Search Results (56)

Cooperation between satellite and mobile network operators enables the integration of non-terrestrial satellite networks (NTNs) with terrestrial networks (TNs), allowing users to seamlessly switch from Mobile Network Operators (MNOs) to Satellite Network Operators (SNOs) in areas with limited MNO coverage or during high-speed travel. However, mobility issues, such as connection failures, the ping-pong effect, and high interruption times, can occur during these transitions. This paper examines various cooperation models, including roaming and handover protocols, and proposes key enhancements to these models to reduce service interruption time. It also discusses the use of machine learning (ML) techniques to reduce the above mobility issue and increase the success rate of cooperation among network operators. Full article

Efficient communication infrastructure is essential for Unmanned Aircraft Systems (UASs) operating beyond visual line of sight (BVLOS). Both terrestrial and non-terrestrial networks struggle with coverage gaps and are susceptible to disruptions. This paper analyzes integrated terrestrial–non-terrestrial network (TN-NTN) architectures for UAS communications in 6G, focusing on three connectivity methods: terrestrial connectivity, indirect satellite connectivity, and direct UAS–satellite links. We provide the assessment of different connectivity options. Major challenges are discussed, including antenna limitations, reliability, channel modeling, and regulatory alignment. Full article

Sixth-generation (6G) networks are expected to provide ubiquitous connectivity, AI-native orchestration, and seamless integration across terrestrial and non-terrestrial infrastructures. However, these capabilities introduce new privacy challenges related to the classification and protection of personal, quasi-personal, and non-personal data in complex data-driven environments. This paper presents a systematic review of 78 peer-reviewed studies published between 2019 and 2025. Following a PRISMA-based methodology, this review analyzes privacy-enhancing technologies (PETs), regulatory compliance frameworks, and architectural patterns for privacy preservation in 6G networks. The findings show that differential privacy (DP) and federated learning (FL) dominate current research, accounting for nearly 52% of the reviewed studies. Blockchain auditing and zero-knowledge proofs (ZKPs) collectively represent approximately 30%, while the remaining mechanisms, including physical-layer security (PLS), trusted execution environments (TEEs), homomorphic encryption (HE), secure multi-party computation (SMPC), and anonymization, account for roughly 18%. These mechanisms exhibit varying levels of privacy strength, utility preservation, latency, and energy cost. At the same time, evolving regulatory frameworks, including GDPR, PDPL, CCPA/CPRA, LGPD, and PIPL, increasingly extend privacy obligations to quasi-personal and aggregated data. Building on these findings, this paper proposes a unified taxonomy that clarifies the boundary between personal and non-personal data. It also provides a cross-layer mapping between PETs and compliance requirements across the Core/SBA, RAN, Edge/MEC, and NTN layers. Finally, this paper presents a forward-looking roadmap for 2025–2030, highlighting hybrid PET pipelines, post-quantum auditability, and AI-driven compliance automation as key directions for privacy-preserving 6G standardization. Full article

The rapid growth of Internet of Things (IoT) deployments in hybrid terrestrial/non-terrestrial networks (TN/NTN) faces a major bottleneck: the verbosity of standard data formats like JSON. This is critical for large-scale M2M systems tracking and monitoring multimodal dry containers, where devices must comply with the strict message-size limits of commercial satellite IoT (around 160 bytes per message). We present a comparative evaluation of four device-friendly binary serialization protocols (CBOR, MessagePack, Protocol Buffers, and a custom Struct+Zlib hybrid) targeted at battery-powered microcontrollers. Using a horizontally scalable testbed with up to 2000 concurrent devices and the oneM2M standard framework, we assess payload efficiency, throughput, latency, and maintainability. Only Protocol Buffers and Struct+Zlib meet NTN message-size limits, with Protocol Buffers providing the best trade-off between performance and long-term maintainability. Real-world validation with the Astrocast LEO satellite platform and the oneM2M Mobius framework confirms these results. Cost analysis suggests potential savings exceeding €62,000 per month for a 10,000-device maritime fleet, demonstrating both technical feasibility and economic viability. This study provides a methodological framework for designing efficient, scalable IoT systems in hybrid TN/NTN networks, offering practical guidance for global container tracking and monitoring deployments. Full article

Satellite communication protocols are increasingly optimized in software-defined, multiorbital networks that combine broadband satellite systems, non-terrestrial 5G components, and inter-satellite transport. This review examines intelligent optimization across the physical, medium-access, network, and transport layers, with emphasis on what can be measured, what can be controlled, and what can be safely deployed under standards and operational constraints. This paper first positions the literature across DVB/ETSI, 3GPP NTN, CCSDS/DTN, LEO routing, and recent AI and digital-twin research. It then links standards-defined control surfaces to layer-specific measurements, feedback delays, and safety constraints and compares optimization families using deployment-relevant criteria such as observability, runtime predictability, verification burden, and robustness. The review argues that the central challenge is not only a simulation-to-reality gap but an evidence gap between experimental gains and operational trust. To address this gap, this paper analyzes delayed observability, rare events, bounded onboard compute, action surface mismatch, certification, and security; formalizes a generic constrained optimization problem with delayed observations and standards-compliant actions; and proposes a digital-twin-assisted research methodology supported by a worked beam-hopping example. The main conclusion is that future progress is most likely to come from hybrid, standards-compliant, and twin-assisted optimization methods whose performance claims are tied to calibration, traceability, and explicit rollback logic. Full article

This work proposes a fiber-tethered UAV-enabled adaptive aerial passive optical network (AA-PON) framework for rapid extension of optical access and backhaul in hard-to-reach or temporarily disrupted environments. The proposed architecture supports two distinct operating modes: (i) an aerial base station (ABS) mode for wide-area service extension and (ii) a ground-to-air-to-ground (G2A2G) mode for targeted high-speed optical bridging to ground terminal units. Unlike conventional UAV relay approaches, the proposed framework is developed as a network-level optical access/backhaul architecture based on tether-assisted aerial nodes and reconfigurable optical topology formation. In the ABS mode, representative Bus, Ring, and Star topologies are analyzed to evaluate serviceability, outage, deployment latency, and scalability as the number of UAV nodes increases. In the G2A2G mode, a stochastic-geometry-based analysis is used to characterize blockage-limited optical serviceability and infrastructure-density trade-offs. To complement the analytical study, a 2 Gb/s proof-of-concept FSO link between two fiber-tethered UAVs is demonstrated as an initial feasibility validation of the end-to-end optical link. The results show that the proposed AA-PON provides a flexible aerial optical networking framework that combines reconfigurable topology support with localized high-capacity optical access extension. Full article

The increasing demand for global connectivity has accelerated the integration of non-terrestrial networks (NTNs), particularly low Earth orbit (LEO) satellite constellations, into next-generation position navigation and time (PNT) systems. While LEO-based PNT offers low-latency and high-accuracy potential, challenges such as high path loss and limited ground-level signal diversity remain. Reconfigurable intelligent surfaces (RISs) have emerged as a cost-effective solution to enhance localization performance by providing controllable reflections with minimal infrastructure. Building on prior work in single-RIS NTN scenarios, this paper investigates RIS-aided localization in a single-LEO PNT setting with multiple RISs. We introduce a detailed signal model and multi-stage processing framework that estimates both the satellite and RIS-assisted paths, enabling accurate receiver localization. Simulations assess the trade-offs in coverage and accuracy, providing insights into the feasibility and optimization of RIS-assisted NTN PNT solutions as a complementary alternative to global navigation satellite system (GNSS). Full article

Accurate time and frequency acquisition is essential for deploying Orthogonal Time–Frequency Space (OTFS) modulation in non-terrestrial networks (NTNs), where severe Doppler shifts and low-SNR conditions are common. We propose a practical synchronization method that inserts an m-sequence-based pilot (illustrated using the 5G NR PSS) periodically in the delay–Doppler grid. Leveraging OTFS mapping properties, the method enables robust matched-filter detection for joint coarse time and frequency acquisition and continuous phase-drift tracking without increasing transmission redundancy. Numerical simulations show that the proposed method achieves a slightly lower PAPR and approximately a 3 dB improvement in detection threshold compared to a recent practical baseline. The algorithm is suitable for 5G/6G NTN links such as LEO constellations and operates reliably at low and negative SNR values. Full article

The deployment of low Earth orbit (LEO) satellite mega-constellations enables global broadband access, but their high orbital velocity demands frequent handover decisions that critically impact service continuity. Conventional strategies that maximize instantaneous signal quality often trigger excessive handovers, while stability-focused approaches may sacrifice link performance. In this paper, we propose the Hybrid Handover Strategy (HHS), a low-complexity algorithm that addresses this trade-off. The HHS utilizes a multi-attribute utility function that integrates the signal-to-interference-plus-noise ratio (SINR), satellite elevation angle, and network load with a novel logistic-decay stability bonus mechanism. We provide a formal mathematical analysis of the algorithm’s stability and performance trade-offs. To ensure industrial relevance, the strategy is validated using a high-fidelity simulator driven by real-world two-line element (TLE) data from the Starlink constellation. Results demonstrate that the HHS reduces the handover frequency by 64% compared to SINR-based benchmarks while maintaining service availability of 90.2%. The proposed algorithm delivers these improvements with significantly smaller computational overhead than machine learning approaches, making it suitable for resource-constrained on-board processing and ground terminals. Full article

In the Non-Terrestrial Networks (NTNs) formed by the integration of fifth-generation mobile communication systems and Low Earth Orbit (LEO) satellites, Doppler frequency offsets can severely degrade the performance of OFDM signal detection. Particularly for the Physical Uplink Control Channel (PUCCH), conventional detection algorithms suffer significant performance degradation due to the difficulty of accurately estimating and compensating for Doppler frequency offsets at the receiver. Consequently, achieving robust signal detection under conditions with high Doppler frequency offsets becomes particularly critical. To address this challenge, we propose a maximum-likelihood detection algorithm robust to both Doppler frequency and time offsets. In the first step, we derive the frequency-offset matrix, which directly affects the detection peaks. Subsequently, we develop a novel two-dimensional search algorithm that jointly considers UCI and frequency offset. Finally, based on the sparse characteristics of the dominant elements in the frequency offset matrix, we simplify the implementation of the frequency offset matrix, reducing computational complexity to 9% of the original algorithm while achieving negligible performance loss. This approach satisfies the requirements for onboard implementation. Full article

Limited terrestrial network coverage in rural and remote areas constitutes a significant barrier to the digital transformation of the agricultural sector. Smart and precision farming applications, ranging from conventional environmental monitoring systems to advanced Digital Twin solutions, rely on the reliable transmission of sensor data, images, and video streams from geographically isolated farms. Such data-intensive services cannot be effectively supported without a robust communication infrastructure. Non-Terrestrial Networks (NTNs), particularly satellite systems, offer both narrowband and broadband connectivity, enabling the transmission of low-rate sensor measurements, as well as high-throughput multimedia data from the field. This paper presents an experimental performance evaluation of two satellite backhauling solutions: a Geostationary Earth Orbit (GEO) system provided by SES and a Low Earth Orbit (LEO) system from Starlink. The networks were first deployed and tested in a laboratory environment and subsequently validated in an operational agricultural field setting. Their performance is benchmarked against a terrestrial cellular network to assess their suitability for supporting advanced agricultural applications. The performance assessment results indicate that both satellite backhauling solutions are reliable and capable of meeting the bandwidth and latency requirements of delay-tolerant agricultural applications. In addition to the technical evaluation, this work presents a cost–benefit analysis that further underscores the advantages of NTN-based solutions. Despite higher initial expenditures, they provide extended coverage in remote areas and enable cost sharing across multiple users, improving overall economic viability. Full article

The anticipated transition from 5G to 6G is driven not by incremental performance demands but by a widening mismatch between emerging application requirements and the capabilities of existing cellular systems. Despite rapid progress across 3GPP Releases 15–20, the current literature lacks a unified analysis that connects these standardization milestones to the concrete technical gaps that 6G must resolve. This study addresses this omission through a cross-release, application-driven review that traces how the evolution from enhanced mobile broadband to intelligent, sensing integrated networks lays the foundation for three core 6G service pillars: immersive communication (IC), everything connected (EC), and high-precision positioning. By examining use cases such as holographic telepresence, cooperative drone swarms, and large-scale Extended Reality (XR) ecosystems, this study exposes the limitations of today’s spectrum strategies, network architectures, and device capabilities and identifies the performance thresholds of Tbps-level throughput, sub-10 cm localization, sub-ms latency, and 10 M/km² device density that next-generation systems must achieve. The novelty of this review lies in its synthesis of 3GPP advancements in XR, the non-terrestrial network (NTN), RedCap, ambient Internet of Things (IoT), and consideration of sustainability into a cohesive key performance indicator (KPI) framework that links future services to the required architectural and protocol innovations, including AI-native design and sub-THz operation. Positioned against global initiatives such as Hexa-X and the Next G Alliance, this paper argues that 6G represents a fundamental redesign of wireless communication advancement in 5G, driven by intelligence, adaptability, and long-term energy efficiency to satisfy diverse uses cases and requirements. Full article

With the rapid development of low-Earth-orbit (LEO) satellite communications, multi-beam precoding has emerged as a key technology for improving spectrum efficiency. However, the long propagation delay and large Doppler frequency offset pose significant challenges to existing precoding techniques. To address this issue, this paper investigates downlink precoding design for multi-beam LEO satellite communications. First, the downlink channel and signal models are established. Then, we reveal that traditional zero-forcing (ZF), regularized zero-forcing (RZF), and minimum mean square error (MMSE) precoding schemes all require the satellite transmitter to acquire the instantaneous channel state information (iCSI) of all users, which is challenging to obtain in satellite communication systems. Subsequently, we propose a downlink precoding design based on statistical channel state information (sCSI) and derive closed-form solutions for statistical-ZF, statistical-RZF, and statistical-MMSE precoding. Furthermore, we propose that sCSI can be computed using the positions of the satellite and users, which reduces the system overhead and complexity of sCSI acquisition. Monte Carlo simulations under the 3GPP non-terrestrial network (NTN) channel model are employed to verify the performance of the proposed method. The simulation results show that the proposed method achieves sum-rate performance comparable to that of iCSI-based schemes and the optimal transmission performance based on sum-rate maximization. In addition, the proposed method significantly reduces the computational complexity of the satellite payload and the system feedback overhead. Full article

Sixth-generation (6G) wireless systems aim to integrate terrestrial, aerial, and satellite networks to support large-scale remote sensing and service delivery. In such non-terrestrial networks (NTNs), channels change quickly and the multi-tier architecture is heterogeneous, which makes real-time channel state acquisition and cooperative resource scheduling difficult. This paper proposes an FMA-MADDPG framework that combines a channel prediction module with a constraint-based multi-agent deep deterministic policy gradient scheme. The Fusion of Mamba and Attention (FMA) predictor uses a Mamba state-space backbone and a multi-head self-attention block to learn both long-term channel evolution and short-term fluctuations, and forecasts future CSI. The predicted channel information is added to the agents’ observations so that scheduling decisions can take expected channel variations into account. A constraint-based reward is also designed, with explicit performance thresholds and anti-idle penalties, to encourage fairness, avoid free-riding, and promote cooperation among heterogeneous agents. In a representative NTN uplink scenario, the proposed method achieves higher total reward, efficiency, load balance, and cooperation than several DRL baselines, with relative gains around 10–20% on key metrics. These results indicate that prediction-aware cooperative reinforcement learning is a useful approach for resource optimization in future 6G NTN systems. Full article

International organizations such as the 3rd Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU) regard non-terrestrial networks (NTNs) as an essential component of the sixth-generation (6G) mobile communication technology and have advanced relevant standardization efforts. Low Earth orbit (LEO) satellite communication (SatCom) constitutes a key part of NTNs, and efficient uplink random access (RA) is crucial for establishing initial connections in LEO SatCom systems. However, the long propagation delay and wide coverage of LEO satellites substantially increase access latency and collision probability due to the limited number of beams and their constrained coverage areas. In addition, the highly non-uniform spatial distribution of user equipment (UE) further aggravates access inefficiency. To this end, this paper investigates joint beam position grouping and RA channel (RACH) occasions (ROs) allocation (JBPGRA) for LEO SatCom systems. Specifically, we develop a system model for RA under beam hopping and identify the key factors that influence RA performance. Furthermore, we derive expressions for both the instantaneous signal-to-interference-plus-noise ratio (SINR) and the average SINR under a given non-uniform UE spatial distribution. Building on this analysis, the JBPGRA problem is formulated as an integer linear programming problem that seeks to maximize RA success while conserving RO resources under non-uniform UE distribution. To achieve a practical solution, we propose an efficient JBPGRA algorithm composed of beam position classification, sparse beam position grouping, and RO allocation modules. Simulation results demonstrate that, under the same UE density, the proposed JBPGRA scheme achieves over 29% higher access success rate in dense beam positions compared with the uniform baseline adopted in existing SatCom systems, while reducing RO consumption by more than 49% and decreasing the number of beam position groups by over 57%. Full article