Next Article in Journal
5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
Previous Article in Journal
Specific Absorption Rate Analysis of Wideband Multiple-Input Multiple-Output Antennas for Upper Mid-Band LTE 46/47 and n102 Future Generation Applications
Previous Article in Special Issue
Fountain Coding Based Two-Way Relaying Cognitive Radio Networks Employing Reconfigurable Intelligent Surface and Energy Harvesting
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Telecom
Volume 6
Issue 2
10.3390/telecom6020023
telecom-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Coexistence in Wireless Networks: Challenges and Opportunities

by
Nagma Parveen
1,*,
Khaizuran Abdullah
1,
Khairayu Badron
1,
Yasir Javed
2 and
Zafar Iqbal Khan
2
1
Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
2
College of Computer Science and Information Sciences, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia
*
Author to whom correspondence should be addressed.
Telecom 2025, 6(2), 23; https://doi.org/10.3390/telecom6020023
Submission received: 6 February 2025 / Revised: 10 March 2025 / Accepted: 21 March 2025 / Published: 1 April 2025
(This article belongs to the Special Issue Performance Criteria for Advanced Wireless Communications)
Browse Figures
Versions Notes

Abstract

:
The potential consequences of interference on communication networks are one of the main challenges in the nature and efficiency of wireless communication links. The interruption is seen as additional noise to the device, which can have a major impact on the efficiency of the connection. The rapid expansion of broadband wireless networks and the increasing congestion of the radio frequency spectrum due to shared usage by terrestrial and satellite networks have heightened concerns about potential interference. To optimize spectrum utilization, multiple terrestrial and satellite networks often coexist within the same frequency bands allocated for satellite communications services. Spectrum interference in wireless networks is a topic of much interest in the current scenario as it can present a lot of challenges. This article provides a critical review of the coexistence and spectrum sharing in wireless networks. Along with this, mitigation techniques to avoid interference have also been discussed in detail. The article aims to give a detailed discussion on the challenges and opportunities in this field by reviewing significant recent works in this field.

1. Introduction

The network densification and spectrum bands available to many technologies are projected to meet the rising capacity demands in new wireless technologies [1]. As a result, the degree of interference will rise, as will the complexity of inter-technology interactions, which will require spectrum sharing methods to handle [2]. It is imperative to devise innovative spectrum sharing methodologies that enable various technologies to access the spectrum efficiently, thereby optimizing its overall utilization. Developing such systems is a complex endeavor, hindered not only by technological challenges but also by legal and economic limitations [3]. The millimeter-wave (mm Wave) frequency range proves suitable for future wireless networks focused on short-range, high-speed communication due to its broader spectrum and higher frequencies, compared to current frequency bands [4]. As a relatively new technology, mm Wave systems should be designed to seamlessly interoperate with existing services and complement other technologies to improve overall network performance. Consequently, the challenges associated with coexistence have emerged as a pivotal issue in the realm of next-generation wireless communications [5].
As the demand for seamless mobile connectivity continues to grow, the IMT-2020 (5G) system, hereafter referred to as 5G, is emerging as a pivotal technology for ensuring comprehensive global broadband coverage in the future [6]. 5G has important features such as delivering low-latency, high-speed connectivity, uniform service with high traffic volume, support for countless connected devices, and exceptional mobility across various scenarios. To address the escalating demand for high data rate services, fifth-generation (5G) technology is being developed to enhance network performance and introduce new capabilities such as high-speed data transmission, ultra-low latency, and massive connectivity [7].
To enhance spectrum efficiency and effectively support high data rates, one viable approach is to identify additional available spectrum resources. Nevertheless, improving spectrum efficiency poses a formidable challenge, particularly in sub-3 GHz bands, where the spectrum is congested, fragmented, and inefficiently utilized across heterogeneous wireless networks, resulting in interference issues. The 3400–3600 MHz frequency band offers a wider contiguous bandwidth compared to lower frequency bands and has superior propagation characteristics relative to higher frequency bands. Consequently, it has emerged as a pivotal frequency band for the commercial deployment of 5G systems [8].

2. Related Survey

The coexistence of satellite and terrestrial communication within the millimeter wave (mm Wave) spectrum presents unique challenges and opportunities, warranting dedicated investigation. While satellite systems operate in various frequency bands, such as the S-band and Ka-band, the mm Wave spectrum is particularly relevant due to its critical role in next-generation broadband communication, including 5G and beyond. The high spectrum availability in mm Wave frequencies makes it attractive for both satellite and terrestrial applications, necessitating effective spectrum sharing strategies to mitigate interference and enhance coexistence. This section reviews key studies that explore coexistence challenges in mm Wave bands, along with their proposed mitigation techniques, while highlighting their limitations and interconnections.
Several surveys have systematically analyzed interference scenarios and spectrum sharing strategies in mm Wave communication. One such study [9] investigated the design space for spectrum sharing across different system layers, emphasizing the impact of inter-technology coexistence. While this study provides a structured overview of interference challenges, it lacks a detailed evaluation of the real-world implementation constraints. Another survey [10] explored the synergy between radar and communication systems, discussing waveform design, signal modeling, and processing techniques. This work contributes to the understanding of spectrum coexistence, yet it primarily focuses on theoretical modeling rather than practical deployment challenges. The study in [11] briefly examines the advancements in satellite communication, providing insights into present and future applications. However, it lacks an in-depth discussion of coexistence issues specific to mm Wave frequencies, making it a complementary reference rather than a core analysis of spectrum sharing.
Further research has delved into frequency reuse strategies within both satellite and mobile cellular systems. Ref. [12] comprehensively examines coexistence challenges in broadband mm Wave communication, covering interactions with fixed services, non-orthogonal multiple access (NOMA), and microwave systems. Additionally, an extensive study [13] investigated radar systems operating in the 5 GHz band, discussing spectrum regulations and sharing principles. Although this research provides a foundational understanding of spectrum sharing strategies, its focus on the 5 GHz band makes its findings less directly applicable to mm Wave coexistence. Finally, another survey [14] examined heterogeneous wireless networks operating in TV white spaces, offering a comparative analysis of different coexistence techniques. While its insights on spectrum sharing mechanisms are valuable, the study does not specifically address the coexistence of mm Wave satellite and terrestrial systems.
However, recent advancements have significantly expanded on these topics, addressing new coexistence challenges. For instance, the emergence of mm Wave-based networking and sensing applications, such as mNetS [15] and CoSense [16], demonstrates novel approaches to integrating networking with sensing for applications in health diagnostics and traffic monitoring. These systems optimize coexistence by leveraging idle periods and deep learning models to enhance sensing resolution, without significantly affecting network throughput. This directly aligns with the objectives of our work, which seeks to optimize resource utilization in congested mm Wave environments by exploiting temporal and spatial variations in traffic.
To address spectrum congestion, bidirectional data offloading techniques have been proposed to optimize spectrum and power allocation in coexisting 5G and Wi-Fi networks at 60 GHz [17]. This approach, which maximizes the sum rate through resource allocation, reflects the growing trend towards dynamic spectrum management, a key consideration in our investigation. Concerns about interference between terrestrial networks and satellite services in shared frequency bands have been extensively analyzed. Studies on co-channel interference and out-of-band emissions provide critical guidelines for protecting satellite services while optimizing terrestrial network deployment [18]. Furthermore, intelligent resource allocation strategies have been explored to manage interference in hybrid satellite–terrestrial networks, ensuring efficient coexistence in congested environments [19]. These studies highlight the importance of adaptive interference management, which is a central theme in our research.
With the growing interest in expanding spectrum access, particularly in the 12 GHz band, recent research emphasizes the need for adaptive spectrum sharing policies to enable the integration of 5G terrestrial services while mitigating interference with incumbent systems [20]. This focus on dynamic spectrum access aligns with our investigation into flexible resource allocation strategies. Furthermore, advancements in reconfigurable hardware, such as the reconfigurable substrate integrated waveguide (SIW) filtenna operating in the 5G mm Wave band [21], offer promising solutions for reducing adjacent channel interference and enabling passive spectrum coexistence. Similarly, joint multi-dimensional resource allocation algorithms based on block coordinate descent have been proposed to optimize uplink throughput in 5G/Wi-Fi coexisting mm Wave networks [22], addressing the growing demand for high throughput in hotspots. These recent developments illustrate the importance of adaptive techniques in managing interference and optimizing resource allocation, which are the core elements of our research. By incorporating these findings, this review extends prior research and addresses the evolving landscape of coexistence challenges in wireless networks, highlighting the need for dynamic and intelligent resource management strategies to ensure efficient and harmonious spectrum utilization.

3. Types of Wireless Communication

Wireless communication networks transmit data through electromagnetic waves instead of using physical cables. Wireless communication networks have emerged as a dominant force, supplanting wired connections in many applications [23,24]. Leveraging radio waves for data transmission, these networks offer unparalleled flexibility and mobility, enabling communication between devices without physical cables. This technology has fostered significant advancements in areas like mobile computing, internet access, and home automation, fundamentally altering how we connect and share information in the modern world [25]. Figure 1 shows the different types of wireless communication systems.

3.1. Cellular Networks

A cellular network is a form of wireless communication system that divides the coverage area into cells supplied by base stations or cell towers in order to deliver voice and data services to mobile devices over a large geographic area [26,27,28]. These networks have undergone several waves of development through time, with each generation offering advances in data speed, coverage, and capabilities [29]. The partitioning of the coverage area into discrete geographic areas known as cells is where the word “cellular” originates from [30].
Despite their diverse technological implementations, various wireless systems share a common network architecture comprising three primary components as follows: the mobile device, the radio access network (RAN), and the core network, as illustrated in Figure 2.

3.2. Wi-Fi Networks

Wi-Fi networks, often referred to as Wireless Fidelity networks, are a type of wireless local area network (LAN) technology that enables devices to connect to the internet and communicate with one another wirelessly inside a constrained space [32,33,34]. Wi-Fi networks are secured through protocols such as WPA, WPA2, and WPA3, ensuring data encryption and connection safety. Wi-Fi is frequently used to wirelessly connect devices like smartphones, computers, tablets, and smart home appliances to the internet in homes, offices, public spaces, and various other locations, as shown in Figure 3 [35,36]. Recent advancements, such as mesh networks for eliminating dead zones, highlight the pivotal role of Wi-Fi in enabling modern communication and supporting the growth of smart technologies.
Bluetooth and Bluetooth Low Energy (BLE): Bluetooth is a short-range wireless technology that enables devices to connect and exchange data over short distances, typically up to 100 m [38]. It was initially created for voice communication and information exchange between electronic devices like PCs, headsets, and cellphones. Bluetooth has developed over time to offer a number of profiles and services, such as audio streaming, file sharing, and device pairing [39]. A Bluetooth extension known as Bluetooth Low Energy was created for applications that only need sporadic data exchanges, optimized for minimal battery consumption [40]. Fitness trackers, smartwatches, and other IoT gadgets that frequently run on batteries are excellent candidates for BLE [41]. Like conventional Bluetooth, it uses the 2.4 GHz radio spectrum to operate [42]. Recent advancements, such as Bluetooth 5.0 and beyond, have enhanced BLE’s range, speed, and support for mesh networking, further solidifying its role in modern wireless ecosystems. Both Bluetooth and BLE play complementary roles, enabling seamless connectivity across diverse applications.

3.3. Zigbee and Other Low-Power Wireless Networks

Zigbee networks are a kind of low-power wireless communication system designed for controlling and transferring data over short distances [43,44]. Zigbee networks are characterized by their mesh topology, which enables devices to communicate directly or relay data through intermediate nodes, improving range and reliability. It supports up to 65,000 nodes, making it highly scalable for large networks. Zigbee is frequently used in situations where devices need to interact wirelessly while using the least amount of power, which makes it suitable for use in sensor networks, home automation, and industrial control [45]. Some other examples of similar low-power networks are Z-Wave and Thread [46,47].
Other low-power wireless networks, such as Z-Wave, Long Range Wide Area Network (LoRaWAN), and Thread, complement Zigbee by targeting similar use cases with unique strengths. Z-Wave operates in the sub-1 GHz band, reducing interference and extending the range, making it popular in smart home devices. LoRaWAN is designed for long-range, low-power communication, often used in industrial IoT and smart city applications. Thread, like Zigbee, leverages IEEE 802.15.4 but focuses on IPv6-based communication for seamless internet integration. These networks prioritize low power consumption to extend the battery life of the connected devices, making them essential for IoT deployments. Each protocol caters to specific requirements, from Zigbee’s robust mesh networking to LoRaWAN’s extensive coverage, ensuring flexibility in low-power wireless communication across diverse environments. Together, they enable scalable, energy-efficient solutions for smart technologies.

3.4. Satellite Communication Systems

Satellite communication systems are advanced technologies that enable the transmission of data, voice, and video signals over long distances by utilizing satellites in the Earth’s orbit. These systems play a crucial role in connecting remote and inaccessible areas where terrestrial communication infrastructure is limited or unavailable. Satellite communications operate through a network of ground stations, user terminals, and orbiting satellites, using radio frequencies in various bands, such as the C-band, Ku-band, Ka-band, and L-band, to provide global coverage [48]. Particularly in regions with sparse or nonexistent terrestrial communication infrastructure, these systems are essential for enabling global connectivity [49]. Wide-area coverage is made possible through satellite communication, which is utilized for many purposes such as navigation, internet access, military communication, television transmission, and more [50]. Depending on the use and coverage region, communication satellites are deployed in a variety of orbits, including medium Earth Orbit (MEO), geostationary Orbit (GEO), and low Earth Orbit (LEO) [51], as shown in Figure 4.
Satellite communication systems are widely used in various domains, including broadcasting, global positioning systems (GPS), disaster management, remote sensing, and military operations [52]. The emergence of high-throughput satellites (HTS) and satellite constellations like SpaceX’s Starlink and OneWeb has revolutionized the industry, enabling high-speed internet access even in the most remote locations. These systems are pivotal in bridging the digital divide and supporting the global communication infrastructure.
Telecom 06 00023 g004
Figure 4. Multi-layer satellite networks [53].
Figure 4. Multi-layer satellite networks [53].
Telecom 06 00023 g004

4. Spectrum Sharing and Coexistence

The mm Wave band, boasting higher frequencies and a wider spectrum than traditional frequency bands, proves to be an ideal candidate for future wireless networks, emphasizing high-speed, short-range communication [54]. As an emerging technology, millimeter wave is expected to be interoperable with existing systems and capable of collaborating with other technologies to enhance overall network performance. Consequently, addressing coexistence challenges has become a crucial aspect of next-generation wireless communications [55].
The coexistence of wireless networks is essential for effective communication between various technologies, such as satellite systems, 5G, and mm Wave. For each of these, specific strategies are needed to reduce interference and maximize spectrum utilization. Because of their narrow propagation ranges and great sensitivity to interference, millimeter-wave coexistence in 5G presents difficulties. While Dynamic Spectrum Sharing (DSS), as demonstrated by Verizon’s implementation, allows for smooth cohabitation across mm Wave and sub-6 GHz bands, beamforming and massive MIMO reduce signal overlap. Dynamic spectrum access strategies have been shown to improve dependability in real-world urban microcell experiments [56].
Spectrum sharing techniques like Carrier Aggregation (CA), which enable LTE and 5G to function on different carriers while maximizing throughput, are necessary for 5G and LTE coexistence. Operators such as NTT Docomo have implemented network slicing, which reduces interference by allowing independent service operation within shared networks. Spectrum sharing issues arise when satellites and terrestrial networks interact. Multi-operator studies have shown that Reverse Spectrum Allocation dynamically reallocates frequencies in order to reduce interference [57]. Furthermore, AI-based power control reduces interference in terrestrial 5G networks by regulating satellite uplink transmissions.
Figure 5 illustrates the coexistence of the following three major communication systems: radar communication, terrestrial communication, and satellite communication. The central “Coexistence” node signifies the interplay and spectrum sharing between these technologies. The arrows indicate bidirectional interactions, showing how each system influences and depends on others.

4.1. Coexistence in Radar Networks

Spectrum sharing between radar and communication systems has emerged as a critical research area. The growing prominence of radar technology, with applications ranging from autonomous vehicles to Internet of Things (IoT) devices, has intensified the need for efficient spectrum utilization [58]. However, as the proliferation of radars continues, ensuring their ability to coexist and function without mutual interference is becoming increasingly vital. This challenge is referred to as coexistence in radar networks.
Convolutional Neural Network (CNN)-based techniques for interference reduction and denoising in [59] produce promising performance outcomes for radar processing. Nevertheless, these models usually require more memory to operate because they have millions of parameters that are kept in hundreds of megabytes of memory [60,61,62]. Previously, studied examined quantization methods for CNN-based radar signal denoising and interference reduction. These studied investigated the potential of various CNN architectures and sizes for quantization by employing piecewise constant activation functions and quantized weights. This approach aims to reduce memory consumption during both model storage and inference. Figure 6 represents a deep learning-based interference mitigation model designed to process and reduce noise in communication signals. The input layer receives raw signals, which pass through multiple convolutional and feature extraction layers that identify interference patterns. These layers apply filtering, normalization, and activation functions to suppress interference while preserving the original signal. The final output layer produces a refined signal with minimized interference, enhancing communication reliability.
In order to maximize the available bandwidth and accomplish both radar detection and communication, Ref. [63] offers a novel transmission approach. With a receiver system that enables channel estimation for radar without compromising the communication data rate, the technique combines orthogonal frequency-division multiplexing (OFDM) for communication and frequency-modulated continuous wave (FMCW) for radar. The results demonstrate that the system provides good sensing accuracy under low SNR circumstances and communication performance that is only slightly lower than a perfect CSI scenario. More specifically, the uplink of a large MIMO communication system coexisting with a radar system within the same frequency band was investigated in [64]. Radar-generated clutter at the MIMO receiver is considered in the system’s model. Several linear receiver architectures, ranging from simple channel-matched beamforming to advanced zero-forcing and linear minimum mean square error techniques, have been proposed to mitigate the effects of channel congestion. These studies demonstrate that clutter significantly impacts cellular communication system performance and highlight the critical role of large-scale antenna arrays at the base station in enhancing data detection and system robustness.
A hybrid radar communication approach is depicted in [65], inserting data into chirp sub-carriers using Fractional Fourier Transform (FrFT) multiplexing with varying time-frequency rates. The goal of the optimization techniques are to improve the Bit Error Rate (BER) and bandwidth occupancy. When compared to a conventional Linear Frequency Modulated (LFM) pulse of comparable duration and bandwidth, the FrFT waveform preserves the radar characteristics while providing better performance, such as a lower BER and strong resistance to channel distortions. A solution for coexisting downlink communication and MIMO radar systems is provided in [66]. By employing a constructive MUI and exchanging multi-user interference power for reduced transmit power, the method strikes a compromise between power efficiency and performance. A gradient projection method is used to address the power reduction problem while considering the effect of BS interference on MIMO radar systems. Additionally, the technique employs a strong power minimization strategy to deal with faulty channel information. The outcomes demonstrate that the suggested strategy preserves performance–complexity balance while saving energy costs.

4.2. Coexistence in Satellite Communication

The rapid rise of internet applications and services has intensified the demand for high-speed, versatile, and reliable communication networks with minimal latency. Consequently, satellite communications have entered a critical phase of development [60]. Satellites can significantly contribute to meeting this demand, either as standalone systems or as integrated satellite–terrestrial networks, leveraging their unique capabilities and technological advancements [67]. Figure 7 illustrates the interference in satellite and terrestrial communication networks, where multiple signals overlap, causing disruptions. The satellite transmits signals to ground stations, but interference sources, such as overlapping frequency bands or environmental factors, introduce noise. Wireless communication devices and base stations also contribute to interference, affecting signal clarity and reception. The distorted signals can degrade communication quality, leading to data loss, increased latency, or connectivity issues.
Facilitating communication across great distances and in isolated areas, satellite communication is an essential part of contemporary communication systems [68]. However, the coexistence of satellite communication systems has become an important issue because of the growing number of satellite systems and the limited amount of orbital space that is accessible [69]. 5G cellular systems operating below 6 GHz are provided in [70]. The study analyzes the potential impact of out-of-band emissions, receiver saturation, and active antenna systems on both the uplink and downlink performance. The outcomes will inform the assessment of how future 5G deployments may affect C-band satellite Earth station receivers and the related stakeholders. It suggests and tests possible ways that enable both systems to coexist more easily, such as turning off essential emitters or reducing the power of their transmission.
Muhammad and Anwar [71] developed a methodology to assess the compatibility of existing satellite networks and emerging 5G services within the millimeter-wave spectrum, aligned with the IMT-2020 framework. This study modeled signal propagation and analyzed interference between fixed satellite services (FSS) operating in the same band and adjacent-channel passive Earth exploration satellite services (EESS). The results indicate that FSS satellites may experience up to 7.9 dB of increased interference, while most EESS sensors fail to meet safety standards. To ensure coexistence, additional spectrum allocation or interference mitigation measures are necessary. This approach can also be applied to assess interference from non-terrestrial platforms such as airships, balloons, and UAVs. A smart antenna system employing the Recursive Least Squares (RLS) algorithm for adaptive beamforming is proposed in [72] to facilitate coexistence between FSS and 5G networks. The RLS algorithm is utilized to generate a null beam directed toward FSS Earth stations, thereby mitigating interference. The simulation results demonstrate that this approach significantly reduces the required separation distance between 5G base stations and FSS Earth stations, enabling effective coexistence with minimal interference.
Ensuring the coexistence of satellite communication systems is a complex challenge that necessitates a multi-disciplinary approach. This involves several key strategies, such as interference mitigation, spectrum management, and advanced signal processing techniques. Interference mitigation focuses on reducing the potential disruptions caused by overlapping signals, while spectrum management involves carefully allocating frequency bands to avoid conflicts. Advanced signal processing techniques are employed to enhance the clarity and reliability of the communication. With ongoing research and development, these strategies can be refined to ensure that multiple satellite communication systems can operate efficiently and reliably within the same orbital space.

4.3. Coexistence in Terrestrial Communication

The deployment of the fifth generation of mobile radio communication by 2020 was projected to face several challenges, including a rise in service demand with low latency, in order to service billions of end customers known as satellite mobile users [73]. Terrestrial communication systems will most likely meet a thick network of countless small cells at any time and anywhere. As a result, services from terrestrial systems are unavailable to some of the world’s most remote locations [74]. Moreover, effective interference control is necessary to share the spectrum between satellite systems and terrestrial equipment because of the limited supply of spectral resources.
Two channel assignment algorithms for maximizing multicast coverage in terrestrial networks are analyzed in [75]. The study investigates the interactions and coexistence of different channel assignment systems operating within the same area and spectrum. Multiple base stations within a common service area are considered, and network performance is evaluated based on the coverage area and the signal-to-interference-plus-noise ratio (SINR). It has been demonstrated that combining different schemes can be beneficial, since blended systems can benefit from the best aspects of each plan. A coexistence test scenario was established across multiple channels with randomly positioned terrestrial base stations to simplify the analysis. Figure 8 represents a base station (BS) coverage and coexistence model, illustrating the overlapping service areas of multiple base stations. Each colored region represents the coverage area of a different BS (BS1, BS2, BS3, BS5, etc.), highlighting how multiple base stations coexist within an 8 km radius. Larger coverage areas, such as BS 5…BS i, indicate extended network service regions, while smaller zones represent localized base stations that provide targeted connectivity. The overlapping areas suggest potential interference zones, where multiple BS signals interact, requiring efficient frequency management and interference mitigation techniques to maintain seamless communication.
A framework for intelligent spectrum management based on artificial intelligence and software-defined networks (SDN) is presented in [76]. An integrated satellite and terrestrial network (ISTN) with interoperability and the capacity to reprogram is created by combining heterogeneous satellite and terrestrial networks using SDN. AI is also used to set up networks for optimal resource distribution and forecast environmental conditions. The framework, in short, suggests a novel method for utilizing and combining the spectrum of terrestrial and satellite networks. The influence of interference between satellite and terrestrial systems sharing a spectrum is examined in [77]. A realistic coexistence model was developed to derive a closed-form expression for the satellite link outage probability in the presence of terrestrial interference. Both terrestrial and satellite links are modeled with Nakagami fading, while the satellite link additionally considers shadowing Rician fading. Numerical simulations demonstrate that unmanaged terrestrial interference substantially degrades the satellite link outage performance. The formulas acquired are useful for forecasting and mitigating harmful interference while creating coexistence strategies for terrestrial and satellite systems.
This study focuses on designing robust beamforming for satellite-based IoT systems coexisting with terrestrial networks. It addresses the challenges posed by phase errors in channel estimation and formulates an optimization problem to maximize system performance under outage and power constraints. A two-level iterative algorithm is proposed to efficiently solve this complex problem [78].
To evaluate coexisting radar, satellite, and terrestrial communication systems, researchers used simulations, testbeds, and field trials to assess key metrics. INR measures interference relative to noise, while SINR evaluates communication quality. BER quantifies transmission errors, and throughput assesses data transfer rates. Latency measures delays from interference, spectral efficiency evaluates spectrum utilization, and PDR determines packet delivery reliability. Table 1 gives an overview of coexistence with the references for all coexistence scenarios.

5. Interference and Mitigation Techniques

Interference is a common issue that occurs in various wireless communication systems. It can cause signal distortion or complete signal loss, leading to poor quality of service or even a complete breakdown of the system [92,93,94,95]. However, various interference mitigation techniques are available to address these issues and ensure a reliable and stable communication system [96].
One of the most common interference mitigation techniques is frequency hopping. This technique rapidly switches communication channels to deter interference [97]. Spread spectrum techniques distribute signals across a wider frequency band to minimize the impact of interfering signals. Power control mitigates interference by adjusting transmission power to optimize signal strength without causing disturbances [98]. This technique is particularly effective in cellular communication systems, where multiple devices may transmit simultaneously.
Another effective interference mitigation technique is beamforming [99]. This technique involves the use of multiple antennas to create a focused signal in a particular direction, minimizing interference from other directions. Beamforming is particularly useful in outdoor communication systems, where signals may be disrupted by natural obstacles or competing signals. The effect of discrete modulations on three-user interference channel interference mitigation is examined in [100]. The study offers effective and straightforward capacity approximations. It implies that for more generic channels, the Ozarow–Wyner bound needs to be improved, since the current bound might be pessimistic in the event of poor alignment or when an inefficient number of levels (N) is used. Although the findings are primarily theoretical, they point to real-world uses for interference alignment by considering discrete interference as noise once discretely modulated signals have been aligned.
The usefulness of relaying in mitigating interference in a communication context with limited interference was examined in [101]. The study optimizes quantization noise covariance to characterize rate scaling with relay link capacity. It links this scaling to the deterministic relay channel by analyzing the spatial DoF gains from MIMO relaying under noise correlation. The study examines a BICM OFDM system using a prediction-error filter (PEF) to minimize the time-domain disturbance [102]. This approach effectively handles interference without weakening distant tones. Simulations show that, without narrowband interference, the system with PEF performs efficiently and matches similar systems [103].
In [104], two spectrum sharing approaches, orthogonal and non-orthogonal, were investigated for integrated satellite-mobile and autonomous terrestrial-mobile systems. An interference management scheme for co-located base stations and indoor small cells was developed, optimizing the number of blank subframes within a pattern period. System-level performance metrics, including the capacity, spectral efficiency, and energy efficiency, were derived. A novel hybrid approach combining Maximum Likelihood Estimation and wavelet-based self-cancelation is proposed to enhance BER performance in OFDM systems [105]. Significant improvements in the bit error rate were observed when comparing 64-QAM and differential offsets with the traditional rapid Fourier transform methods.
A reliable method for mitigating interference between IMT-advanced and FSS in the 3400–4200 and 4500–4800 MHz bands is suggested in [106], employing null steering MU–MIMO SDMA. In order to produce spatial nulls towards FSS Earth stations, the current precoding matrix is altered. Through simulation, a numerical method for determining the interference power at FSS stations from IMT-Advanced base stations is derived and put into practice, greatly speeding up the solution process. The method’s efficiency was evaluated across varying separation distances and FSS Earth station orientations under both co-channel and adjacent channel sharing scenarios.
Deep Q-learning-dependent transmission power control, which dynamically modifies a system’s transmission power depending on current interference, is one of the newer methods for mitigating interference in dynamic environments [107]. This strategy has been especially successful in High Altitude Platform Stations (HAPS), where intelligent power adaptation is necessary for spectrum sharing with terrestrial and non-terrestrial networks (NTNs). This technique balances interference levels and increases system throughput by optimizing power allocation through the use of reinforcement learning. Message Passing (MP)-based distributed Multi-User Detection (MUD) is a recent development that improves decoding efficiency and reduces interference in satellite communication networks [108]. In situations requiring Inter-Satellite Links (ISLs), where effective detection algorithms are needed to handle signal distortions and multi-user interference, this technique is very pertinent. MP-based MUD improves detection accuracy while lessening the computational load on individual satellite nodes by utilizing distributed processing.
Static frequency division is the strategy used in traditional spectrum allocation techniques, which frequently leads to inefficient use and increased interference. By dynamically reassigning frequency bands between Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN) in response to current interference circumstances and spectrum demand, Reverse Spectrum Allocation solves this problem [57]. This method improves overall coexistence efficiency, reduces spectral congestion, and permits smooth spectrum sharing.
The bar graph with the interference reduction is shown in Figure 9. CNN-based denoising offers the highest interference reduction (95%) but demands high computational resources, making it ideal for radar and wireless systems. Techniques like beamforming and null steering provide strong performance (90–92%) for high-precision applications, while adaptive power control (75%) suits low-complexity scenarios like cellular networks.
Coexistence in the communication network was analyzed and studied, and the research advantages and limitations are tabulated in Table 2.

6. Challenges, Opportunities, and Limitations of Coexistence

Wireless communication technology has become increasingly integrated into our daily lives. The proliferation of devices capable of wireless connectivity has led to a surge in the demand for network capacity [110]. The coexistence of multiple wireless communication systems poses significant challenges in ensuring reliable and efficient network operation. Diverse technologies, protocols, and spectrum sharing requirements create complex interactions that can degrade system performance [111]. Below are some of the key challenges of coexistence in wireless communication.
  • Interference: Interference remains a primary challenge in wireless communication coexistence [112]. Multiple wireless systems operating within the same frequency band can cause mutual interference, leading to signal degradation and elevated error rates. This can result in poor quality of service, reduced range, and decreased data rates. Interference can occur due to overlapping frequency bands, insufficient separation between systems, and power imbalances [113].
  • Network congestion: As the number of wireless communication systems increases, they compete for limited resources such as bandwidth and transmission power, leading to network congestion [114]. This can result in reduced performance, increased latency, and lower throughput. Network congestion can occur due to insufficient capacity, high demand, and inefficient resource allocation.
  • Security: With multiple wireless communication systems operating in the same space, there is an increased risk of unauthorized access and security breaches. This can result in data theft, malicious attacks, and network downtime [115]. Security challenges can occur due to weak encryption, insufficient authentication, and vulnerabilities in the wireless communication systems.
  • Coexistence standards: The coexistence of multiple wireless communication systems requires the development of coexistence standards to ensure that different systems can effectively operate together. However, the development of these standards can be complex, time-consuming, and costly. This can result in delays in the implementation of new wireless communication systems and reduced innovation [116].
  • Compatibility: Diverse wireless systems employing disparate protocols and technologies often exhibit incompatibility challenges [117]. This can result in reduced interoperability and increased complexity in the management of wireless communication systems [118].
  • Frequency resource management: The growing number of wireless communication technologies has intensified the competition for available spectrum resources. Efficient frequency management is required to mitigate spectrum scarcity, but issues such as spectrum fragmentation, inefficient allocation, and regulatory constraints pose significant challenges.
  • Energy consumption management: The demand for high-speed, always-on connectivity has led to concerns about energy efficiency in wireless networks. Managing energy consumption, especially in battery-powered devices and infrastructure, is critical; however, existing systems often lack optimized power control mechanisms, resulting in excessive energy use.
  • Adaptation to dynamic environments: Wireless networks operate in highly dynamic conditions where factors such as interference levels, user mobility, and environmental changes constantly fluctuate. The inability of current systems to adapt efficiently to these variations leads to network inefficiencies and degraded service quality.
Wireless communication is an essential aspect of modern society, with many devices and technologies relying on it [119]. However, a primary challenge in wireless communication is the coexistence problem, where multiple wireless systems sharing the same frequency band experience interference, resulting in degraded performance [120]. To avoid coexistence and ensure efficient and reliable wireless communication, several opportunities exist, including the following:
  • Spectrum allocation: One of the primary ways to avoid coexistence is to allocate specific frequency bands to different wireless systems. This approach ensures that each wireless system operates in its own frequency band, thereby reducing the likelihood of interference [121]. Governments and regulatory bodies often play a significant role in spectrum allocation, and they allocate frequency bands based on factors such as the type of wireless application, geographical location, and licensing requirements [122].
  • Frequency agility: Another opportunity to avoid coexistence is frequency agility, where wireless systems are designed to switch between multiple frequency bands [123]. This approach allows wireless systems to avoid congested frequency bands and operate in less crowded areas, thereby reducing the likelihood of interference. Frequency agility is commonly used in wireless networks such as Wi-Fi, where multiple frequency bands are available for use [124].
  • Power control: Wireless systems can employ power control to mitigate interference. This involves dynamically adjusting the transmission power of the devices to avoid disrupting nearby wireless networks [125]. For instance, in cellular networks, base stations regulate their power output based on the mobile device’s distance and signal strength, thereby reducing the potential for interference with neighboring cellular systems [126].
  • Antenna design: Antenna design plays a critical role in avoiding coexistence. Antennas can be designed to focus their energy on specific directions, thereby reducing the likelihood of interference with nearby wireless systems. Additionally, antenna diversity techniques such as MIMO (Multiple Input Multiple Output) can be used to improve the reliability of wireless communication by using multiple antennas to transmit and receive signals [127]
  • Protocol design: Protocol design is also an essential aspect of avoiding coexistence. Wireless protocols can be designed to detect and avoid interference, such as using collision avoidance techniques to avoid packet collisions in Wi-Fi networks [128]. In addition, protocols can be designed to prioritize critical data and ensure that it is transmitted without interference [129].

7. Conclusions and Future Recommendations

Coexistence in wireless communication is generally advantageous; however, certain scenarios demand targeted strategies to mitigate interference and maintain reliable performance. Key techniques such as dedicated frequency bands, spatial separation, time division, power control, and interference avoidance are crucial in managing coexistence challenges. Furthermore, optimizing spectrum allocation, enhancing frequency agility, implementing adaptive power control, advancing antenna technologies, and refining protocol frameworks present valuable opportunities to improve wireless system performance in congested frequency environments. Future research should prioritize the development of intelligent coexistence mechanisms that harness artificial intelligence and machine learning for dynamic spectrum management and adaptive interference mitigation. Investigating reconfigurable intelligent surfaces and cutting-edge antenna technologies will further strengthen signal directionality and optimize coexistence approaches. Moreover, establishing unified coexistence standards will be essential to ensure seamless integration across emerging wireless technologies, including 6G and beyond. By addressing these challenges and advancing coexistence strategies, wireless systems can achieve greater efficiency, enhanced service quality, and lead to more sustainable spectrum utilization in increasingly dense and complex communication networks.

Author Contributions

The original draft of the manuscript was prepared by N.P. The authors K.A. and K.B. supervised and reviewed the manuscript for further improvement. Funding for the study was acquired by Y.J. and Z.I.K. All authors have read and agreed to the published version of the manuscript.

Funding

The authors acknowledge the support of Prince Sultan University for paying for the article processing charges (APC) of this publication.

Data Availability Statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Acknowledgments

The author Nagma Parveen would like to thank the International Islamic University Malaysia for the support of the TFW2020 scheme of the Kulliyyah of Engineering.

Conflicts of Interest

The authors declare no conflict of interest.

List of Abbreviations

5G5th—generation
NOMANon-orthogonal multiple access
RANRadio access network
LANLocal area network
BLEBluetooth low energy
LoRaWANLong range wide area network
MIMOMultiple input multiple output
mmMillimeter
GEOGeostationary orbit
MEOMedium Earth orbit
LEOlow Earth orbit
FrFTFractional Fourier transform
IoTInternet of things
CNNConvolutional neural network
FMCWFrequency modulated continuous wave
OFDMOrthogonal frequency-division multiplexing
LFMLinear frequency modulated
EESSEarth exploration satellite service
RLSRecursive least square
FSSFixed satellite services

References

  1. Smith, L.; Lynch, C.; Kaylor, C.A.; Campos, L.A.; Cheng, L.; Ralph, S.; Tentzeris, M.M. IF2-30 Lens Antenna System Enabling Simplified Designs of 5G/mmW Base Stations and Network Densification. In 2023 IEEE/MTT-S International Microwave Symposium-IMS 2023; IEEE: New York, NY, USA, 2023; pp. 1211–1214. [Google Scholar] [CrossRef]
  2. Cho, H.; Mukherjee, S.; Kim, D.; Noh, T.; Lee, J. Facing to wireless network densification in 6G: Challenges and opportunities. ICT Express 2023, 9, 517–524. [Google Scholar] [CrossRef]
  3. Nidhi; Mihovska, A. Small Cell Deployment Challenges in Ultradense Networks: Architecture and Resource Management. In Proceedings of the 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Porto, Portugal, 20–22 July 2020; pp. 10–15. [Google Scholar] [CrossRef]
  4. Parveen, N.; Islam, R.; Abdullah, K.; Boby, R.I. Performance of BER with Different Diversity Techniques for Millimeter-Wave Communication System. Int. J. Innov. Technol. Explor. Eng. 2019, 8, 1149–1152. [Google Scholar] [CrossRef]
  5. Chiaraviglio, L.; Turco, S.; Bianchi, G.; Blefari-Melazzi, N. “Cellular Network Densification Increases Radio-Frequency Pollution”: True or False? IEEE Trans. Wirel. Commun. 2022, 21, 2608–2622. [Google Scholar] [CrossRef]
  6. Xu, J.; Rao, Z.; Xu, L.; Yang, D.; Li, T. Incentive Mechanism for Multiple Cooperative Tasks with Compatible Users in Mobile Crowd Sensing via Online Communities. IEEE Trans. Mob. Comput. 2020, 19, 1618–1633. [Google Scholar] [CrossRef]
  7. Liu, G.; Wang, Z.; Hu, J.; Ding, Z.; Fan, P. Cooperative NOMA Broadcasting/Multicasting for Low-Latency and High-Reliability 5G Cellular V2X Communications. IEEE Internet Things J. 2019, 6, 7828–7838. [Google Scholar] [CrossRef]
  8. Tan, H.; Liu, Y.; Feng, Z.; Zhang, Q. Coexistence analysis between 5G system and fixed-satellite service in 3400–3600 MHz. China Commun. 2018, 15, 33–50. [Google Scholar] [CrossRef]
  9. Voicu, A.M.; Simić, L.; Petrova, M. Survey of spectrum sharing for inter-technology coexistence. IEEE Commun. Surv. Tutor. 2018, 21, 1112–1144. [Google Scholar] [CrossRef]
  10. Zheng, L.; Lops, M.; Eldar, Y.C.; Wang, X. Radar and Communication Coexistence: An Overview: A Review of Recent Methods. IEEE Signal Process. Mag. 2019, 36, 85–99. [Google Scholar] [CrossRef]
  11. Misra, D.; Misra, D.K.; Tripathi, S.P. Satellite Communication Advancement, Issues, Challenges and Applications. Int. J. Adv. Res. Comput. Commun. Eng. 2013, 2, 1681–1686. [Google Scholar]
  12. Tai, C.L.; Wu, D. A Review on Coexistence Issues of Broadband Millimeter-Wave Communications. arXiv 2008, arXiv:2008.08916. [Google Scholar]
  13. Labib, M.; Marojevic, V.; Martone, A.F.; Reed, J.H.; Zaghlour, A.I. Coexistence Between Communications and Radar Systems: A Survey. URSI Radio Sci. Bull. 2017, 2, 74–82. [Google Scholar] [CrossRef]
  14. Maloku, H.; Fazliu, Z.L.; Ibrani, M. A Survey on Coexistence in Heterogeneous Wireless Networks in TV White Spaces. Wirel. Commun. Mob. Comput. 2018, 2018, 7256835. [Google Scholar] [CrossRef]
  15. Saadat, M.S.; Sur, S. Enabling Coexistence of Indoor Millimeter- Wave Networking and Human Activity Sensing. In Proceedings of the 2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Wilmington, DE, USA, 19–21 June 2024; pp. 37–48. [Google Scholar] [CrossRef]
  16. Regmi, H.; Sur, S. CoSense: Deep Learning Augmented Sensing for Coexistence with Networking in Millimeter-Wave Picocells. ACM Trans. Internet Things 2024, 5, 1–35. [Google Scholar] [CrossRef]
  17. Xie, Y.; Liu, D.; Yin, F. Joint Spectrum and Power Allocation with User Association for 5G/Wi-Fi Coexisting Millimeter Wave Networks. Electronics 2023, 12, 959. [Google Scholar] [CrossRef]
  18. Lim, B.; Vu, M. Interference Analysis for Coexistence of Terrestrial Networks with Satellite Services. IEEE Trans. Wirel. Commun. 2024, 23, 3146–3161. [Google Scholar] [CrossRef]
  19. Ati, S.B.; Dahrouj, H.; Alouini, M.S. Interference Mitigation in Coexisting Satellites and Cloud-Assisted Ground Networks in the MmWave Band. IEEE Open J. Commun. Soc. 2024, 5, 7483–7497. [Google Scholar] [CrossRef]
  20. Hassan, Z.; Heeren-Moon, E.; Sabzehali, J.; Shah, V.K.; Dietrich, C.; Reed, J.H.; Burger, E.W. Spectrum Sharing of the 12 GHz Band with Two-Way Terrestrial 5G Mobile Services: Motivations, Challenges, and Research Road Map. IEEE Commun. Mag. 2023, 61, 53–59. [Google Scholar] [CrossRef]
  21. Vosoughitabar, S.; Mandayam, N.B.; Brodie, J.F.; Golparvar, B.; Wang, R.Q.; Wu, C.T.M. Design of a Reconfigurable Filtenna with Constant Bandwidth for Enhanced 5G mmWave Communication and Spectrum Coexistence. In Proceedings of the 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Washington, DC, USA, 13–16 May 2024; pp. 517–523. [Google Scholar] [CrossRef]
  22. Xie, Y.; Liu, D.; Zhang, Z. Joint Beam and Spectrum Allocation with User Association for 5G/WiFi Coexisting Millimeter Wave Networks. Lect. Notes Electr. Eng. 2024, 1033, 59–67. [Google Scholar] [CrossRef]
  23. Aboagye, S.; Saeidi, M.A.; Tabassum, H.; Tayyar, Y.; Hossain, E.; Yang, H.C.; Alouini, M.S. Multi-Band Wireless Communication Networks: Fundamentals, Challenges, and Resource Allocation. IEEE Trans. Commun. 2024, 72, 4333–4383. [Google Scholar] [CrossRef]
  24. Parveen, N.; Abdullah, K.; Badron, K.; Diyana, F.; Rahman, A.; Morshidi, A. Bin Generation and Analysis of 5G Downlink Signal. J. Adv. Res. Appl. Sci. Eng. Technol. 2022, 1, 23–30. [Google Scholar]
  25. Zhang, R.; Zhang, L.; Yue, Y.; Feng, H.; Li, Z.; Ying, D. Editorial for Special Issue “Advancements in Wireless Communications, Networks, and Signal Processing”. Appl. Sci. 2024, 14, 5725. [Google Scholar] [CrossRef]
  26. Soh, Y.S.; Quek, T.Q.S.; Kountouris, M.; Shin, H. Energy efficient heterogeneous cellular networks. IEEE J. Sel. Areas Commun. 2013, 31, 840–850. [Google Scholar] [CrossRef]
  27. Andrews, J.G.; Baccelli, F.; Ganti, R.K. A tractable approach to coverage and rate in cellular networks. IEEE Trans. Commun. 2011, 59, 3122–3134. [Google Scholar] [CrossRef]
  28. Hasan, Z.; Boostanimehr, H.; Bhargava, V.K. Green cellular networks: A survey, some research issues and challenges. IEEE Commun. Surv. Tutor. 2011, 13, 524–540. [Google Scholar] [CrossRef]
  29. Ge, X.; Tu, S.; Mao, G.; Wang, C.X.; Han, T. 5G Ultra-Dense Cellular Networks. IEEE Wirel. Commun. 2016, 23, 72–79. [Google Scholar] [CrossRef]
  30. Ghosh, A.; Mangalvedhe, N.; Ratasuk, R.; Mondal, B.; Cudak, M.; Visotsky, E.; Thomas, T.A.; Andrews, J.G.; Xia, P.; Jo, H.S.; et al. Heterogeneous cellular networks: From theory to practice. IEEE Commun. Mag. 2012, 50, 54–64. [Google Scholar] [CrossRef]
  31. Brunello, A.; Dalla Torre, A.; Gallo, P.; Gubiani, D.; Montanari, A.; Saccomanno, N. Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis. Sensors 2023, 23, 352. [Google Scholar] [CrossRef]
  32. Afanasyev, M.; Chen, T.; Voelker, G.M.; Snoeren, A.C. Usage patterns in an urban WiFi network. IEEE/ACM Trans. Netw. 2010, 18, 1359–1372. [Google Scholar] [CrossRef]
  33. Dimatteo, S.; Hui, P.; Han, B.; Li, V.O.K. Cellular traffic offloading through WiFi networks. In Proceedings of the 8th IEEE International Conference on Mobile Ad-hoc and Sensor Systems, Valencia, Spain, 17–21 October 2011; pp. 192–201. [Google Scholar] [CrossRef]
  34. Zhong, Z.; Kulkarni, P.; Cao, F.; Fan, Z.; Armour, S. Issues and challenges in dense WiFi networks. In Proceedings of the IWCMC 2015—11th International Wireless Communications and Mobile Computing Conference, Leeds, UK, 24–28 August 2015; pp. 947–951. [Google Scholar] [CrossRef]
  35. Duarte, M.; Sabharwal, A.; Aggarwal, V.; Jana, R.; Ramakrishnan, K.K.; Rice, C.W.; Shankaranarayanan, N.K. Design and characterization of a full-duplex multiantenna system for wifi networks. IEEE Trans. Veh. Technol. 2014, 63, 1160–1177. [Google Scholar] [CrossRef]
  36. Wu, X.; Soltani, M.D.; Zhou, L.; Safari, M.; Haas, H. Hybrid LiFi and WiFi Networks: A Survey. IEEE Commun. Surv. Tutor. 2021, 23, 1398–1420. [Google Scholar] [CrossRef]
  37. Roy, S.C.; Funabiki, N.; Rahman, M.M.; Wu, B.; Kuribayashi, M.; Kao, W.C. A Study of the Active Access-Point Configuration Algorithm under Channel Bonding to Dual IEEE 802.11n and 11ac Interfaces in an Elastic WLAN System for IoT Applications. Signals 2023, 4, 274–296. [Google Scholar] [CrossRef]
  38. Bello, L.L.; Mirabella, O. Communication Techniques and Architectures for Bluetooth Networks in Industrial Scenarios. In Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation, Catania, Italy, 19–22 September 2005; Volume 1, pp. 53–61. [Google Scholar]
  39. Raza, S.; Misra, P.; He, Z.; Voigt, T. Bluetooth smart: An enabling technology for the Internet of Things. In Proceedings of the 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Abu Dhabi, United Arab Emirates, 19–21 October 2015; pp. 155–162. [Google Scholar] [CrossRef]
  40. Nair, K.; Kulkarni, J.; Warde, M.; Dave, Z.; Rawalgaonkar, V.; Gore, G.; Joshi, J. Optimizing power consumption in iot based wireless sensor networks using Bluetooth Low Energy. In Proceedings of the 2015 International Conference on Green Computing and Internet of Things, ICGCIoT 2015, Delhi, India, 8–10 October 2015; pp. 589–593. [Google Scholar] [CrossRef]
  41. Darroudi, S.M.; Gomez, C.; Crowcroft, J. Bluetooth Low Energy Mesh Networks: A Standards Perspective. IEEE Commun. Mag. 2020, 58, 95–101. [Google Scholar] [CrossRef]
  42. Jung, C.; Kim, K.; Seo, J.; Silva, B.N.; Han, K. Topology Configuration and Multihop Routing Protocol for Bluetooth Low Energy Networks. IEEE Access 2017, 5, 9587–9598. [Google Scholar] [CrossRef]
  43. Kumar, T.; Mane, P.B. ZigBee topology: A survey. In Proceedings of the 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, India, 16–17 December 2016; pp. 164–166. [Google Scholar] [CrossRef]
  44. Pan, G.; He, J.; Wu, Q.; Fang, R.; Cao, J.; Liao, D. Automatic stabilization of Zigbee network. In Proceedings of the 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD 2018), Chengdu, China, 26–28 May 2018; pp. 224–227. [Google Scholar] [CrossRef]
  45. Cao, X.; Shila, D.M.; Cheng, Y.; Yang, Z.; Zhou, Y.; Chen, J. Ghost-in-ZigBee: Energy Depletion Attack on ZigBee-Based Wireless Networks. IEEE Internet Things J. 2016, 3, 816–829. [Google Scholar] [CrossRef]
  46. Lalle, Y.; Fourati, L.C.; Fourati, M.; Barraca, J.P. A Comparative Study of LoRaWAN, SigFox, and NB-IoT for Smart Water Grid. In Proceedings of the Global Information Infrastructure and Networking Symposium, Pairs, France, 18–20 December 2019; pp. 1–6. [Google Scholar] [CrossRef]
  47. Unwala, I.; Taqvi, Z.; Lu, J. IoT security: ZWave and thread. In Proceedings of the 2018 IEEE Green Technologies Conference (GreenTech), Austin, TX, USA, 4–6 April 2018; pp. 176–182. [Google Scholar] [CrossRef]
  48. Di, B.; Zhang, H.; Song, L.; Li, Y.; Li, G.Y. Ultra-Dense LEO: Integrating Terrestrial-Satellite Networks into 5G and beyond for Data Offloading. IEEE Trans. Wirel. Commun. 2019, 18, 47–62. [Google Scholar] [CrossRef]
  49. Bertaux, L.; Medjiah, S.; Berthou, P.; Abdellatif, S.; Hakiri, A.; Gelard, P.; Planchou, F.; Bruyere, M. Software defined networking and virtualization for broadband satellite networks. IEEE Commun. Mag. 2015, 53, 54–60. [Google Scholar] [CrossRef]
  50. Zhu, X.; Jiang, C.; Kuang, L.; Ge, N.; Lu, J. Non-Orthogonal Multiple Access Based Integrated Terrestrial-Satellite Networks. IEEE J. Sel. Areas Commun. 2017, 35, 2253–2267. [Google Scholar] [CrossRef]
  51. Al-Hourani, A. Session Duration between Handovers in Dense LEO Satellite Networks. IEEE Wirel. Commun. Lett. 2021, 10, 2810–2814. [Google Scholar] [CrossRef]
  52. Baba, T.; Ahmed Siddiqui, N.; Bte Saidin, N.; Md Yusoff, S.H.; Abdul Sani, S.F.B.; Abdul Karim, J.; Hasbullah, N.F. Radiation-induced degradation of silicon carbide MOSFETs—A review. Mater. Sci. Eng. B 2024, 300, 117096. [Google Scholar] [CrossRef]
  53. Cao, X.; Li, Y.; Xiong, X.; Wang, J. Dynamic Routings in Satellite Networks: An Overview. Sensors 2022, 22, 4552. [Google Scholar] [CrossRef]
  54. Parveen, N.; Abdullah, K.; Islam, R.; Boby, R.I. Performance of MIMO DWT for Millimeter Wave Communication System. In Proceedings of the 2019 IEEE 14th Malaysia International Conference on Communication (MICC), Selangor, Malaysia, 2–4 December 2019; pp. 2–4. [Google Scholar]
  55. Munir, M.E.; Al Harbi, A.G.; Kiani, S.H.; Marey, M.; Parchin, N.O.; Khan, J.; Mostafa, H.; Iqbal, J.; Khan, M.A.; See, C.H.; et al. A New mm-Wave Antenna Array with Wideband Characteristics for Next Generation Communication Systems. Electronics 2022, 11, 1560. [Google Scholar] [CrossRef]
  56. Sharma, S.K.; Bogale, T.E.; Le, L.B.; Chatzinotas, S.; Wang, X.; Ottersten, B. Dynamic Spectrum Sharing in 5G Wireless Networks with Full-Duplex Technology: Recent Advances and Research Challenges. IEEE Commun. Surv. Tutor. 2018, 20, 674–707. [Google Scholar] [CrossRef]
  57. Lee, H.W.; Medles, A.; Jie, V.; Lin, D.; Zhu, X.C.; Fu, I.K.; Wei, H.Y. Reverse Spectrum Allocation for Spectrum Sharing between TN and NTN. In Proceedings of the 2021 IEEE Conference on Standards for Communications and Networking (CSCN), Thessaloniki, Greece, 15–17 December 2021. [Google Scholar]
  58. Gottinger, M.; Hoffmann, M.; Christmann, M.; Schutz, M.; Kirsch, F.; Gulden, P.; Vossiek, M. Coherent Automotive Radar Networks: The Next Generation of Radar-Based Imaging and Mapping. IEEE J. Microw. 2021, 1, 149–163. [Google Scholar] [CrossRef]
  59. Rock, J.; Roth, W.; Meissner, P.; Pernkopf, F. Quantized Neural Networks for Radar Interference Mitigation. arXiv 2020, arXiv:2011.12706. [Google Scholar]
  60. Rock, J.; Roth, W.; Toth, M.; Meissner, P.; Pernkopf, F. Resource-Efficient Deep Neural Networks for Automotive Radar Interference Mitigation. IEEE J. Sel. Top. Signal Process. 2021, 15, 927–940. [Google Scholar] [CrossRef]
  61. Shaikh, I.M.; Akhtar, M.N.; Aabid, A.; Ahmed, O.S. Enhancing sustainability in the production of palm oil: Creative monitoring methods using YOLOv7 and YOLOv8 for effective plantation management. Biotechnol. Rep. 2024, 44, e00853. [Google Scholar] [CrossRef]
  62. Alaskar, H.; Saba, T. Machine Learning and Deep Learning: A Comparative Review. In Proceedings of Integrated Intelligence Enable Networks and Computing; Springer: Berlin/Heidelberg, Germany, 2021; pp. 143–150. [Google Scholar]
  63. Mert Sahin, M.; Arslan, H. Multi-functional Coexistence of Radar-Sensing and Communication Waveforms. In Proceedings of the 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Victoria, BC, Canada, 18 November–16 December 2020; pp. 1–5. [Google Scholar] [CrossRef]
  64. Buzzi, S.; Lops, M.; D’Andrea, C.; Delia, C. Co-Existence between a Radar System and a Massive MIMO Wireless Cellular System. In Proceedings of the 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Kalamata, Greece, 25–28 June 2018. [Google Scholar] [CrossRef]
  65. Gaglione, D.; Clemente, C.; Ilioudis, C.V.; Persico, A.R.; Proudler, I.K.; Soraghan, J.J. Fractional fourier based waveform for a joint radar-communication system. In Proceedings of the 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, USA, 2–6 May 2016. [Google Scholar] [CrossRef]
  66. Liu, F.; Masouros, C.; Li, A.; Ratnarajah, T.; Zhou, J. MIMO Radar and cellular coexistence: A power-efficient approach enabled by interference exploitation. IEEE Trans. Signal Process. 2018, 66, 3681–3695. [Google Scholar] [CrossRef]
  67. Di, B.; Song, L.; Li, Y.; Poor, H.V. Ultra-Dense LEO: Integration of satellite access networks into 5G and beyond. IEEE Wirel. Commun. 2019, 26, 62–69. [Google Scholar] [CrossRef]
  68. Wang, Y.; Yang, J.; Guo, X.; Qu, Z. A Game-Theoretic Approach to Computation Offloading in Satellite Edge Computing. IEEE Access 2020, 8, 12510–12520. [Google Scholar] [CrossRef]
  69. Centenaro, M.; Costa, C.E.; Granelli, F.; Sacchi, C.; Vangelista, L. A survey on technologies, standards and open challenges in satellite IoT. IEEE Commun. Surv. Tutor. 2021, 23, 1693–1720. [Google Scholar] [CrossRef]
  70. Lagunas, E.; Tsinos, C.G.; Sharma, S.K.; Chatzinotas, S. 5G cellular and fixed satellite service spectrum coexistence in C-band. IEEE Access 2020, 8, 72078–72094. [Google Scholar] [CrossRef]
  71. Cho, Y.; Kim, H.-K.; Nekovee, M.; Jo, H.-S. Coexistence of 5G With Satellite Services in the Millimeter-Wave Band. IEEE Access 2020, 8, 163618–163636. [Google Scholar] [CrossRef]
  72. Muhammad, C.B.; Anwar, K. Interference Mitigation using Adaptive Beamforming with RLS Algorithm for Coexistence between 5G and Fixed Satellite Services in C-Band. J. Phys. Conf. Ser. 2020, 1577, 012029. [Google Scholar] [CrossRef]
  73. Habibi, M.A.; Nasimi, M.; Han, B.; Schotten, H.D. A Comprehensive Survey of RAN Architectures Toward 5G Mobile Communication System. IEEE Access 2019, 7, 70371–70421. [Google Scholar] [CrossRef]
  74. Logeshwaran, J.; Selvaraj, K. A Smart Design of a Multi-Dimensional Antenna to Enhance the Maximum Signal Clutch to the Allowable Standards in 5G Communication Networks. J. Microelectron. 2022, 8, 1269–1274. [Google Scholar] [CrossRef]
  75. Yang, M.; Grace, D. Interaction and coexistence of multicast terrestrial communication systems with area optimized channel assignments. In Proceedings of the 2008 Third International Conference on Communications and Networking in China, Hangzhou, China, 25–27 August 2008; pp. 1292–1296. [Google Scholar] [CrossRef]
  76. Liang, Y.; Tan, J.; Jia, H.; Zhang, J.; Zhao, L. Realizing Intelligent Spectrum Management for Integrated Satellite and Terrestrial Networks. J. Commun. Inf. Netw. 2021, 6, 32–43. [Google Scholar]
  77. Noh, G.; Chung, H.; Kim, I. Outage Analysis for Terrestrial-Satellite Spectrum Sharing. IEEE Commun. Lett. 2020, 24, 2280–2284. [Google Scholar] [CrossRef]
  78. Yan, Y.; An, K.; Zhang, B.; Zhu, W.; Ding, G.; Guo, D. Outage-Constrained Robust Multigroup Multicast Beamforming for Satellite-Based Internet of Things Coexisting with Terrestrial Networks. IEEE Internet Things J. 2021, 8, 8159–8172. [Google Scholar]
  79. Guidolin, F.; Nekovee, M.; Badia, L.; Zorzi, M. A study on the coexistence of fixed satellite service and cellular networks in a mmWave scenario. In Proceedings of the 2015 IEEE International Conference on Communications (ICC), London, UK, 8–12 September 2015; pp. 2444–2449. [Google Scholar] [CrossRef]
  80. Castellanos, G.; Teuta, G. Coexistence for LTE-Advanced and FSS Services in the 3.5 GHz Band in Colombia. In Proceedings of the 10th Advanced Satellite Multimedia Systems Conference and the 16th Signal Processing for Space Communications Workshop (ASMS/SPSC), Graz, Austria, 20–21 October 2020. [Google Scholar]
  81. Meng, X.; Zhong, L.; Zhou, D.; Yang, D. Co-channel coexistence analysis between 5G iot system and fixed-satellite service at 40 GHz. Wirel. Commun. Mob. Comput. 2019, 2019, 9790219. [Google Scholar] [CrossRef]
  82. Hassan, W.A.; Jo, H.S.; Tharek, A.R. The Feasibility of Coexistence between 5G and Existing Services in the IMT-2020 Candidate Bands in Malaysia. IEEE Access 2017, 5, 14867–14888. [Google Scholar] [CrossRef]
  83. Alexandre, L.; Veiga, L.; Linhares, A.; Moreira, J.; Abreu, M.; Sodré Junior, A. Coexistence Analysis Between 5G NR and TVRO in C-Band. J. Commun. Inf. Syst. 2020, 35, 198–202. [Google Scholar] [CrossRef]
  84. Hattab, G.; Moorut, P.; Visotsky, E.; Cudak, M.; Ghosh, A. Interference analysis of the coexistence of 5G cellular networks with satellite earth stations in 3.7–4.2 GHz. In Proceedings of the 2018 IEEE International Conference on Communications Workshops (ICC Workshops), Kansas City, MO, USA, 20–24 May 2018. [Google Scholar] [CrossRef]
  85. Qian, J.; Liu, R.; Huang, Y.; Luo, Y.; Huang, Y.; Tian, F. MIMO Radar and MIMO Communication Spectrum Sharing with Interference Mitigation. In Proceedings of the 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT), Harbin, China, 20–22 January 2019; Volume 1, pp. 562–567. [Google Scholar] [CrossRef]
  86. Alland, S.; Stark, W.; Ali, M.; Hegde, M. Interference in Automotive Radar Systems. IEEE Signal Process. Mag. 2019, 36, 45–59. [Google Scholar]
  87. Rock, J.; Toth, M.; Meissner, P.; Pernkopf, F. Deep interference mitigation and denoising of real-world FMCW radar signals. In Proceedings of the 2020 IEEE International Radar Conference (RADAR), Florence, Italy, 28–30 April 2020; pp. 624–629. [Google Scholar] [CrossRef]
  88. Azari, M.M.; Rosas, F.; Chiumento, A.; Pollin, S. Coexistence of Terrestrial and Aerial Users in Cellular Networks. In Proceedings of the 2017 IEEE Globecom Workshops (GC Wkshps), Singapore, 4–8 December 2018; pp. 1–6. [Google Scholar] [CrossRef]
  89. Yrjola, S.; Huuhka, E.; Talmola, P.; Knuutila, T. Coexistence of Digital Terrestrial Television and 4G LTE Mobile Network Utilizing Supplemental Downlink Concept: A Real Case Study. IEEE Trans. Veh. Technol. 2017, 66, 5422–5434. [Google Scholar] [CrossRef]
  90. Rudd, R. Coexistence of Terrestrial and Satellite services at 26 GHz. IEEE Trans. Aerosp. Electron. Syst. 2019, 59, 8342–8354. [Google Scholar]
  91. Lagunas, E.; Sharma, S.K.; Maleki, S.; Chatzinotas, S.; Ottersten, B. Resource Allocation for Cognitive Satellite Communications with Incumbent Terrestrial Networks. IEEE Trans. Cogn. Commun. Netw. 2015, 1, 305–317. [Google Scholar] [CrossRef]
  92. Polak, L.; Klozar, L.; Kaller, O.; Sebesta, J.; Slanina, M.; Kratochvil, T. Study of coexistence between indoor LTE femtocell and outdoor-to-indoor DVB-T2-Lite reception in a shared frequency band. Eurasip J. Wirel. Commun. Netw. 2015, 2015, 114. [Google Scholar] [CrossRef]
  93. Joshi, S.; Mallik, R.K. Coverage Probability Analysis in a Device-to-Device Network: Interference Functional and Laplace Transform Based Approach. IEEE Commun. Lett. 2019, 23, 466–469. [Google Scholar] [CrossRef]
  94. Parveen, N.; Abdullah, K.; Islam, M.R.; Abbasi, M.A.K. Performance Analysis Antenna Diversity Technique with Wavelet Transform Using Array Gain for Millimeter Wave Communication System. Electronics 2022, 11, 2626. [Google Scholar] [CrossRef]
  95. Ahmad, I.; Hussain, S.; Mahmood, S.N.; Mostafa, H.; Alkhayyat, A.; Marey, M.; Abbas, A.H.; Abdulateef Rashed, Z. Co-Channel Interference Management for Heterogeneous Networks Using Deep Learning Approach. Information 2023, 14, 139. [Google Scholar] [CrossRef]
  96. Wang, W.; Qi, N.; Jia, L.; Li, C.; Tsiftsis, T.A.; Wang, M. Energy-Efficient UAV-Relaying 5G/6G Spectrum Sharing Networks: Interference Coordination with Power Management and Trajectory Design. IEEE Open J. Commun. Soc. 2022, 3, 1672–1687. [Google Scholar] [CrossRef]
  97. Kim, H.; Kim, J.; Hong, D. Dynamic TDD Systems for 5G and Beyond: A Survey of Cross-Link Interference Mitigation. IEEE Commun. Surv. Tutor. 2020, 22, 2315–2348. [Google Scholar] [CrossRef]
  98. Aydogdu, C.; Keskin, M.F.; Garcia, N.; Wymeersch, H.; Bliss, D.W. RadChat: Spectrum Sharing for Automotive Radar Interference Mitigation. IEEE Trans. Intell. Transp. Syst. 2021, 22, 416–429. [Google Scholar] [CrossRef]
  99. Pang, X.; Mei, W.; Zhao, N.; Zhang, R. Intelligent Reflecting Surface Assisted Interference Mitigation for Cellular-Connected UAV. IEEE Wirel. Commun. Lett. 2022, 11, 1708–1712. [Google Scholar] [CrossRef]
  100. Baig, M.U.; Host-Madsen, A.; Nosratinia, A. Discrete Modulation for Interference Mitigation. IEEE Trans. Inf. Theory 2020, 66, 3026–3039. [Google Scholar] [CrossRef]
  101. Ayoughi, S.A.; Yu, W. Interference mitigation via relaying. IEEE Trans. Inf. Theory 2019, 65, 1137–1152. [Google Scholar] [CrossRef]
  102. Baba, T.; Mustapha, N.A.C.; Hasbullah, N.F. A review on techniques and modelling methodologies used for checking electromagnetic interference in integrated circuits. Indones. J. Electr. Eng. Comput. Sci. 2022, 25, 796–804. [Google Scholar] [CrossRef]
  103. Batra, A.; Zeidler, J.R. Narrowband interference mitigation in OFDM systems. In Proceedings of the MILCOM 2008—2008 IEEE Military Communications Conference, San Diego, CA, USA, 16–19 November 2008. [Google Scholar] [CrossRef]
  104. Saha, R.K. On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G. Energies 2020, 13, 748. [Google Scholar] [CrossRef]
  105. Sarowa, S.; Singh, H.; Agrawal, S.; Sohi, B.S. Design of a novel hybrid intercarrier interference mitigation technique through wavelet implication in an OFDM system. Digit. Commun. Netw. 2018, 4, 258–263. [Google Scholar] [CrossRef]
  106. Lim, J.W.; Jo, H.S.; Yoon, H.G.; Yook, J.G. Interference mitigation technique for the sharing between IMT-advanced and fixed satellite service. J. Commun. Netw. 2007, 9, 159–166. [Google Scholar] [CrossRef]
  107. Jo, S.; Yang, W.; Choi, H.K.; Noh, E.; Jo, H.S.; Park, J. Deep Q-Learning-Based Transmission Power Control of a High Altitude Platform Station with Spectrum Sharing. Sensors 2022, 22, 1630. [Google Scholar] [CrossRef]
  108. Ye, N.; Miao, S.; Pan, J.; Xiang, Y.; Mumtaz, S. Dancing with Chains: Spaceborne Distributed Multi-User Detection under Inter-Satellite Link Constraints. IEEE J. Sel. Top. Signal Process. 2025, 1, 1–16. [Google Scholar]
  109. Yang, J.; Zhang, Y.; Zhang, D.; Hong, T.; Liu, Q.; Sun, D.; Dong, A.; Lv, S. Highly Selective Filter for Suppressing Interference of 5G Signals to C-band Satellite Receiver. In Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 15–19 June 2020; pp. 24–27. [Google Scholar] [CrossRef]
  110. Soltanaghai, E. Perceiving the World with Pervasive Wireless Communication Infrastructures. In Proceedings of the 2022 14th International Conference on Communication Systems & NETworkS (COMSNETS), Bangalore, India, 4–8 January 2022; pp. 558–564. [Google Scholar] [CrossRef]
  111. Uzunidis, D.; Kosmatos, E.; Matrakidis, C.; Stavdas, A.; Lord, A.; Uzunidis, D. Strategies for Upgrading an Operator’s Backbone Network beyond the C-Band: Towards Multi-Band Optical Networks. IEEE Photonics J. 2021, 13, 7200118. [Google Scholar] [CrossRef]
  112. O’Mahony, G.D.; Curran, J.T.; Harris, P.J.; Murphy, C.C. Interference and intrusion in wireless sensor networks. IEEE Aerosp. Electron. Syst. Mag. 2020, 35, 4–16. [Google Scholar] [CrossRef]
  113. Hweesa, N.L.M.; Zerek, A.R.; Daeri, A.M. Investigation the Performance Effect of Adjacent and Co-Channel Interferences on AWGN and Rayleigh Channels Using16-QAM Modulation. In Proceedings of the 2020 17th International Multi-Conference on Systems, Signals & Devices (SSD), Sfax, Tunisia, 20–23 July 2020; pp. 1052–1057. [Google Scholar] [CrossRef]
  114. Hu, J.; Wu, J.; Ai, X.; Liu, N. Coordinated Energy Management of Prosumers in a Distribution System Considering Network Congestion. IEEE Trans. Smart Grid 2021, 12, 468–478. [Google Scholar] [CrossRef]
  115. Feng, M.; Xu, H. MSNET-Blockchain: A New Framework for Securing Mobile Satellite Communication Network. In Proceedings of the 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Boston, MA, USA, 10–13 June 2019; pp. 1–9. [Google Scholar] [CrossRef]
  116. Kassab, R.; Munari, A.; Clazzer, F.; Simeone, O. Grant-Free Coexistence of Critical and Noncritical IoT Services in Two-Hop Satellite and Terrestrial Networks. IEEE Internet Things J. 2022, 9, 14829–14843. [Google Scholar] [CrossRef]
  117. Angueira, P.; Val, I.; Montalban, J.; Seijo, O.; Iradier, E.; Fontaneda, P.S.; Fanari, L.; Arriola, A. A Survey of Physical Layer Techniques for Secure Wireless Communications in Industry. IEEE Commun. Surv. Tutor. 2022, 24, 810–838. [Google Scholar] [CrossRef]
  118. Chen, S.; Sun, S.; Kang, S. System integration of terrestrial mobile communication and satellite communication-The trends, challenges and key technologies in B5G and 6G. China Commun. 2020, 17, 156–171. [Google Scholar] [CrossRef]
  119. Yuan, X.; Zhang, Y.J.A.; Shi, Y.; Yan, W.; Liu, H. Reconfigurable-Intelligent-Surface Empowered Wireless Communications: Challenges and Opportunities. IEEE Wirel. Commun. 2021, 28, 136–143. [Google Scholar] [CrossRef]
  120. Niknam, S.; Dhillon, H.S.; Reed, J.H. Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges. IEEE Commun. Mag. 2020, 58, 46–51. [Google Scholar] [CrossRef]
  121. Chen, J.; Wu, Q.; Xu, Y.; Qi, N.; Fang, T.; Liu, D. Spectrum Allocation for Task-Driven UAV Communication Networks Exploiting Game Theory. IEEE Wirel. Commun. 2021, 28, 174–181. [Google Scholar] [CrossRef]
  122. Chen, J.; Wu, Q.; Xu, Y.; Qi, N.; Guan, X.; Zhang, Y.; Xue, Z. Joint Task Assignment and Spectrum Allocation in Heterogeneous UAV Communication Networks: A Coalition Formation Game-Theoretic Approach. IEEE Trans. Wirel. Commun. 2021, 20, 440–452. [Google Scholar] [CrossRef]
  123. Omar, A.R.S.; Qwakneh, M.M.; Alhammami, K.A.; Faouri, Y.S. Triple Split Ring Resonators Metamaterial for Frequency Agility. In Proceedings of the 2020 IEEE 5th International Symposium on Telecommunication Technologies (ISTT), Shah Alam, Malaysia, 9–11 November 2020; pp. 61–64. [Google Scholar] [CrossRef]
  124. Zhang, Y.; Zhao, Z.; Zheng, S.; Qiang, F. Intelligent Anti-Jamming decision with Continuous Action and State in Bivariate Frequency Agility Communication System. IEEE Trans. Cogn. Commun. Netw. 2023, 9, 1579–1595. [Google Scholar] [CrossRef]
  125. Zhang, H.; Yang, N.; Huangfu, W.; Long, K.; Leung, V.C.M. Power Control Based on Deep Reinforcement Learning for Spectrum Sharing. IEEE Trans. Wirel. Commun. 2020, 19, 4209–4219. [Google Scholar] [CrossRef]
  126. Zhang, T.; Mao, S. Energy-Efficient Power Control in Wireless Networks with Spatial Deep Neural Networks. IEEE Trans. Cogn. Commun. Netw. 2020, 6, 111–124. [Google Scholar] [CrossRef]
  127. Dai, L.; Renzo, M.D.; Chae, C.B.; Hanzo, L.; Wang, B.; Wang, M.; Yang, X.; Tan, J.; Bi, S.; Xu, S.; et al. Reconfigurable Intelligent Surface-Based Wireless Communications: Antenna Design, Prototyping, and Experimental Results. IEEE Access 2020, 8, 45913–45923. [Google Scholar] [CrossRef]
  128. Hu, J.; Zhang, H.; Song, L.; Han, Z.; Poor, H.V. Reinforcement Learning for a Cellular Internet of UAVs: Protocol Design, Trajectory Control, and Resource Management. IEEE Wirel. Commun. 2020, 27, 116–123. [Google Scholar] [CrossRef]
  129. Yang, Y.; Zheng, B.; Zhang, S.; Zhang, R. Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization. IEEE Trans. Commun. 2020, 68, 4522–4535. [Google Scholar] [CrossRef]
Telecom 06 00023 g001
Figure 1. Types of wireless networks.
Figure 1. Types of wireless networks.
Telecom 06 00023 g001
Telecom 06 00023 g002
Figure 2. General architecture for cellular networks [31].
Figure 2. General architecture for cellular networks [31].
Telecom 06 00023 g002
Telecom 06 00023 g003
Figure 3. Example of a LAN for an IOT system [37].
Figure 3. Example of a LAN for an IOT system [37].
Telecom 06 00023 g003
Telecom 06 00023 g005
Figure 5. Coexistence of communication networks.
Figure 5. Coexistence of communication networks.
Telecom 06 00023 g005
Telecom 06 00023 g006
Figure 6. CNN architecture for denoising.
Figure 6. CNN architecture for denoising.
Telecom 06 00023 g006
Telecom 06 00023 g007
Figure 7. Coexistence of the 5G signal and satellite services.
Figure 7. Coexistence of the 5G signal and satellite services.
Telecom 06 00023 g007
Telecom 06 00023 g008
Figure 8. Base station coverage and coexistence.
Figure 8. Base station coverage and coexistence.
Telecom 06 00023 g008
Telecom 06 00023 g009
Figure 9. Bar graph with interference reduction.
Figure 9. Bar graph with interference reduction.
Telecom 06 00023 g009
Table 1. Overview of coexistence with the References.
Table 1. Overview of coexistence with the References.
CoexistenceReferences
Coexistence of 5G system and fixed satellite service[8,71,72,79,80,81,82,83,84,85]
Coexistence of radar and communication systems [59,63,64,65,66,86,87,88,89,90]
Coexistence of terrestrial and satellite services [75,76,77,78,91]
Table 2. Comparison of the coexistence and interference mitigation techniques.
Table 2. Comparison of the coexistence and interference mitigation techniques.
Proposed Technique/AlgorithmAdvantagesLimitationsReferences
Deep interference mitigation and denoise real-world FMCW radar signalsCNN was usedTo evaluate and optimize based on real data[9]
Co-channel coexistence analysis between 5G IOT system and fixed satellite service at 40 GHzParameters considered are height of station, distance, and antenna patternsSpecific to high frequency of 40 GHz[18]
Interference mitigation using adaptive beamforming with the RLS algorithmMinimum distance to mitigate the interferenceOnly the distance parameter is considered[72]
Spread spectrumDistributes signals across a wider frequency band to minimize interference impactMay require additional bandwidth and resources[84]
BeamformingCreates a focused signal in a specific direction using multiple antennasRequires advanced hardware and spatial processing capabilities[85]
Frequency hoppingMitigates interference by rapidly switching frequenciesSusceptible to frequency hopping jammers[100]
Interference mitigation via relayingA relay channel with correlation is usedRequires a large number of antennas, assumes optimal quantization noise covariance, may not be practical.[101]
BICM OFDM systemMinimize interference using a bit-interleaved coded systemEffective in the absence of narrowband interference[103]
Spectrum sharingExplore spectrum sharing techniques for multiple systemsMay require complex interference management schemes[104]
Hybrid techniquesCombine Maximum Likelihood Estimation and self-cancellationResults are dependent on modulation and data rates[105]
Null steering MU-MIMO SDMAEmploys null steering MU-MIMO SDMA for interference mitigationRequires knowledge of victim FSS ES direction angles[106]
Highly selective filter for suppressing interference of 5G signals at C-band satellite receiverFilters are used to provide less lossParameters are not considered[109]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Parveen, N.; Abdullah, K.; Badron, K.; Javed, Y.; Khan, Z.I. Coexistence in Wireless Networks: Challenges and Opportunities. Telecom 2025, 6, 23. https://doi.org/10.3390/telecom6020023

AMA Style

Parveen N, Abdullah K, Badron K, Javed Y, Khan ZI. Coexistence in Wireless Networks: Challenges and Opportunities. Telecom. 2025; 6(2):23. https://doi.org/10.3390/telecom6020023

Chicago/Turabian Style

Parveen, Nagma, Khaizuran Abdullah, Khairayu Badron, Yasir Javed, and Zafar Iqbal Khan. 2025. "Coexistence in Wireless Networks: Challenges and Opportunities" Telecom 6, no. 2: 23. https://doi.org/10.3390/telecom6020023

APA Style

Parveen, N., Abdullah, K., Badron, K., Javed, Y., & Khan, Z. I. (2025). Coexistence in Wireless Networks: Challenges and Opportunities. Telecom, 6(2), 23. https://doi.org/10.3390/telecom6020023

Article Metrics

Back to TopTop