapting to real-time spectrum availability, helping to tackle the issue of limited spectrum resources. Its adaptable design makes it easy to integrate new services and standards without requiring hardware changes. Furthermore, by enabling multiple functions and standards on a single reconfigurable device, RRS reduces the need for various hardware platforms.

ETSI formed the technical committee on RRS (TC RRS) to create standards for various aspects of RRS, including the architecture of reconfigurable equipment and cognitive radio systems. TC RRS focuses on the following main topics [217,218].

• Proposing standards that allow radios to detect and adapt to their surroundings for enhanced performance.
• Developing frameworks that facilitate the shared use of licensed spectrum bands, thereby improving spectrum efficiency.
• Outlining architectures and interfaces that enable the dynamic reconfiguration of radio equipment.
• Setting up certification processes and security protocols to guarantee the reliable and secure functioning of reconfigurable radio systems.

ETSI’s TC RRS has created a range of standards to support software reconfiguration for commercial uses. These standards tackle issues such as spectrum sharing, coexistence among various cognitive radio networks, and the management and control of reconfigurable radio systems. An example is the TS 103 681-3 V1.1.1, which defines protocols for the unified radio application interface, facilitating interoperability among reconfigurable radio devices [218].

10. Conclusions

The emergence of 6G wireless networks offers a once-in-a-generation chance to rethink global connectivity based on intelligent, high-speed, and ultra-reliable communication systems. This review investigated three disruptive technologies: UAV-based communication, THz communication, and IRS technology. These technologies are expected to shape the development of future-generation wireless networks. Each of these enablers contributes unique advantages: UAVs provide flexible and adaptive communication solutions in remote or dynamic environments, THz communication overcomes bandwidth limitations to support ultra-high data rates, and IRS technology enhances spectral and energy efficiency by dynamically reconfiguring signal propagation. Although they hold great promise, there are still some technical issues that need to be addressed, such as energy efficiency, security, seamless integration, and real-time network flexibility. Efforts to overcome these challenges require interdisciplinary collaboration between academia, industry, and policymakers to create new solutions that increase the effectiveness and extensibility of 6G networks. This work provided deep insights into the challenges and key solutions of deploying these three technologies for different 6G applications. Open research questions were also introduced. Future research should address (a) the development of intelligent resource allocation algorithms, (b) hardware feasibility, and (c) a standardized framework for the integration of these technologies in the context of the overall 6G ecosystem.