Global Navigation Satellite Systems (GNSS) provide the backbone of Positioning, Navigation, and Timing (PNT) but remain vulnerable to interference. Low Earth Orbit (LEO) constellations within Fifth-Generation (5G) Non-Terrestrial Networks (NTN) can enhance resilience by jointly supporting communication and navigation. This paper presents the first quantitative analysis of GNSS tracking and navigation message demodulation under a hybrid waveform where a low-power Direct-Sequence Spread Spectrum (DSSS) component is overlaid on an Orthogonal Frequency-Division Multiplexing (OFDM) 5G downlink. We evaluate a minimally modified GNSS receiver that tracks a legacy Global Positioning System (GPS) L1 Coarse/Acquisition (C/A) overlay aligned with 5G frames while treating the 5G waveform as structured interference. Using Monte Carlo simulations under realistic LEO Doppler dynamics, we analyze the Bit Error Rate (BER) of GPS L1 C/A navigation bits and the subframe decoding probability versus Signalto- Interference-plus-Noise Ratio (SINR) for multiple Signalto- Interference Ratios (SIR) and dynamic classes. Results show reliable demodulation across wide SINR ranges for low and medium dynamics, whereas high dynamics impose strict lock limits. These findings confirm the feasibility of Joint Communication and Positioning (JCAP) using a near-legacy GNSS chipset with minimal receiver modifications.

Remote and resource-constrained Internet-of Things (IoT) deployments often lack terrestrial connectivity for task offloading, motivating non-terrestrial networks (NTNs) with onboard multiaccess edge computing (MEC) capabilities. Nevertheless, in the presence of malicious actors, authentication needs to be performed to avoid non-authorized nodes from draining the computing resources of the NTN nodes. As a solution, we propose a four-layer MEC-enabled NTN with unmanned aerial vehicles (UAVs) acting as access nodes, a high altitude platform station (HAPS) acting as coordinator and authenticator, and a constellation of low-Earth orbit satellites (LEOSats) acting as remote MEC servers. We consider a tag-based physical-layer authentication (PLA) scheme to authenticate legitimate users, and formulate a joint task offloading decision and resource allocation for the admitted tasks, which is solved via block coordinate descent. Numerical results show that the PLA scheme is efficient and performs better than the benchmark schemes. We also demonstrate that the proposed scheme is robust against malicious attacks even under relaxed false-alarm constraints.

peer reviewed

This paper examines wireless-powered Internet of Things (IoT) networks involving multiple unmanned aerial vehicles (UAVs) equipped with backscatter and caching technologies to relay and transmit signals. For data communication and energy harvesting (EH), the source transmits information and power to UAVs using the dynamic time splitting (DTS) method. UAVs use harvested energy for passive communication (backscatter) and for active communication (transmitting information) to the destination. The primary objective is to maximize the total throughput by jointly optimizing the DTS ratio, trajectory, and transmission power, leveraging the UAVs’ caching capability. This optimization problem is challenging due to its non-convexity. Therefore, an efficient alternating algorithm using the block coordinate descent (BCD) method is proposed to optimize each variable given the fixed values of the other parameters. By applying the Karush-Kuhn-Tucker (KKT) conditions, we derive a closed-form expression for the optimal DTS ratio, significantly reducing computation time. The optimal values for the other two parameters are determined using the BCD. In order to thoroughly assess the effectiveness of various solutions for the original problem, this paper introduces an approach leveraging a genetic algorithm (GA). The GA in this context employs a one-point crossover method, value mutation, and rank-based selection based on fitness values. Numerical results show that the BCD and GA achieve at least 31% throughput improvement over the benchmarks, with reduced computational time. These findings demonstrate the performance gain and practical feasibility of our solutions in caching-enabled UAV-aided IoT networks.

In this paper, we address the resource allocation problem for task offloading from Internet of Things (IoT) devices to a non-terrestrial network. The proposed architecture contains clusters of IoT devices that can either execute their computing tasks locally or offload them to a dedicated unmanned aerial vehicle (UAV) functioning as a multi-access edge computing (MEC) server. The UAV can process the tasks itself or further offload them to an available high-altitude platform station (HAPS) or to a low-earth orbit (LEO) satellite within line-of-sight for remote computing. We formulate an optimization problem that aims to minimize the weighted sum of the total task-execution delay and the energy consumption of the IoT devices. Due to non-convexity of the problem and the inherent complexity-performance trade-off in optimization algorithms, we propose a set of low-complexity solutions. These include optimal methods based on convex subproblem decomposition and a greedy heuristic guided by convex optimization criteria. The framework jointly optimizes the computing resources and transmission power of IoT devices, the digital precoders and combiners at the UAV, the computing resources at the remote nodes (UAV, HAPS, and LEO), as well as task offloading decisions and subchannel allocation through a one-shot block coordinate descent approach. Simulation results highlight the performance gains of the proposed methods, demonstrating the impact of algorithmic complexity on key system metrics and the benefits of incorporating multiple non-terrestrial nodes compared to architectures lacking such capabilities.

The integration of reconfigurable intelligent surfaces (RIS) and simultaneous wireless information and power transfer (SWIPT) present a promising solution for sustainable and efficient wireless communications in large-scale IoT networks, especially within energy-constrained smart agriculture applications. However, most existing works assume ideal energy harvesting (EH) models and continuous RIS phase shifts that limit their practical relevance. This paper addresses these limitations by proposing a total transmit power minimization framework for a multi-user RIS-assisted MISO power-splitting (PS) SWIPT system. First, a practical logistic non-linear energy harvesting (NL-EH) model is adopted to better reflect the realistic behavior of RF energy conversion circuits. Second, a discrete phase shift (DPS) model with finite quantization levels is employed to account for practical RIS hardware constraints. Third, an alternating optimization algorithm is developed for the resulting non-convex optimization problem through joint optimization of the base station’s beamforming vectors, RIS reflection matrix, and PS ratio. Techniques such as Zero-Forcing (ZF), Semidefinite Relaxation (SDR), and Gaussian randomization (GR) are leveraged to address the associated sub-problems, while an alternate one-dimensional search strategy is used for RIS phase shift optimization. Finally, numerical simulations are conducted to validate the proposed framework in terms of performance and convergence. The results demonstrate robustness under imperfect channel state information (ICSI) and varying system parameters for next-generation IoT-enabled smart farming use-case.

Hierarchical edge-cloud computing-aided Internet of Things (IoT) networks offer low-latency and cost-efficient services to a growing number of data-intensive IoT devices. However, optimizing service placement, which involves determining the most suitable locations within a network to deploy various services, is critical to balancing workloads dynamically and ensuring efficient resource utilization. In this paper, we jointly optimize service placement, edge/cloud cooperation, task offloading, and bandwidth allocation to enhance processing efficiency and response times. The main objective is to minimize both the overall end-to-end latency and the system cost, including service deployment and operational costs. The formulated problem belongs to the class of non-convex mixed-integer nonlinear programming, where finding a feasible solution is already challenging. Towards a stable system, we first transform the original problem into a more tractable form and then decompose it into sub-problems which are solved at different timescales. Combining tools from relaxation and the successive convex approximation method, we develop iterative algorithms to solve these problems efficiently. With an appropriate penalty parameter, the proposed algorithms guarantee convergence to at least a local optimum. We produce extensive numerical results to demonstrate the superior performance of the proposed algorithms over benchmark schemes as well as emphasize the significance of the joint service placement and resource allocation in enhancing system performance and efficiency.

This paper explores the energy efficiency (EE) of integrated sensing and communication (ISAC) systems employing massive multiple-input multiple-output (mMIMO) techniques to leverage spatial beamforming gains for both communication and sensing. We focus on an mMIMO-ISAC system operating in an orthogonal frequency-division multiplexing setting with a uniform planar array, zero-forcing downlink transmission, and mono-static radar sensing to exploit multi-carrier channel diversity. By deriving closed-form expressions for the achievable communication rate and Cramér-Rao bounds (CRBs), we are able to determine the overall EE in closed-form. A power allocation problem is then formulated to maximize the system’s EE by balancing communication and sensing efficiency while satisfying communication rate requirements and CRB constraints. Through a detailed analysis of CRB properties, we reformulate the problem into a more manageable form and leverage Dinkelbach’s and successive convex approximation (SCA) techniques to develop an efficient iterative algorithm. A novel initialization strategy is also proposed to ensure high-quality feasible starting points for the iterative optimization process. Extensive simulations demonstrate the significant performance improvement of the proposed approach over baseline approaches. Results further reveal that as communication spectral efficiency rises, the influence of sensing EE on the overall system EE becomes more pronounced, even in sensing-dominated scenarios. Specifically, in the high ω regime of 2 × 10−3, we observe a 16.7% reduction in overall EE when spectral efficiency increases from 4 to 8 bps/Hz, despite the system being sensing-dominated.

Atmospheric turbulence poses a significant challenge to free-space measurement-device-independent quantum key distribution (FSO MDI-QKD) by inducing polarization decoherence and depolarization, which degrade the secret key rate (SKR). In this paper, we propose a unified depolarizing-dephasing channel model for turbulence-induced polarization decoherence in FSO MDI-QKD. This model consolidates phase perturbations, Gaussian beam spreading, beam drift, aperture truncation, and scintillation into closed-form parameters: depolarization factor, decoherence factor, and detection probability. By mapping turbulence to a von Mises-Fisher/Watson-distributed SU(2) rotation, we derive an analytic SKR expression compatible with existing MDI-QKD security analyses. The model excels in clear, overcast, and hazy weather conditions, offering computational efficiency and experimental verifiability for real-time link adaptation. Numerical simulations, illustrated on a ground-to-satellite free-space link, confirm its accuracy, enabling robust physical layer design for global-scale MDI-QKD networks.

This paper investigates the design of distributed precoding for multi-satellite massive MIMO transmissions. We first conduct a detailed analysis of the transceiver model, in which delay and Doppler precompensation is introduced to ensure coherent transmission. In this analysis, we examine the impact of precompensation errors on the transmission model, emphasize the near-independence of inter-satellite interference, and ultimately derive the received signal model. Based on such signal model, we formulate an approximate expected rate maximization problem that considers both statistical channel state information (sCSI) and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation fails to maintain equivalence in the considered scenario. To address this, we introduce an equivalent covariance decomposition-based WMMSE (CDWMMSE) formulation derived based on channel covariance matrix decomposition. By exploiting the channel characteristics, we develop a low-complexity decomposition method and propose an optimization algorithm. To further reduce computational complexity, we introduce a model-driven scalable deep learning (DL) approach that leverages the equivariance of the mapping from sCSI to the unknown variables in the optimal closed-form solution, enhancing performance through novel dense Transformer network and scaling-invariant loss function design. Simulation results validate the effectiveness and robustness of the proposed method in some practical scenarios. We also demonstrate that the DL approach can adapt to dynamic settings with varying numbers of users and satellites.

This study investigates the synergy between ratesplitting multiple access (RSMA) and simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) as a unified framework to realize ubiquitous, intelligent, and resilient connectivity in future sixth-generation networks, while enhancing both spectral and energy efficiency. Specifically, in the STAR-RIS-assisted multi-user RSMA network under consideration, we develop an intelligent optimization strategy that jointly designs the active beamforming at the transmitter, the allocated transmission rate for the common stream, and the passive beamforming vectors for both transmission and reflection regions of the STAR-RIS, while accounting for transceiver hardware impairments and imperfect channel state information (CSI). In addition, system robustness is ensured by incorporating a bounded channel estimation error model that rigorously reflects CSI imperfections and ensures resilience against worst-case estimation errors. To tackle the highly non-convex problem, we propose an intelligent optimization algorithm that decouples the original problem into two sub-problems, which are then solved iteratively. Firstly, the active beamforming vectors for both the common and private signals are obtained by reformulating the original non-convex problem into a tractable convex semi-definite programming (SDP) framework, leveraging successive convex approximation (SCA) and semi-definite relaxation (SDR) for enhanced computational efficiency. Secondly, the passive beamforming vectors for the transmission and reflection regions of the STAR-RIS are optimized through a convex SDP reformulation by exploiting SCA and SDR techniques. Additionally, when the resulting active or passive beamforming solutions are of higher rank, Gaussian randomization is employed to construct rank-one solutions. Finally, the effectiveness of the proposed optimization strategy is demonstrated through numerical simulations, which reveal significant performance gains over benchmark schemes and confirm rapid convergence.

Mega-constellations of low Earth orbit (LEO) satellites are envisioned to deliver global broadband and direct-to-cell services, requiring seamless handovers (HOs) to ensure uninterrupted connectivity. Conventional break-before-make HO protocols, although supported by inter-satellite links, suffer from beamforming delays due to the high orbital velocities of LEO satellites. To address these limitations, we propose an integrated sensing and communication (ISAC)-assisted HO (ISAC-HO) protocol that enables make-before-break HOs via ISAC-enabled ground terminals (GTs). A novel ISAC design capable of generating a three-dimensional (3D) beampattern under realistic conditions allows GTs to sense approaching LEO satellites without significantly compromising communication with the currently serving LEO satellite. The design problem is highly challenging due to its non-convexity and mixed-integer nature. To tackle these challenges, we propose an approach that leverages Riemannian manifold optimization and closed-form solutions. For multi-GT scenarios, we introduce a multi-agent deep reinforcement learning framework that mitigates sensing collisions and ensures quality-of-service under shared spectrum constraints. Numerical results confirm that the proposed ISAC design significantly improves the communication–sensing trade-off, enables smooth HO, and remains robust in the presence of imperfect channel state information.

6291

This paper presents a comprehensive analysis of the information-energy capacity region for simultaneous lightwave information and power transfer (SLIPT) systems over lognormal fading channels. Unlike conventional studies that primarily focus on additive white Gaussian noise channels, we study the complex impact of lognormal fading, which is prevalent in optical wireless communication systems such as underwater and atmospheric channels. By applying the Smith’s framework to these channels, we demonstrate that the optimal input distribution is discrete, characterized by a finite number of mass points. We further investigate the properties of these mass points, especially at the transition points, to reveal critical insights into the rate-power trade-off inherent in SLIPT systems. Additionally, we introduce a novel cooperative information-energy capacity learning framework, leveraging generative adversarial networks, to effectively estimate and optimize the information-energy capacity region under practical constraints. Numerical results validate our theoretical findings, illustrating the significant influence of channel fading on system performance. The insights and methodologies presented in this work provide a solid foundation for the design and optimization of future SLIPT systems operating in challenging environments.

This paper pioneers the domain of multi-user holographic unmanned aerial vehicle (UAV) communications, establishing a robust foundation for future advancements in next-generation aerial wireless networks. It investigates the joint design of hybrid holographic beamforming and three-dimensional (3D) positioning for a UAV equipped with a reconfigurable holographic surface (RHS), with the objective of maximizing the network’s sum rate. To tackle this inherently complex and non-convex optimization problem, a novel alternating optimization framework is proposed. The solution leverages zero-forcing (ZF) digital beamforming and a gradient ascent strategy to iteratively update the holographic beamforming weights and the UAV’s 3D location, while satisfying key system constraints. This framework is tailored to efficiently navigate the trade-offs between hybrid transceiver design and UAV mobility limitations, ensuring both adaptability and performance scalability. Simulation results confirm that the proposed approach achieves substantial gains in sum rate and system robustness compared to conventional methods, validating its effectiveness under diverse channel and deployment conditions.

This paper investigates a low Earth orbit (LEO) satellite communication system enhanced by an active stacked intelligent metasurface (ASIM), mounted on the backplate of the satellite’s solar panels to efficiently utilize limited onboard space and reduce the main satellite power amplifier requirements. The system serves multiple ground users via rate-splitting multiple access (RSMA) and IoT devices through a symbiotic radio network. Multi-layer sequential processing in the ASIM improves effective channel gains and suppresses inter-user interference, outperforming active RIS and beyond-diagonal RIS designs. Three optimization approaches are evaluated: block coordinate descent with successive convex approximation (BCD-SCA), model-assisted multi-agent constraint soft actor-critic (MA-CSAC), and multi-constraint proximal policy optimization (MCPPO). Simulation results show that BCD-SCA converges fast and stably in convex scenarios without learning, MCPPO achieves rapid initial convergence with moderate stability, and MA-CSAC attains the highest long-term spectral and energy efficiency in large-scale networks. Energy–spectral efficiency trade-offs are analyzed for different ASIM elements, satellite antennas, and transmit power. Overall, the study demonstrates that integrating multi-layer ASIM with suitable optimization algorithms offers a scalable, energy-efficient, and high-performance solution for next-generation LEO satellite communications.

Integrated sensing and communications (ISAC) has emerged as a key enabler for 6G and beyond. However, ISAC systems face significant challenges, including the sensing function that introduces interference and degrades communication performance, as well as high sensing power consumption that reduces overall communication efficiency, particularly in complex urban environments. To address these issues, we propose a reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) ISAC system, where a RIS enhances connectivity for users in localized coverage gaps. We formulate and study two optimization problems: i) maximizing system sum spectral efficiency and ii) maximizing global energy efficiency, by jointly optimizing transmit precoding, subcarrier allocation, and RIS phase shifts under power, quality of service, and sensing accuracy constraints. These problems are classified as mixed-integer nonlinear programs, which are generally difficult to solve optimally. To tackle this, we develop efficient iterative algorithms leveraging successive convex approximation, alternating optimization, Riemannian manifolds, and Dinkelbach’s method to obtain at least locally optimal solutions. Simulation results validate the effectiveness of the proposed designs, demonstrating their superiority over benchmark schemes, achieving up to 40% higher spectral efficiency and up to 60% improvement in energy efficiency compared to conventional overlap and random-phase approaches.

Mobile edge computing (MEC) has emerged as a promising technology to extend the functionality of end-users' wireless devices while prolonging their battery life by offloading computationally intensive tasks to remote edge servers. However, the inherent broadcast nature of wireless transmission during offloading introduces notable security challenges. To address this issue, we propose leveraging intelligent reflecting surface (IRS) technology to enhance physical layer security (PLS). Nevertheless, attaining high PLS for all users in dense networks with multiple malicious terminals is challenging. In this paper, we investigate the physical layer encryption (PLE) to complement the PLS in enabling secure wireless transmission. Since such encryption and decryption processes require computation resources, we aim to optimize the encryption decision, offloading decision, as well as wireless and computing resource allocations. Our objective is to minimize the maximum weighted energy consumption while satisfying practical constraints, including limited computing and wireless resources, fulfilling minimum user rate requirements, and complying with IRS conditions. To tackle the non-convex objective and constraints, we explore the utilization of bisection search and successive convex approximation (SCA) methods. Our numerical results confirm the efficiency of the proposed design in terms of energy consumption and network capacity within a secure MEC network.

The spectral efficiency of sixth-generation (6G) wireless networks is anticipated to experience large improvements through the implementation of non-orthogonal multiple access (NOMA) technology. The integration of short-packet communications (SPC) into NOMA networks enables low-latency operation and high spectral efficiency. The present study investigates the performance of multiple users in a NOMA downlink SPC system operating over an$\alpha - \kappa - \mu$shadowed fading channel. Precise and asymptotic closed-form approximations for the average block error rate (BLER), reliability, throughput, goodput, and overall BLER were derived using approximate Gaussian-Chebyshev quadrature. The analytical results were validated through numerical simulations, providing insights into the impact of fading parameters on system performance. The study's findings indicate that the proposed downlink NOMA SPC system is highly suitable for ultra-reliable and low-latency communications (URLLC), achieving reliability levels of 99.99% for multiple users. The study also determined the optimal transmission bit rate required to maximize throughput and goodput while minimizing the BLER. The proposed downlink NOMA SPC system operating with an$\alpha - \kappa - \mu$shadowed fading channel demonstrates high potential for improving Internet of Things (IoT) network performance over conventional downlink orthogonal multiple access (OMA) approaches. Most of the analysis adopts perfect successive interference cancellation (pSIC) and perfect channel state information (pCSI) as theoretical benchmarks, while additional results explicitly quantify the performance degradation caused by residual interference and channel estimation errors (CEE). The results reveal that imperfect SIC (ipSIC) dominates the BLER floor at high signal-to-noise ratio (SNR), whereas imperfect CSI (ipCSI) primarily affects the moderate-SNR regime, highlighting distinct impairment-driven performance bottlenecks. Therefore, we have extended the study by including two additional scenarios: pCSI combined with ipSIC, and ipCSI combined with ipSIC. This extension allows us to compare the performance of these systems and clearly shows that the system with pSIC and pCSI achieves the best performance. Finally, the results were validated with Monte Carlo simulations.

Goal-oriented semantic communication (SC) aims to revolutionize communication systems by transmitting only task-essential information. However, current approaches face challenges such as joint training at transceivers, leading to redundant data exchange and reliance on labeled datasets, which limits their task-agnostic utility. To address these challenges, we propose a novel framework called Goal-oriented Invariant Representation-based SC (SC-GIR) for image transmission. Our framework leverages self-supervised learning to extract an invariant representation that encapsulates crucial information from the source data, independent of the specific downstream task. This compressed representation facilitates efficient communication while retaining key features for successful downstream task execution. Focusing on machine-to-machine tasks, we utilize covariance-based contrastive learning techniques to obtain a latent representation that is both meaningful and semantically dense. To evaluate the effectiveness of the proposed scheme on downstream tasks, we apply it to various image datasets for lossy compression. The compressed representations are then used in a goal-oriented AI task. Extensive experiments on several datasets demonstrate that SC-GIR outperforms baseline schemes by nearly 10%,, and achieves over 85% classification accuracy for compressed data under different SNR conditions. These results underscore the effectiveness of the proposed framework in learning compact and informative latent representations.

This study delves into the effectiveness of various learning methods in improving Transformer models, focusing mainly on the Gated Residual Network (GRN) Transformer in the context of pediatric intensive care units (PICUs) with limited data availability. Our findings indicate that Transformers trained via supervised learning are less effective than MLP, CNN, and LSTM networks in such environments. Yet, leveraging unsupervised and self-supervised learning (SSL) on unannotated data, with subsequent fine-tuning on annotated data, notably enhances Transformer performance, although not to the level of the GRN-Transformer. Central to our research is analyzing different activation functions for the gated linear unit (GLU), a crucial element of the GRN structure. We also employ Mutual Information Neural Estimation (MINE) to evaluate the GRN's contribution. Additionally, the study examines the effects of integrating GRN within the Transformer's attention mechanism versus using it as a separate intermediary layer. Our results highlight that GLU with sigmoid activation stands out, achieving 0.98 accuracy, 0.91 precision, 0.96 recall, and $0.94~F1$ -score. The MINE analysis supports the hypothesis that GRN enhances the mutual information (MI) between the hidden representations and the output. Moreover, using GRN as an intermediate filter layer proves more beneficial than incorporating it within the Attention mechanism. This study clarifies how GRN boosters GRN-Transformer's performance surpasses other techniques. These findings offer a promising avenue for adopting sophisticated models like Transformers in data-constrained environments, such as PPG artifact detection in PICU settings.

With the surging demand for data-intensive applications, ensuring seamless content delivery in Satellite-Terrestrial Integrated Networks (STINs) is crucial, especially for remote users. Dynamic Ad Insertion (DAI) enhances monetization and user experience, while Mobile Edge Computing (MEC) in STINs enables distributed content caching and ad insertion. However, satellite mobility and time-varying topologies cause service disruptions, while excessive or poorly placed ads risk user disengagement, impacting revenue. This paper proposes a novel framework that jointly addresses three challenges: (i) service continuity-and topology-aware content caching to adapt to STIN dynamics, (ii) Distributed DAI (D-DAI) that minimizes feeder link load and storage overhead by avoiding redundant ad-variant content storage through distributed ad stitching, and (iii) revenue-aware content distribution that explicitly models user disengagement due to ad overload to balance monetization and user satisfaction. We formulate the problem as two hierarchical Integer Linear Programming (ILP) optimizations: one content caching that aims to maximize cache hit rate and another optimizing content distribution with DAI to maximize revenue, minimize end-user costs, and enhance user experience. We develop greedy algorithms for fast initialization and a Binary Particle Swarm Optimization (BPSO)–based strategy for enhanced performance. Simulation results demonstrate that the proposed approach achieves over a 4.5% increase in revenue and reduces cache retrieval delay by more than 39% compared to the benchmark algorithms.

peer reviewed

Task offloading among low-earth orbit (LEO) satellites with on-board computing (OBC) is important for real-time applications. However, OBC is constrained by the battery capacity of LEO, which fluctuates with orbital dynamics and available solar power. This paper addresses the problem of energy sustainability and timeliness in LEO-OBC systems by proposing a sustainable OBC-LEO framework that combines parallel offloading strategies with dynamic energy management. This problem is formulated as a Markov decision process aiming to minimize the overall delay while satisfying the LEO satellite energy constraints and achieving a high task success rate. To balance immediate computational demands and long-term energy stability, a Lyapunov optimization-based dynamic parallel offloading (LODPO) algorithm is designed to make decisions dynamically within each time slot, integrated with subtask allocation based on a low-cost (SABLC) algorithm that dynamically adjusts task allocations. Finally, simulation results demonstrate that the LODPO framework achieves a significant reduction in execution delay, incurring only 34.0% of the delay cost of binary offloading. Most critically, it ensures exceptional reliability, with a task drop rate that is only 8.5% of that seen in binary offloading and 12.0% of that in the DQN-based approach. This ensures high responsiveness and dependability for mission-critical, delay-sensitive applications.

The transition to beyond-fifth-generation (B5G) wireless systems has revolutionized cellular networks, driving unprecedented demand for high-bandwidth, ultra low-latency, and massive connectivity services. The open radio access network (Open RAN) and network slicing provide B5G with greater flexibility and efficiency by enabling tailored virtual networks on shared infrastructure. However, managing resource allocation in these frameworks has become increasingly complex. This paper addresses the challenge of optimizing resource allocation across virtual network functions (VNFs) and network slices, aiming to maximize the total reward for admitted slices while minimizing associated costs. By adhering to the Open RAN architecture, we decompose the formulated problem into two subproblems solved at different timescales. Initially, the successive convex approximation (SCA) method is employed to achieve at least a locally optimal solution. To handle the high complexity of binary variables and adapt to time-varying network conditions, traffic patterns, and service demands, we propose a deep reinforcement learning (DRL) approach for real-time and autonomous optimization of resource allocation. Extensive simulations demonstrate that the DRL framework quickly adapts to evolving network environments, significantly improving slicing performance. The results highlight DRL’s potential to enhance resource allocation in future wireless networks, paving the way for smarter, self-optimizing systems capable of meeting the diverse requirements of modern communication services.

Terahertz (THz) massive multiple-input multiple-output (mMIMO) systems offer ultra-high data rates but face significant challenges such as beam squint effects, high power consumption, severe path loss, and signal blockage. Incorporating reconfigurable intelligent surfaces (RIS) can mitigate these issues but complicates channel state information (CSI) acquisition. To address this, we propose a generative artificial intelligence-based hierarchical deep reinforcement learning (GAI-HDRL) framework that jointly performs channel prediction, hybrid precoding at the base station (BS), passive RIS beamforming, and digital combining at the UE to minimize transmit power. The proposed GAI-HDRL efficiently decomposes the optimization into high-level (precoding and combining) and low-level actions (CSI prediction and RIS configuration), achieving fast convergence and improved prediction accuracy. Simulation results confirm its superiority over state-of-the-art methods in terms of performance and computational efficiency, demonstrating practical viability in THz communications.

This work explores the integration of rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), and beyond-diagonal reconfigurable intelligent surface (BD-RIS) to enhance the spectral-efficiency, energy efficiency, coverage, and connectivity of future sixth-generation (6G) communication networks. Specifically, with a multiuser BD-RIS-empowered RSMA-SWIPT system, we jointly optimize the transmit precoding vectors, the common rate proportion of users, the power-splitting ratios, and scattering matrix of the BD-RIS, under the assumption of imperfect channel state information (CSI). Additionally, to better capture practical hardware behavior, we incorporate a nonlinear energy harvesting model and ensure that the resulting system satisfies all energy harvesting constraints. In the considered system, we design a robust optimization framework to maximize the system sum-rate, while explicitly accounting for the worst-case impact of CSI uncertainties. To tackle the inherent non-convexity of the problem, we introduce an alternating optimization framework that partitions the problem into several blocks, which are optimized in an iterative manner. More specifically, the transmit precoding vectors are optimized by reformulating the problem as a convex semidefinite programming problem through successive-convex approximation (SCA), whereas the inherently convex power-splitting problem is solved using the MOSEK-enabled CVX toolbox. Subsequently, to optimize the scattering matrix of the BD-RIS, we first employ SCA to reformulate the problem into a convex form, and then design a manifold optimization strategy based on the conjugate-gradient method. Finally, numerical simulations are conducted to evaluate the performance of the proposed scheme, revealing significant performance improvements over existing benchmarks and demonstrating rapid convergence within a reasonable number of iterations.

This paper investigates robust transmit (TX) beamforming from the satellite to user terminals (UTs), based on statistical channel state information (CSI). The proposed design specifically targets the mitigation of satellite-to-terrestrial interference in spectrum-sharing integrated terrestrial and satellite networks. By leveraging the distribution information of terrestrial UTs, we first establish an interference model from the satellite to terrestrial systems without shared CSI. Based on this, robust TX beamforming schemes are developed under both the interference threshold and the power budget. Two optimization criteria are considered: satellite weighted sum rate maximization and mean square error minimization. The former achieves a superior achievable rate performance through an iterative optimization framework, whereas the latter enables a low-complexity closed-form solution at the expense of reduced rate, with interference constraints satisfied via a bisection method. To avoid complex integral calculations and the dependence on user distribution information in inter-system interference evaluations, we propose a terrestrial base station position-aided approximation method, and the approximation errors are subsequently analyzed. Numerical simulations validate the effectiveness of our proposed schemes.

This paper addresses the joint optimization of trajectories and bandwidth allocation for multiple Unmanned Aerial Vehicles (UAVs) to enhance energy efficiency in the cooperative data collection problem. We focus on an important yet underestimated aspect of the system, where action synchronization across all UAVs is impossible. Since most existing learning-based solutions are not designed to learn in this asynchronous environment, we formulate the trajectory planning problem as a Decentralized Partially Observable Semi-Markov Decision Process and introduce an asynchronous multi-agent learning algorithm to learn UAVs’ cooperative policies. Once the UAVs’ trajectory policies are learned, the bandwidth allocation can be optimally solved based on local observations at each collection point. Comprehensive empirical results demonstrate the superiority of the proposed method over other learning-based and heuristic baselines in terms of both energy efficiency and mission completion time. Additionally, the learned policies exhibit robustness under varying environmental conditions.

Recently, the noise modulation scheme and active anti-jamming (AAJ) scheme have shown the potential advantages, such as high anti-jamming performance, low transmitter complexity and low power consumption. Inspired by the prior schemes, this paper proposes a novel hybrid noise and jamming modulation (NJM) scheme that enables reliable communication under diverse jamming scenarios. Specifically, this scheme encodes information bits through a noise modulated transmitter and the amplification factor of a programmable gain amplifier (PGA), achieving robust anti-jamming performance. Theoretical bit error rate (BER) is conducted to derive the optimal decoding thresholds for different jamming strategies, and the approximate optimal threshold proposed for practical implementation using training symbols. Furthermore, we formulate and solve the power allocation optimization problem to enhance BER performance by properly splitting power between the two modulation components. To comprehensively evaluate the scheme, we provide the capacity analysis for the hybrid NJM scheme under three jamming strategies. Simulation results show that the theoretical results match well with the simulated ones, which demonstrates the correctness of our BER performance analysis. Moreover, the hybrid NJM scheme can achieve the superior BER performance and the channel capacity under the jamming attacks compared with the benchmark schemes.

Accurate acquisition of channel state information (CSI) is essential for fully harnessing the potential of stacked intelligent metasurfaces (SIMs) in communication systems. In this paper, we address the channel estimation (CE) problem in SIM-based multi-user (MU) millimeter-wave (mmWave) near-field communication systems. To address the severe path loss and blockage in mmWave communication systems, many meta-atoms are typically integrated into each layer of the SIM. Then, the number of radio frequency (RF) chains at the base station (BS) is fewer than that of meta-atoms per layer, resulting in an underdetermined problem. Additionally, the increase in the number of meta-atoms in each layer expands the SIM’s near-field region, leading to the user equipment (UEs) being mostly situated in this region, necessitating precise modeling of the channel under the spherical wavefront assumption. To address these issues, we introduce a compressed sensing (CS)-based CE protocol to tackle the underdetermined problem. In contrast to the traditional CS-based estimation framework, we investigate a polar-domain channel representation to tackle the severe energy spread effect of the classical angular-domain channel representation in near-field communication systems. Specifically, we design a novel polar-domain transform matrix for uniform planar arrays (UPAs), thereby transforming the CE problem into a sparse recovery task of the paths’ support set and complex gains. To overcome the limitations of the sparse Bayesian learning (SBL) framework in tackling high-dimensional dictionaries, we propose a low-complexity polar-domain SBL (LCPD-SBL) algorithm, which significantly reduces computational complexity without compromising estimation accuracy. Numerical simulation results demonstrate that the proposed polar-domain transform matrix yields a better estimation accuracy than traditional angular-domain approaches. Additionally, the proposed LCPD-SBL algorithm can be faster than existing SBL methods by up to 4× while sustaining the same estimation performance.

Multi-beam low earth orbit (LEO) satellite communications, as an essential component for 6G systems, may encounter challenges from severe Doppler shifts and co-channel interference. This paper addresses a realistic problem in 6G-LEO systems, that is, how to meet the high-reliability demands of massive high-mobility terminals. We propose an integrated framework to exploit the synergy of non-orthogonal multiple access (NOMA) and orthogonal time frequency space (OTFS). OTFS modulation is employed to achieve full time-frequency diversity to combat Doppler shifts, while NOMA is used to accommodate more access requests. Specifically, within each beam, power domain superposition is applied to the delay-Doppler domain, enabling multiple terminals to share delay-Doppler grid resources. We analyze the performance of reliability, outage probability and ergodic capacity. Notably, we derive a novel closed-form expression to characterize the distribution of multi-beam interference with varying beam gains. Theoretical analysis and simulation results confirm that the proposed framework achieves a substantially lower outage probability compared to conventional OFDM schemes, with a system capacity improvement exceeding 11.9%.

Quantum key distribution (QKD) offers exceptionally high levels of data security during transmission by using principles of quantum physics. It is renowned for its provable security features. However, a gap between theoretical models and real-world applications, known as quantum hacking, challenges the reliability of QKD networks. Trojan-horse attacks represent a significant threat to the Bob subsystem in QKD, allowing Eve to infer Alice’s basis choices through back-reflected pulses. This can compromise security without detection in severe cases, especially when quantum bit error rates (QBER) fall below the abort threshold. The proposed method combines a category-based Gaussian Mixture Model (GMM) with the Kolmogorov-Smirnov test to estimate the posterior QBER distribution and assess risks in practical QKD systems. By processing the QBER, the approach also evaluates the dependability of the QKD scenario. Numerical results are presented using a state-of-the-art point-to-point QKD device operating over optical quantum channels of 1 m, 1 km, and 30 km lengths. The results of the experimental analysis of a 30 km optical link suggest that the QKD device provided prior information to the proposed learner. Consequently, our proposed trustworthy monitor offers a defensive mechanism that identifies potential Eve attacks, effectively mitigating the risk of security vulnerabilities.

peer reviewed

Stacked intelligent metasurfaces (SIMs), emerging as a revolutionary programmable electromagnetic architecture, have demonstrated unprecedented capabilities in manipulating wireless propagation environments. However, the existing research on SIM-aided downlink communication does not consider the fairness among users. Therefore, this paper studies a SIM-aided hybrid analog-digital system, which aims to fairly guarantee the communication quality of each user while minimizing the transmission power. The hybrid system avails of a SIM for enhancing the communication channel with digital precoding to effectively suppress the interference between users. To this end, we formulate a transmit power minimization problem under quality-of-service constraints, solved by an efficient alternating optimization (AO) algorithm. Simulation results demonstrate that compared to conventional fully digital multiuser multiple-input single-output (MISO) systems, the proposed SIM-aided hybrid system requires 6.93 dBm less transmit power under the same signal-to-interference-plus-noise ratio (SINR) constraints for users. This work reveals the SIM’s powerful wave-based beamforming capabilities, providing effective solutions for energy-efficient networks with low hardware cost.

This paper investigates the effectiveness of employing multiple reconfigurable intelligent surfaces (RIS) for simultaneous wireless information and power transfer (SWIPT) in a vehicle-to-infrastructure (V2I) system. The optimal RIS is selected for transmission based on instantaneous signal-to-noise ratio (SNR) values, with the objective of optimizing the SWIPT system employing the power-splitting (PS) protocol and nonlinear energy harvesting (NL-EH). A unified objective is proposed to maximize information rate and harvested energy via joint optimization of transmit power and power splitting factor. Nonconvexity is addressed via an iterative algorithm, supported by closed-form expressions obtained through Karush-Kuhn-Tucker (KKT) conditions. Monte-Carlo simulations are performed to validate the accuracy of the analytical expressions. Additionally, a deep neural network (DNN) framework is introduced for realtime optimization prediction, achieving superior SWIPT performance over single RIS configurations with reduced complexity and faster execution.

This paper investigates joint device activity detection and channel estimation for grant-free random access in Lowearth orbit (LEO) satellite communications. We consider uplink communications from multiple single-antenna terrestrial users to a LEO satellite equipped with a uniform planar array of multiple antennas, where orthogonal frequency division multiplexing (OFDM) modulation is adopted. To combat the severe Doppler shift, a transmission scheme is proposed, where the discrete prolate spheroidal basis expansion model (DPS-BEM) is introduced to reduce the number of unknown channel parameters. Then the vector approximate message passing (VAMP) algorithm is employed to approximate the minimum mean square error estimation of the channel, and the Markov random field is combined to capture the channel sparsity. Meanwhile, the expectation-maximization (EM) approach is integrated to learn the hyperparameters in priors. Finally, active devices are detected by calculating energy of the estimated channel. Simulation results demonstrate that the proposed method outperforms conventional algorithms in terms of activity error rate and channel estimation precision.

This study focuses on downlink transmissions of a low earth orbit (LEO) satellite, assisted by a beyond diagonal reconfigurable intelligent surface (BD-RIS) to serve ground terminals. Toward optimizing the performance of this system, we formulate the minimization of the average age of information (AoI) achieved at ground terminals. Our formulation respects the power budget of the LEO satellite and guarantees the quality-of-service of ground terminals by optimizing the downlink transmit power at the LEO satellite and reflection coefficients at the BD-RIS as decision variables. Owing to its non-convex and tightly-coupled nature, we reformulate the problem as a Markov decision process which effectively captures its dynamics. Next, a Q-learning propagation (Q-Prop) agent is trained to optimize the decision variables. In light of the mobility of ground terminals as well as LEO satellite, this communication system is highly dynamic. Therefore, we enhance the trained Q-Prop model with meta-learning strategy, which augments its adaptability and generalization to system variances. Numerical results indicate that, in comparison to RIS-lacking and RIS-assisted counterparts, our optimised solution achieves 38% and 26% lower average AoI, respectively.

This paper presents a digital beamforming architecture based on the discrete Fourier transform, designed for medium-Earth orbit satellite payloads to serve multiple ground users. The system leverages a 16×16 16-point two-dimensional fast Fourier transform (2DFFT) to address the growing demand for high-speed data traffic and adaptable satellite communications. The architecture features a routing algorithm for flexible user allocation to any beam position and a cluster-based linear precoding approach to reduce resource and power consumption. Two versions of the 2DFFT module—quantized and non-quantized—are compared in terms of resource usage, power consumption, and performance. Experimental results show that the non-quantized version provides better power efficiency, while the quantized version removes the need for DSP blocks.

This paper presents an intelligent device selection framework for Federated Learning (FL) in Low Earth Orbit (LEO) satellite constellation networks. In particular, the high mobility, intermittent connectivity and long communication delays in LEO satellite networks significantly impact FL convergence and model performance. To address these challenges, we formulate a device selection optimization problem that determines the optimal subset of LEO satellites to participate in global model aggregation at each round. The objective is to minimize the average loss while satisfying latency constraints. To solve the formulated problem, we develop a deep reinforcement learning (DRL)-based solution that enables adaptive satellite selection considering the stochastic nature of satellite connectivity and channel variability. The proposed framework selects participating satellites dynamically based on their communication latency and computational capabilities to improve the overall efficiency of the FL process. Simulation results show that the proposed approach accelerates convergence compared to conventional synchronous FL while maintaining model accuracy.

In this paper, we study the effect of differential delay on the performance of a centralized and cooperative multi-gateway satellite system, where multiple spatially separated gateways transmit simultaneously in the feeder forward link, each serving a cluster of beams. Full frequency reuse is assumed in the user forward link and thus precoding is employed at the central gateway to mitigate the interference, before the precoded signals are distributed to the remote gateways to be sent to the satellite. Simulation results considering the DVB-S2X waveform show that channel state information (CSI) estimation, and as a result the signal-to-interference-plus-noise ratio (SINR), can be significantly degraded, when the inter-cluster interference originating from different gateways is high, emphasizing the importance of user scheduling in this architecture such that the inter-cluster interference is minimized.

The increasing demand for reliable, high-capacity communication during large-scale outdoor events poses significant challenges for traditional Terrestrial Networks (TNs), which often struggle to provide consistent coverage in high-density environments. This paper presents a novel Sixth Generation (6G) radio network planning framework that integrates Non-Terrestrial Networks (NTNs) with Reconfigurable Intelligent Surfaces (RISs) to deliver ubiquitous coverage and enhanced network capacity. Our framework overcomes the limitations of conventional deployable base stations by leveraging NTN architectures, including Low Earth Orbit (LEO) satellites—and passive RIS platforms seamlessly integrated with Beyond 5G (B5G) TNs. By incorporating advanced B5G technologies such as Massive Multiple-Input Multiple-Output (mMIMO) and beamforming, and by optimizing spectrum utilization across the C, S, and Ka bands, we implement a rigorous interference management strategy based on a dynamic SINR model. Comprehensive calculations and simulations validate the proposed framework, demonstrating significant improvements in connectivity, reliability, and cost-efficiency in crowded scenarios. This integration strategy represents a promising solution for meeting the evolving demands of future 6G networks.

This paper presents a novel framework for cognitive radio (CR)-enabled integrated terrestrial and non-terrestrial networks (ITNTNs), comprising a primary low-Earth-orbit (LEO) satellite network and a secondary terrestrial network. In particular, a beyond-diagonal reconfigurable intelligent surface (BD-RIS) mounted secondary base station (BS) reuses the same spectrum to communicate with the secondary user. The proposed framework improves the energy efficiency of the secondary network while ensuring that the interference temperature threshold of the primary LEO network is not violated. The joint optimization of BS power allocation and BD-RIS phase shifts is considered, which results in a highly nonconvex problem. To address this challenge, the Dinkelbach method is first employed to transform the fractional objective function, followed by the development of an alternating optimization strategy. Specifically, the BS power allocation is optimized using the Lagrangian method with Karush-Kuhn-Tucker (KKT) conditions, while the BD-RIS phase shifts are updated through manifold optimization techniques. Numerical results demonstrate that the proposed BD-RIS framework performs better than conventional diagonal RIS (D-RIS) configurations in terms of energy and spectral efficiency, highlighting its potential to enable green, adaptive, and high-capacity 6G ITNTN deployments.

This work lays the foundations for full-duplex (FD) extremely large (XL) holographic multiple-input multiple-output (MIMO) communication systems to achieve seamless integration of reconfigurable holographic surfaces (RHS) and FD capabilities, enabling ultra-high-capacity, low-latency, and energy-efficient wireless communications. We consider the problem of sum-rate maximization by jointly designing the digital beamformers, holographic beamformer, and holographic combiner at the FD base station to jointly suppress self-interference (SI) and cross-interference. However, this results in a highly non-convex problem, for which a novel alternating optimization combining the minorization-maximization principle and the gradient ascent method is proposed. Simulation results demonstrate that the proposed method almost doubles the spectral efficiency compared to a half-duplex (HD) system.

Index modulation based frequency hopping spread spectrum (IM-FHSS) has shown the attractive anti-jamming capability. With the aid of jamming detection, IM-FHSS becomes efficient to defend various malicious jamming attacks, especially the reactive jamming. In this paper, we propose two jamming detection approaches for IM-FHSS to efficiently detect reactive jamming. Specifically, the pilot symbols for channel estimation are used in the first detection approach, where a single frame to calculate the optimal test statistic and make the decision. Second, the proposed approach II collects the pilot symbols from many data frames to detect the reactive jamming. Moreover, we theoretically derive the closed-form expressions of the optimal thresholds and the probabilities of detection, and provide the complexity comparison. Simulation results validate the correctness of our performance analysis and show that the proposed approaches outperform the prior detection schemes.

Integrated sensing and communication (ISAC), which can exploit the wireless spectrum for concurrent sensing and communication functions, is regarded as a promising technology for the future sixth generation (6G) wireless communication networks. This paper proposes a robust beam tracking method for maneuverable unmanned aerial vehicles (UAVs) with jittering effect within the ISAC framework. By utilizing reflected echoes, the kinematic parameters are measured and the interacting multiple model with extended Kalman filter (IMM-EKF) is designed for robust beam tracking of maneuverable UAVs. Furthermore, due to the jittering effect, the UAV may not point to the optimal alignment direction. To this end, we propose the coordinate descent particle swarm optimization (CDPSO) algorithm to balance the jittering effect by maximizing the received signal-to-noise ratio (SNR) of the UAV. The effectiveness of the proposed scheme is verified via simulation results.

This paper investigates robust transmit beamforming based on statistical channel state information (CSI), against satellite-to-terrestrial user terminal (UT) interference arising from spectrum sharing in the integrated terrestrial and satellite network. First, we develop an integral-form interference model free of shared CSI to characterize the interference from satellite to terrestrial UTs. Then, we propose a robust interference-avoidance transmit beamforming scheme under the interference threshold and power budget. We derive a closed-form solution based on the minimum mean square error criterion and apply a bisection method to satisfy interference thresholds. Furthermore, we introduce a base station position-aided approximation scheme to eliminate the complex integral calculations. Numerical simulations validate the proposed schemes.

High peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) signals presents a persistent challenge in satellite communications (SatCom), impacting signal quality and causing adjacent channel interference. This paper introduces a novel framework that combines the elastic net-based machine learning (ML) model with the partial transmit sequence (PTS) technique to effectively reduce PAPR. Additionally, the potential of artificial intelligence (AI) approaches are investigated, specifically swarm intelligence and ML methods, for high-performance, low-complexity solutions. In this regard, ML models are applied to mitigate PAPR in SatCom networks under the presence of a traveling wave tube amplifier (TWTA) model and a land mobile satellite (LMS) channel, employing 16-quadrature amplitude modulation (16-QAM). Compared with the baseline schemes, simulation results demonstrate that the proposed ML framework, integrating principal component analysis (PCA) with the elastic net learning model, achieves comparable PAPR performance and minimal computational complexity.

This work proposes a framework for the robust design of UAV-assisted wireless networks that combine 3D trajectory optimization with user mobility prediction to address dynamic resource allocation challenges. We proposed a sparse second-order prediction model for real-time user tracking coupled with heuristic user clustering to balance service quality and computational complexity. The joint optimization problem is formulated to maximize the minimum rate. It is then decomposed into user association, 3D trajectory design, and resource allocation subproblems, which are solved iteratively via successive convex approximation (SCA). Extensive simulations demonstrate: (1) near-optimal performance with ϵ ≈ 0.67% deviation from upper-bound solutions, (2) 16% higher minimum rates for distant users compared to non-predictive 3D designs, and (3) 10 − 30% faster outage mitigation than time-division benchmarks. The framework’s adaptive speed control enables precise mobile user tracking while maintaining energy efficiency under constrained flight time. Results demonstrate superior robustness in edge-coverage scenarios, making it particularly suitable for 5G/6G networks.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by dynamically controlling the propagation environment. Recently, a non-diagonal RIS architecture has been proposed, enabling more advanced signal manipulation by allowing signals impinging on one element to be reflected from another element after appropriate phase-shift adjustment. This paper analyzes the energy efficiency of non-diagonal RIS-assisted wireless communication systems in high- and low-signal-to-noise-ratio (SNR) regime. We derive closed form expressions of the spectral and energy efficiency for both the non-diagonal and its diagonal counterpart, which is used as a benchmark for comparison. Simulation results reveal that non-diagonal RIS systems are the preferred choice for communication systems that prioritize spectral efficiency. Interestingly, for energy efficiency, the selection between diagonal and non-diagonal RIS architectures depends on the received SNR conditions, with diagonal RIS systems excelling at high SNR and non-diagonal RIS systems performing better at low SNR scenarios.

This paper studies the distributed precoding design for multi-satellite massive MIMO transmission. We first conduct a detailed analysis of the transceiver process, examining the effects of delay and Doppler compensation errors and emphasizing the nearly independent nature of inter-satellite interference. Based on the derived signal model, an approximate expected sum rate maximization problem is formulated, incorporating statistical channel state information and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation cannot hold equivalence in the considered scenario. To address this, we propose a modified WMMSE formulation leveraging channel covariance matrix decomposition. By exploiting channel characteristics, a low-complexity decomposition method is then developed, accompanied by an efficient algorithm. Simulation results validate the effectiveness and robustness of the proposed method in some practical simulated scenarios.

Low Earth Orbit (LEO) satellite constellations consist of numerous satellites orbiting at different altitudes to serve diverse terrestrial users. Efficient resource management in such dynamic and large-scale networks presents a significant challenge. This paper studies Resource Blocks (RBs) and transmit power allocation at each LEO satellite, aiming at enhancing network performance while meeting Quality of Service (QoS) requirements. A stochastic optimization problem is formulated where the required QoS by each user is expressed as a chance constraint of the minimum data rate requirements considering network dynamics and uncertainties in channel conditions and traffic demands. The Conditional Value at Risk (CVaR) is employed to reformulate the chance constraint into a convex form and achieve a robust solution. Then, an alternating optimization approach is applied to solve the optimization problem. Through simulations using real data from the Starlink constellation, we demonstrate the efficacy of the proposed approach in improving network data rates while maintaining the required QoS levels.

In this paper, we investigate the performance of the rate-splitting multiple access (RSMA) framework in a mmWave cell-free massive multiple-input multiple-output (CF-mMIMO) system assisted by multiple reconfigurable intelligent surfaces (RISs). We consider the practical scenario of low-resolution digital-to-analog converters (DACs) at the distributed access points (APs) to reduce hardware complexity and power consumption. Our main objective is to maximize the minimum rate among the users by jointly optimizing the precoding vectors at each AP, the common rates, and the reflection coefficients of RISs. The resultant non-convex optimization problem is then solved using alternating optimization and successive convex approximation-based methods. Numerical results demonstrate the superior performance of the proposed RSMA-based scheme over traditional methods across several deployment scenarios, with performance gains from RIS deployment notably improved in hotspot scenarios.

Reconfigurable Intelligent Surface (RIS) technology has emerged as a transformative solution for enhancing satellite networks in next-generation wireless communication. The integration of RIS in satellite networks addresses critical challenges such as limited spectrum resources and high path loss, making it an ideal candidate for next-generation Internet of Things (IoT) networks. This paper provides a new framework based on transmissive beyond diagonal RIS (T-BD-RIS) mounted low earth orbit (LEO) satellite networks with non-orthogonal multiple access (NOMA). The NOMA power allocation at LEO and phase shift design at T-BD-RIS are optimized to maximize the system's spectral efficiency. The optimization problem is formulated as non-convex, which is first transformed using successive convex approximation and then divided into two problems. A closed-form solution is obtained for LEO satellite transmit power using KKT conditions, and a semi-definite relaxation approach is adopted for the T-BD-RIS phase shift design. Numerical results are obtained based on Monte Carlo simulations, which demonstrate the advantages of T-BD-RIS in satellite networks.

Network energy saving has received great attention from operators and vendors to reduce energy consumption and CO2 emissions to the environment as well as significantly reduce costs for mobile network operators. However, the design of energy-saving networks also needs to ensure that mobile users' (MUs) QoS requirements such as throughput requirements (TR). This work considers a mobile cellular network including many ground base stations (GBSs), and some GBSs are intentionally turned off due to network energy saving (NES) or crash, so the MUs located in these outage GBSs are not served in time. Based on this observation, we propose the problem of maximizing the total achievable throughput in the network by optimizing the GBSs' antenna tilt and adaptive transmission power with a given number of served MUs satisfied. Notice that, the MU is considered successfully served if its Reference Signal Received Power (RSRP) and throughput requirement are satisfied. The formulated optimization problem becomes difficult to solve with multiple binary variables and nonconvex constraints along with random throughput requirements and random placement of MUs. We propose a Deep Q-learning-based algorithm to help the network learn the uncertainty and dynamics of the transmission environment. Extensive simulation results show that our proposed algorithm achieves much better performance than the benchmark schemes.

Satellite communication (SatCom) systems play a vital role in providing global connectivity and enable a wide range of applications, including Internet of Things (IoT) connectivity for remote areas, such as forests and oceans. Two crucial resource allocation challenges in SatCom are beam placement (BP) and frequency assignment (FA) problems, which involve the clique covering (CC) and graph coloring (GC) problems, respectively. Conventional solutions for these problems incur excessive computational cost, which is intractable for classical computers. A promising approach is to formulate these problems using the Ising model, construct their Hamiltonians, and then solve them efficiently by a quantum computer. However, the current quantum computers have very limited hardware and can only handle rather small inputs. To overcome this limitation, we propose a hybrid-quantum-classical-computational pipeline where an efficient hamiltonian reduction method is the key for solving large CC/GC instances. Through experiments on real quantum computers, our reduction method outperforms commercial solutions, allowing quantum annealers to handle significantly larger BP/FA instances while maintaining high probability to achieve feasible solutions and near-optimal performance. Although the inherent hardness of the CC/GC problems cannot be overcome by quantum computing, our research contributes to the early exploration of quantum computing in the context of the complex optimization problems in SatCom systems, particularly in the realm of IoT connectivity for remote areas.

Malicious jamming attacks pose a significant threat to the integrity and performance of Internet of Things (IoT) networks. However, many jamming patterns are rare or infrequent, which makes them difficult to counter effectively. This article addresses the critical issue of anti-jamming (AJ) under few-shot sample conditions in IoT networks. Source separation is a key component of AJ communication. Although deep learning-based source separation has demonstrated significant advantages, it typically requires a large amount of labeled data, which can be impractical in certain environments. To overcome this challenge, we propose two novel schemes that leverage multitask learning (MTL) and meta-learning (ML) to enhance the model’s signal separation capabilities within the constraints of limited sample scenarios. MTL enhances robustness by leveraging shared representations across tasks, while ML allows for rapid adaptation to novel jamming signals with minimal samples. Specifically, we employ a modified separation model, SepFormer, as our baseline and integrate MTL and ML schemes to enable the separation of unknown or few-shot jamming signals. Additionally, we have constructed two datasets encompassing both simulated and real-world environmental data to test and evaluate the performance of the proposed methods. Simulation results demonstrate the superior AJ performance of our schemes, particularly when compared with a direct application of the separation model with few-shot samples. Furthermore, our evaluation of performance across various jamming scenarios and interference-to-signal ratios (ISRs) further confirms the effectiveness of our proposed scheme.

This article focuses on the serious threat to security of key-based physical-layer authentication (PLA) by an eavesdropper using an elaborate impersonation attack, which aims to pass the authentication process illegally. To prevent the eavesdropper from decreasing the authentication performance, we propose a robust channel-phase-based PLA scheme for multicarriers transmission, which contains a novel two-level decision. Specifically, the first level decision is used to protect legitimate users from high-receiver power caused by the elaborate attack of the eavesdropper, and the second one is used to further authenticate the user. The optimal threshold for accurately detecting the response signal with high-receiver power is derived. Moreover, we provide the theoretical performance analysis for the proposed scheme, and derive the closed-form expressions of the probability of detection and false alarm via the numerical statistic and the proper approximation. Simulation results show the robustness of our proposed scheme and verify the effectiveness of the theoretical analysis.

Physical-layer key generation (PKG) technology offers a lightweight encryption solution for device-to-device (D2D) communications. However, it faces significant challenges due to the high correlation between the eavesdropping and legitimate channels, as well as the imperfect estimated channel, which cause substantial degradation of secret key capacity. To address the challenges, we propose a novel PKG framework that leverages the reflective beamforming of reconfigurable intelligent surface (RIS) for D2D communications. Specifically, we first derive closed-form expression for the secret key capacity under correlated and imperfect estimated channels. Then, we propose to maximize the minimum secret key capacity by optimizing the reflection coefficient matrix (O-RCM) of RIS, which is a non-convex optimization problem. Next, a semi-definite relaxation and successive convex approximation-based method for O-RCM (SSO-RCM) is proposed to tackle the non-convex max-min-min problem. To further reduce the computational complexity, we propose a low-complexity method based on the path-following algorithm for O-RCM (PFO-RCM). Simulation results show that under correlated and imperfect channels, both proposed methods significantly improve the minimum secret key capacity compared to the existing RIS-assisted methods. Moreover, the PFO-RCM method, while exhibiting lower computational complexity than the SSO-RCM method, incurs a small performance loss.

Network slicing (NS) is one of the key techniques to manage logical and functionally separated networks on a common infrastructure, in a dynamic manner. As the complexity of virtualizing a full infrastructure required unprecedented effort, the initial idea of combining satellite and terrestrial networks has not been fully implemented in 5G yet. 6G networks are expected to further bring NS to a substrate network that is more heterogeneous, due to the full integration between terrestrial and satellite networks. NS describes the process of accommodating virtual networks, typically composed of nodes and links with the respective requirements, into the main infrastructure. This is an NP-Hard problem, typically also known as Virtual Network Embedding (VNE). Existing VNE solutions are designed per use-case and lack flexibility, adaptation and traffic-awareness, especially in such dynamic satellite environment. In this work, we investigate the VNE implementation to integrated satellite-terrestrial networks and propose a novel flexible framework, named Slice-Aware VNE for Satellite-Terrestrial (SAST-VNE), which 1) operates based on traffic prioritization; 2) jointly optimizes the load-balancing and the migration cost when network congestion occurs; and 3) provides a near-optimal solution. We compare SAST-VNE to existing well-known near-optimal VNE algorithms such as VINEYard and CEVNE and the shortest-path SN-VNE solution for satellite networks. The simulations showed that SAST-VNE reduces the migration costs between 10% and 40% during satellite handovers while maintaining the network load under control. Furthermore, when congestion occurs, SAST-VNE proved to be flexible in matching the priority of the slice, i.e., tolerated latency, with the time complexity and optimality of the solution.

Satellite communications (SatComs) have recently been through a renaissance, both technologically and entrepreneurially. Ambitious plans have already come into fruition with the operation of low-Earth orbit (LEO) constellations including thousands of satellites and supported by state of the art but proprietary technologies, such as active antenna arrays and intersatellite links (ISLs). In this context, this article aims to provide a forward-looking vision of use cases and a deep dive into technological enablers that will be prominent in space communications beyond 2030. In parallel, it motivates how open standards can play a role in delivering affordable communication services in space. Starting from the 5G plans for nonterrestrial networks, we provide a survey and roadmap toward artificial intelligence (AI)-supported satellite systems, space-enabled quantum networks, and joint communications and positioning (JCAP) for space missions and interplanetary exploration.

Low Earth orbiting (LEO) satellite-assisted wireless communication is increasingly vital for future communication networks due to the significant spectrum scarcity in radio frequency channels, presenting a critical bottleneck. Thus, optimizing the utilization of available radio frequency spectrum has become imperative. Advanced techniques like underlay communication and Rate Split Multiple Access (RSMA) have proven effective in enhancing spectrum utilization. When LEO satellites are applied to tasks such as agricultural assistance, search and rescue operations, and military defense, LEO-to-ground communication can leverage underlay fashion using RSMA to transmit messages to multiple users simultaneously on the same channel. However, conventional underlay communication setups necessitate transmitter cooperation to manage system interference. Enabling non-cooperative systems to communicate in an underlay fashion unlocks the untapped potential of these advanced transmission techniques. This study addresses the challenge of maximizing the RSMA rate of the LEO-to-ground communication system (secondary system) operating in an underlay mode without cooperation with the ground-to-ground communication system (primary system), where the primary network operates in a time-division multiple-access fashion. We propose a dueling-based double deep Q-learning solution to optimize the allowed transmission power at the LEO satellite, ensuring no outage in the primary system. Additionally, we introduce an optimal solution framework to distribute the allowed transmission power among all signals of the secondary devices, maximizing the RSMA rate while meeting the rate requirements of all underlay secondary devices. Simulation results demonstrate that this hybrid solution framework provides excellent performance while ensuring no outage at the primary network.

This manuscript introduces a novel energy-efficient optimization strategy for a zero-energy reconfigurable intelligent reflecting surface (Ze-RIS) supported backscatter communication system employing non-orthogonal multiple access (NOMA). The central objective is to maximize the energy-efficiency of the system by optimizing the several key parameters, including the amplitude reflection coefficient of Ze-RIS, the reflection coefficients of the backscatter tags, transmit beamforming at the base station, and passive beamforming at the Ze-RIS node, while incorporating a practical non-linear energy harvesting model both for the Ze-RIS and backscatter nodes. The proposed algorithm addresses the complex non-convex problem through three stages. Firstly, the transmit beamforming vectors are determined by leveraging the semi-definite programming and successive-convex approximation, while handling the rank-1 constraint with the semi-definite relaxation. Secondly, we determine the amplitude reflection coefficient of Ze-RIS by leveraging the monotonicity property of the objective function. Simultaneously, we compute the reflection coefficients of backscatter tags using the Dinkelbach algorithm, Lagrange duality, and the sub-gradient method. Thirdly, we compute passive beamforming using successive-convex approximation and semi-definite programming techniques, achieving a rank-1 solution through the penalty-based method. Finally, the numerical simulations confirm the effectiveness of the proposed approach, demonstrating its superiority over the benchmark competitors with rapid convergence within a few iterations.

The channel estimation and signal detection are key issues in the Multi-Input Multi-Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system. However, there exist severe impacts of phase noise (PN) on the estimations with the application of higher frequency in 5th Generation New Radio (5G-NR). In this paper, the joint channel estimation and signal detection (JCESD) method based on the maximum a posteriori (MAP) criterion under the assumption of Wiener process for PN is proposed in MIMO-OFDM system, which is called as the JCESD-PN-MAP method. Firstly, the MAP criterion is derived based on Bayesian theories and the structures of matrices in MAP criterion are analyzed to simplify the optimizations. Secondly, the optimizations for PN of receiving and transmitting antennas are translated into solving a tridiagonal linear equation and a sparse linear equation, respectively, which are optimized by the Gaussian elimination (GE) method with low computation complexity. To further reduce the computational complexity, the latter is solved by the alternating direction method of multipliers (ADMM) method. Thirdly, the optimizations for channel responses and transmitted signals are translated into solving two block diagonal linear equations, which are solved by calculating the inverse matrix with low computation complexity. The numerical results and complexity analysis confirm the effectiveness of our proposed method in terms of the accuracy and computation complexity.

Quantum networks connect devices to facilitate the transmission of quantum bits (qubits), harnessing quantum mechanics for groundbreaking telecommunications applications. The quantum state exchange rely on quantum entangled particles which have to be sent from a Quantum Base Station (QBS) to Quantum Nodes (QNs) through an optical channel. Free Space Optical (FSO) links promise flexibility and cost advantages over the classical fiber-based infrastructure. However, the absence of Line of Sight (LoS) between the QBS and QNs may completely disrupt the communication. Therefore, this work focuses on an Intelligent Reflective Surface (IRS)-assisted Unmanned Aerial Vehicle (UAV)-aided FSO quantum network. An optimization problem is formulated to fairly maximize qubit reception at QNs by optimizing UAV hovering locations, constrained by fidelity and link-per-IRS limits. The intractability of the original problem is first tackled with a sigmoid-based approximations, subsequently split into three sub-problems, sequentially solved applying the Successive Convex Approximation (SCA) technique. Numerical results (i) validate the approximation accuracy, and (ii) demonstrates the effectiveness of the proposal against three baseline approaches, showing an order of magnitude increase in terms of fairness.

In the ever-evolving landscape ofNextGwireless networks, Open radio access network (RAN) emerges as a transformative paradigm, revolutionizing network architectures and fostering innovation through its open, intelligent and disaggregated approach. By integrating RAN intelligent controllers (RICs), we can seamlessly implement machine learning (ML) algorithms to cater to diverse vertical applications and deployment environments without the need for intricate planning. However, this architecture suffers from two critical challenges: frequent handovers and load balancing amid varying traffic demands of different services in dynamic environments. To address these issues, this study proposes a joint intelligent user association, congestion control, and resource scheduling (IUCR) scheme. Aligning with the 7.2x functional split (FS) option recommended by the O-RAN Alliance, we present a hierarchical optimization framework incorporating heuristic methods, successive convex approximation (SCA), and a distributed deep reinforcement learning (DRL) approach across different Open RAN components, such as RICs and RAN layers. The simulation results convincingly demonstrate the superior performance of the proposed scheme compared to centralized approaches, validating its effectiveness.

In this paper, we study the fundamental limits of simultaneous semantic information and power transfer in wireless networks, where we consider both the point-to-point case as well as the Gaussian multiple access channel (MAC). Specifically, for the point-to-point case, we consider a three-party communication system, where a transmitter aims to simultaneously convey semantic information to an information receiver and energy to an energy harvesting receiver (ER). An achievable and a converse region in terms of information and energy rates are presented for both the discrete memoryless (DM) and Gaussian channel. For the DM channel, the achievable region is obtained by utilizing the asymptotic equipartition property and a converse region is obtained by using outer bounds on the semantic information rates. For the Gaussian channel, we characterize an achievable region by applying a power splitting technique between the information and the semantic context parts. A converse region is obtained that provides an estimate on the information-energy capacity while taking into account semantics. On the other hand, for the Gaussian MAC case, we consider an hybrid setup where a semantic transmitter and a conventional transmitter are employed subject to an energy harvesting constraint at the ER. Specifically, we characterize the semantic-bit information energy region, by providing an achievable and a converse region. Numerical results show that in both cases a higher performance can be achieved in terms of information and energy rates when considering a low semantic ambiguity code in comparison to the classical coding scheme (without semantic). Moreover, in the context of Gaussian MAC, it is shown that it is preferable to use semantic communications in scenarios with low signal-to-noise ratio (SNR), while conventional communications is more suitable at high SNRs.

With countless promising applications in various domains such as IoT and Industry 4.0, task-oriented communication design (TOCD) is getting accelerated attention from the research community. This paper presents a novel approach for designing scalable task-oriented quantization and communications in cooperative multi-agent systems (MAS). The proposed approach utilizes the TOCD framework and the value of information (VoI) concept to enable efficient communication of quantized observations among agents while maximizing the average return performance of the MAS, a parameter that quantifies the MAS’s task effectiveness. The computational complexity of learning the VoI, however, grows exponentially with the number of agents. Thus, we propose a three-step framework: (i) learning the VoI (using reinforcement learning (RL)) for a two-agent system, (ii) designing the quantization policy for an N-agent MAS using the learned VoI for a range of bit-budgets and, (iii) learning the agents’ control policies using RL while following the designed quantization policies in the earlier step. Our analytical results show the applicability of the proposed framework under a wide range of problems. Numerical results show striking improvements in reducing the computational complexity of obtaining VoI needed for the TOCD in a MAS problem without compromising the average return performance of the MAS.

This paper investigates downlink transmission in 5G Integrated Satellite-Terrestrial Networks (ISTNs) supporting automotive users (UEs) in urban environments, where base stations (BSs) and Low Earth Orbit (LEO) satellites (LSats) cooperate to serve moving UEs over shared C-band frequency carriers. Urban settings, characterized by dense obstructions, together with UE mobility, and the dynamic movement and coverage of LSats pose significant challenges to user association and resource allocation. To address these challenges, we formulate a multi-objective optimization problem designed to improve both throughput and seamless handover (HO). Particularly, the formulated problem balances sum-rate (SR) maximization and connection change (CC) minimization through a weighted trade-off by jointly optimizing power allocation and BS-UE/LSat-UE associations over a given time window. This is a mixed-integer and non-convex problem which is inherently difficult to solve. To solve this problem efficiently, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) technique. Furthermore, we introduce a practical prediction-based algorithm capable of providing efficient solutions in real-world implementations. Especially, the simulations use arealistic 3D map of Londonand UE routes obtained from the Google Navigator application to ensure practical examination. Thanks to these realistic data, the simulation results can show valuable insights into the link budget assessment in urban areas due to the impact of buildings on transmission links under the blockage, reflection, and diffraction effects. Furthermore, the numerical results demonstrate the effectiveness of our proposed algorithms in terms of SR and the CC-number compared to the greedy and benchmark algorithms.

In this paper, we investigate symbol-level precoding (SLP) and efficient decoding techniques for downlink transmission, where we focus on scenarios where the base station (BS) transmits multiple quadrature amplitude modulation (QAM) constellation streams to users equipped with multiple receive antennas. We begin by formulating a symbol-level joint design scheme aimed at collaboratively optimizing the transmit precoding and receive combining matrices. This coupled problem is addressed by employing the alternating optimization (AO) method, and closed-form solutions are derived by analyzing the obtained two subproblems. Furthermore, to address the dependence of the receive combining matrix on the transmit signals, we switch to maximum likelihood detection (MLD) method for decoding. Notably, we have demonstrated that the smallest singular value of the precoding matrix significantly impacts the performance of MLD method. Specifically, a lower value of the smallest singular value results in degraded detection performance. Additionally, we show that the traditional SLP matrix is rank-one, making it infeasible to directly apply MLD at the receiver end. To circumvent this limitation, we propose a novel symbol-level smallest singular value maximization problem, termed SSVMP, to enable SLP in systems where users employ the MLD decoding approach. Moreover, to reduce the number of variables to be optimized, we further derive a more generic semidefinite programming (SDP)-based optimization problem. Numerical results validate the effectiveness of our proposed schemes and demonstrate that they significantly outperform the traditional block diagonalization (BD)-based method.

The escalating number of wireless users requiring different services, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC), has led to exploring non-orthogonal multiplexing methods like heterogeneous non-orthogonal multiple access (H-NOMA). This method allows users demanding divergent services to share the same resources. However, implementing the H-NOMA scheme faces major resource management challenges due to unpredictable interference caused by the random access mechanism of mMTC users. To address this issue, this paper proposes a joint optimization and cooperative multi-agent (MA) deep reinforcement learning-based resource allocation mechanism, aimed at maximizing the energy efficiency (EE) of H-NOMA-based networks. Specifically, this work initially establishes an optimization framework capable of determining the optimal power allocation for any specific sub-channel assignment (SA) setting for all users. Based on that, a cooperative MA double deep Q network (CMADDQN) scheme is carefully designed at the base station to conduct SA among users. In addition, a distributed full learning-based approach using MADDQN for both SA and power allocation is also designed for comparison purposes. Simulation results show that the proposed joint optimization and machine learning method outperforms the solely-learning-based approach and other benchmark schemes in terms of convergence rate and EE performance.

In this work, we consider a typical scenario in a harsh urban propagation environment which is typical for a smart city scenario where multiple reconfigurable intelligent surfaces (RISs) are deployed in different hotspot areas to overcome signal blockage between the base station and users. Our goal is to ensure uninterrupted service availability to users in different hotspot areas regardless of their location. Consistent service availability can be achieved by guaranteeing that each RIS deployed in a hotspot area can support a certain number of users. This plays a critical role in smart city applications in the context of emergency communications and ubiquitous connectivity since the design ensures service availability to as many users as possible in all relevant locations. Taking into consideration the challenges in obtaining channel state information (CSI) given the passive nature of RIS and dynamic environments, we formulate a robust fairness problem to maximize the minimum expected number of served users in proximity to each RIS while considering the available transmit power and the worst-case quality of service (QoS) constraints within the bounded CSI error model framework. The resulting problem is a mixed integer non-convex program which is highly coupled and challenging to solve in polynomial time. Thus, we resort to binary variable relaxation, convex approximation techniques, and alternating optimization to tackle the problem. Additionally, we handle the semi-infinite uncertainty constraints by employing the S-procedure and general sign-definiteness. Simulation results demonstrate the effectiveness of the proposed design in obtaining consistent and reliable service in different hotspot areas compared to the relevant benchmark schemes. In addition, the proposed design shows flexibility in serving users with their target QoS given different channel uncertainty levels.

Although reconfigurable intelligent surface (RIS) is a promising technology for shaping the propagation environment, it consists of a single-layer structure within inherent limitations regarding the number of beam steering patterns. Based on the recently revolutionary technology, denoted as stacked intelligent metasurface (SIM), we propose its implementation not only on the base station (BS) side in a massive multiple-input multiple-output (mMIMO) setup but also in the intermediate space between the base station and the users to adjust the environment further as needed. For the sake of convenience, we call the former BS SIM (BSIM), and the latter channel SIM (CSIM). To this end, we achieve hybrid wave-based combining at the BS and wave-based configuration at the intermediate space. Specifically, we propose a channel estimation method with reduced overhead, being crucial for SIM-assisted communications. Next, we derive the uplink sum spectral efficiency (SE) in closed form in terms of statistical channel state information (CSI). Notably, we optimize the phase shifts of both BSIM and CSIM simultaneously by using the projected gradient ascent method (PGAM). Compared to previous works on SIMs, we study the uplink transmission in a mMIMO setup, channel estimation in a single phase, a second SIM at the intermediate space, and simultaneous optimization of the two SIMs. Simulation results show the impact of various parameters on the sum SE, and demonstrate the superiority of our optimization approach compared to the alternating optimization (AO) method.

This paper investigates joint device identification, channel estimation, and symbol detection for LEO satellite-enabled grant-free random access systems, specifically targeting scenarios where remote Internet-of-Things (IoT) devices operate without global navigation satellite system (GNSS) assistance. Considering the constrained power consumption of these devices, the large differential delay and Doppler shift are handled at the satellite receiver. We firstly propose a spreading-based multi-frame transmission scheme with orthogonal time-frequency space (OTFS) modulation to mitigate the doubly dispersive effect in time and frequency, and then analyze the input-output relationship of the system. Next, we propose a receiver structure based on three modules: a linear module for identifying active devices that leverages the generalized approximate message passing algorithm to eliminate inter-user and inter-carrier interference; a non-linear module that employs the message passing algorithm to jointly estimate the channel and detect the transmitted symbols; and a third module that aims to exploit the three dimensional block channel sparsity in the delay-Doppler-angle domain. Soft information is exchanged among the three modules by careful message scheduling. Furthermore, the expectation-maximization algorithm is integrated to adjust phase rotation caused by the fractional Doppler and to learn the hyperparameters in the priors. Finally, the convolutional neural network is incorporated to enhance the symbol detection. Simulation results demonstrate that the proposed transmission scheme boosts the system performance, and the designed algorithms outperform the conventional methods significantly in terms of the device identification, channel estimation, and symbol detection.

The co-design of fluid antenna (FA) technology and simultaneous wireless information and power transfer (SWIPT) can be jointly beneficial. Specifically, SWIPT facilitates both data and energy transfer to low-power devices, while FA technology introduces a new dimension for optimizing SWIPT performance through intelligent port selection. Thus, in this work, we develop an analytical framework by employing stochastic geometry and copula theory to evaluate FA-enabled users’ performance in SWIPT networks. All users utilize successive interference cancellation and two novel port selection schemes, namely information decoding-focused (IDf) and energy harvest-focused (EHf), to leverage FAs’ liquid dimension for enhanced data or energy transfer, by considering the counterposed effects of multi-user interference. We derive closed-form expressions for signal-to-interference ratio and received signal power under correlated Nakagami-$\kappa $fading by using Student’s t copula. The developed framework assesses SWIPT performance meta-distribution of the proposed schemes and facilitates the performance evaluation of two user location-based classifications i.e., cell-center (CC) and cell-edge (CE) users. Results reveal the beneficial synergy of FAs and SWIPT, with around 29% improvement for CC and 133% for CE users compared to conventional static SWIPT communications, and highlight that the EHf scheme proves more efficient for CE users, while the IDf scheme benefits CC users.

As the demand for remote sensing increases, it is gradually becoming possible for remote sensing satellites to access large-scale communication constellations through laser inter-satellite links, enabling high-throughput data backhaul. This paper addresses the inter-layer topology planning problem using potential game theory for the first time, strategically framing it as a decision-making problem for remote sensing satellites to access communication satellites. Then, theoretical derivation confirms that the above problem is a potential game and verifies the existence of Nash equilibrium. Additionally, considering factors such as laser link establishment time, cross-layer link visibility time consumption rate, mission transmission delay, and communication satellite loading level, a potential game strategy selection probability update algorithm (PG) based on revenue contribution is proposed. Finally, the superior performance of the PG algorithm in terms of task completion, transmission delay, and transport layer network load is effectively demonstrated through two scenarios involving the Starlink&DOVE and GW&R-SAT constellations.

The next-generation regenerative payload-enabled low Earth orbit (LEO) satellites enable task offloading and delivering services to energy and computation-constrained devices in remote terrains. Recent studies on satellite-aided edge computing often focus on binary offloading scenarios, neglecting crucial system parameters such as service period, task deadline, and output size. To address these limitations, we propose a hierarchical computation framework for remote Earth observation-related services such as disaster prediction, 2D/3D scene observation, route finding, and rescue operations based on satellite images/videos. The proposed framework supports parallel and partial task offloading strategies, optimizing the communication and computation resources across the serving LEO satellite, adjacent LEO satellites, and cloud-aided gateway. Our objective is to minimize the worst-case task completion time, ensuring near real-time delivery of requested tasks. The formulated multi-time slot joint optimization problem is tackled via the proposed iterative algorithm based on successive convex approximation, demonstrating superior performance compared to baseline solutions.

Open Radio Access Network (RAN), with its open and disaggregated architecture, fosters innovation in traffic and congestion control in dynamic environments. However, achieving optimal user association and resource scheduling under incomplete information and varying traffic patterns remains challenging due to non-convexity and combinatorial aspects. To address this, we propose a hierarchical approach that features a heuristic, iterative successive convex approximation (SCA), and deep reinforcement learning (DRL) algorithm. The proposed solution considers a practical constraint on limited information exchange among radio units (RUs) and complies with the O-RAN Alliance’s 7.2x functional split (FS) option. This scheme optimizes performance through intelligent user association, re-source scheduling, and congestion control. The simulation results highlight its superiority over the benchmark schemes, confirming its effectiveness and demonstrating a throughput improvement of 106.27% compared to the benchmark scheme.

This paper addresses the challenge of balancing conflicting goals, namely data rate and energy harvesting (EH) in Simultaneous Wireless Information and Power Transfer (SWIPT) systems, while incorporating Reconfigurable Intelligent Surface (RIS) technology. We formulate a weighted optimization objective to address this issue, seeking to simultaneously maximize data rate, EH, and minimize transmit power utilization. The proposed approach involves optimizing time switching (TS) ratios and transmit power using a practical phase-dependent amplitude model for each RIS element’s reflectivity. To address this complex optimization problem involving ratio of concave-convex problem, the paper introduces fractional programming-based modified Dinkelbach Algorithm providing upper and lower bounds, which are then compared with Quadratic transform-related algorithms and solutions based on Karush-Kuhn-Tucker (KKT) conditions. Numerical findings highlight the effectiveness of the proposed algorithms in enhancing the overall performance of SWIPT systems with RIS technology.

The 3rd Generation Partnership Project (3GPP) has integrated a positioning service into the 5G standard since its release 16 and has subsequently introduced support for Non-Terrestrial Networks (NTN). This paper explores the integration of these advancements within a unified framework to create a novel Low Earth Orbit (LEO)-based Positioning, Navigation, and Timing (PNT) service complementary to the existing Global Navigation Satellite System (GNSS). One major challenge in implementing this service is ensuring adequate coverage for users while minimizing interference between the signals from different LEO satellites, as the user requires to receive at least four signals to estimate its position.The Positioning Reference Signal (PRS) is a valuable positioning tool, particularly in scenarios with multiple transmitters. This study focuses on analyzing the interference issues arising when these transmitters are placed onboard LEO satellites. In this satellite context, the coverage area of the beam exceeds that of terrestrial environments, resulting in differential delay. Additionally, multiplexing PRS signals from multiple satellites within the same carrier reduces the Signal-to-Interference-plus-Noise ratio (SINR) owing to the orthogonality loss. To address these challenges, this study proposes a solution based on a Cell Averaging (CA)-Constant False Alarm Rate (CFAR) algorithm. The results of this study show the relationship between the minimum elevation angle of a satellite and the degradation of the SINR owing to other satellites.

Satellite Edge Computing (SEC) is seen as a promising solution to content caching onboard by reducing retrieval latency and improving user experience. Deciding content type, number of copies, and where to cache it in a satellite con-stellation remains challenging, requiring careful consideration of many factors, such as satellite coverage, content popularity, and resource constraints. In this paper, we study resource-aware onboard content caching strategies in a multi-layer hierarchical satellite network that includes Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Orbit (GEO) satellites. The primary objective is to design a cache placement strategy that maximizes resource utilization ratio while determining the optimal number of content copies, all within the context of a time-varying network topology that primarly occurs due to the mobility of non-geostationary (NGSO) satellites. We model a novel proximity-based hierarchical hybrid content popularity model and formulate the problem as an Integer Linear Programming (ILP) problem to utilize the resources in a network. To solve the ILP problem, we propose two algorithms: Greedy based Content Cache Placement (G_CCP) and Simulated Annealing based Content Cache Placement (SA_CCP). Extensive simulations demonstrate that both G_CCP and SA_CCP are near-optimal and outperform the benchmark in terms of cache-hit ratio, resource utilization ratio, and cache fetching duration.

This paper proposes a joint optimization framework for energy-efficient linear precoding and feeder-link-beam matching design in a multi-gateway multi-beam bent-pipe satellite communication system. The proposed scheme jointly optimizes the precoding vectors at the gateway antennas and amplifying-and-matching mechanism at the satellite to maximize the system-weighted energy efficiency under the transmit power budget constraint. The technical designs are formulated into a non-convex sparsity problem consisting of a fractional-form objective function and sparsity-related constraints. To address these challenges, two iterative efficient designs are proposed by utilizing the concepts of Dinkelbach's method and the compressed-sensing approach. The simulation results demonstrate the effectiveness of the proposed scheme compared to another benchmark method.

In this paper, we study the resource allocation problem in multi-RIS-aided outdoor-to-indoor communications. Specifically, we aim to provide geographical fairness to ensure that users in different hotspot areas in a smart city can be served regardless of their location. We consider a scenario where RISs are deployed to extend coverage to indoor users in different buildings where there is limited network accessibility. This design is crucial in smart cities in the context of emergency communication and ubiquitous connectivity since it ensures service availability to as many users as possible independently of the locations. Thus, to achieve geographical fairness, we formulate a max-min fairness problem to maximize the minimum number of users served by each RIS by jointly optimizing the active precoding and RIS-based beamforming subject to power and quality of service constraints. The geographical location of users is directly linked to the RIS which means that users are served by the RIS closest to them. In this case, we ensure that a certain number of users can be supported by each RIS. The formulated problem is a mixed integer nonlinear program, which is challenging to solve directly using methods of convex optimization. Accordingly, we propose an efficient successive convex approximation-based alternating optimization algorithm to tackle the complexity of the formulated problem. The presented results show the performance gain of the proposed design in providing geographical fairness compared to the relevant benchmark schemes.

High capacity satellite networks utilize beamforming as a critical pillar to ensure efficient and reliable communication. This paper presents a scalable and constrained clustering algorithm for downlink MEO beamforming with detailed attention to load balancing. We propose a three-step approach to address the challenges of grouping users under minimum load constraints as well as antenna requirements. The low-complexity algorithm operates by first sampling available user-traffic-data while respecting Must-Link (ML) and Cannot-Link (CL) constraints such that the sampled subset is representative of the entire dataset. In the second step, a Bivariate Gaussian Mixture Model (BGMM) is applied to cluster the sampled data, incorporating the ML/CL constraints directly into an expectation-maximization (EM) process. The user/beam assignments are computed by considering both spatial proximity and operator linkage constraints to address beams compliance with the defined relationships between users. The final step involves populating these clusters with the remaining data points using a modified stable marriage algorithm, ensuring each component meets the minimum load requirements. Critical iterative refinements are performed to optimize the cluster assignments while guaranteeing convergence to a balanced and efficient beamforming solution. The effectiveness of the proposed solution is tested against a payload system with/without power flexibility, which is often hard to parameterize optimally.

Enhancing reliability in high-throughput satellites (HTS) operating in geostationary orbit (GEO) is critical, particularly under adverse channel conditions. This study investigates the potential of multi-connectivity (MC) enabled by carrier aggregation (CA) to improve the data rate, ensuring a high level of reliability of multi-band $\mathrm{K a} / \mathrm{Ku}$ GEO HTS systems. A system and channel model for the multi-band GEO satellite system is developed, and an inter-band CA algorithm is proposed. This algorithm dynamically adjusts the user link transmission scheme based on channel quality and user requirements, ranging from a single Ka-band connectivity to MC, utilizing both bands with CA via packet duplication or packet splitting. The numerical results in various weather scenarios validate the effectiveness of the algorithm, demonstrating significant improvements in system performance, reduced outage probability, and improved overall system reliability. These findings highlight the importance of MC and multi-band technologies in future $\mathrm{6 G}$ networks.

During natural disasters such as earthquakes, wildfires, hurricanes, landslides, tsunamis, CBRNE (chemical, medical, radiological, nuclear, or explosive) incidents, or terrorist threats, critical infrastructure can be severely damaged, with rescue workers facing immense obstacles. Providing help and reaching affected areas can be hazardous for human rescue personnel. To overcome this issue, 5G-enabled UAVs can play a significant role in deploying disaster-resilient networks. Such networks imply multiple challenges, including end-to-end communication reliability and low latency for UAV real-time control. In this paper, we focus on the end-to-end communication aspects of such networks. We present and implement our disaster-resilient network based on a combination of 5G-UAVs and satellite networks. We highlight the related challenges and define solutions based on 5G, Multi-access Edge Computing, UAVs, and dynamic geofencing. We integrate these solutions in an end-to-end network architecture and ultimately deploy it on a national joint 5G-satellite infrastructure to assess its performance. It performs well and demonstrates a recovery time of less than 30 seconds with $\mathbf{9 9 \%}$ network availability.

This paper introduces a joint optimization framework for user-centric beam selection and linear precoding (LP) design in a coordinated multiple-satellite (CoMSat) system, employing a Digital-Fourier-Transform-based (DFT) beamforming (BF) technique. Regarding serving users at their target SINRs and minimizing the total transmit power, the scheme aims to efficiently determine satellites for users to associate with and activate the best cluster of beams together with optimizing LP for every satellite-to-user transmission. These technical objectives are first framed as a complex mixed-integer programming (MIP) challenge. To tackle this, we reformulate it into a joint cluster association and LP design problem. Then, by theoretically analyzing the duality relationship between downlink and uplink transmissions, we develop an efficient iterative method to identify the optimal solution. Additionally, a simpler duality approach for rapid beam selection and LP design is presented for comparison purposes. Simulation results underscore the effectiveness of our proposed schemes across various settings.

The terahertz communications have the potential to revolutionize data transfer with unmatched speed and facilitate the development of new high-bandwidth applications. This paper studies the performance of downlink terahertz system assisted by beyond diagonal intelligent reconfigurable surface (BD-IRS). For enhanced energy efficiency and low cost, a joint precoding and BD-IRS phase shift design satisfying the 1-bit resolution constraints to maximize the spectral efficiency is presented. The original problem is non-linear, NP-hard, and intricately coupled, and obtaining an optimal solution is challenging. To reduce the complexity, we first transform the optimization problem into two problems and then iteratively solve them to achieve an efficient solution. Numerical results demonstrate that the proposed approach for the BD-IRS assisted terahertz system significantly enhances the spectral efficiency compared to the conventional diagonal IRS assisted system.

This work investigates the application of Beyond Diagonal Intelligent Reflective Surface (BD-IRS) to enhance THz downlink communication systems, operating in a hybrid: reflective and transmissive mode, to simultaneously provide services to indoor and outdoor users. We propose an optimization framework that jointly optimizes the beamforming vectors and phase shifts in the hybrid reflective/transmissive mode, aiming to maximize the system sum rate. To tackle the challenges in solving the joint design problem, we employ the conjugate gradient method and propose an iterative algorithm that successively optimizes the hybrid beamforming vectors and the phase shifts. Through comprehensive numerical simulations, our findings demonstrate a significant improvement in rate when compared to existing benchmark schemes, including time- and frequency-divided approaches, by approximately 30.5% and 69.9% respectively and even outperforms the STAR-IRS system by 76.99%. This underscores the significant influence of IRS elements on system performance relative to that of base station antennas, highlighting their pivotal role in advancing the communication system efficacy.

The demand for improved satellite communication (SatCom)-based broadband connectivity has led to significant technological advancements, particularly in non-geostationary orbit (NGSO) satellites. The new SatCom systems are expected to have flexible beam footprints with fully adaptable payloads while being energy-efficient. With this in mind, this paper explores using neuromorphic processors (NPs) for the in-orbit receive digital beamforming design. We specifically address the beamsteering challenges of high-speed user mobility by means of beamforming adaptation. Inspired by thinned antenna arrays, the proposed beamforming solutions are based on the least absolute shrinkage and selection operator (LASSO) and are adapted to NPs using spiking locally competitive algorithms, namely S-LCA and S-LCA with graded spikes. The proposed approaches can benefit from the energy efficiency of NPs and further reduce the SatCom payload’s power consumption by turning off as many radio frequency chains as possible without compromising the beamforming performance. Numerical experiments conducted on a real-world aeronautical dataset demonstrate that the proposed NP-oriented solutions offer performance on par with conventional optimization algorithms, with the promise of a lower energy expenditure after future implementation on dedicated hardware.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is an emerging technology that enables full-space ($\mathbf{3 6 0}$ degrees) coverage on both sides of the surface. To harness the benefits of the dynamic configuration of STAR-RIS while avoiding co-channel interference, we investigate the performance of a multi-user network assisted by STARRIS. In this setup, users are divided into multiple groups, each comprising two users located on opposite sides of the STARRIS. Orthogonal time resources are allocated to each group such that the groups are served sequentially. Based on the Gamma moment matching method, we introduce a Gamma distribution to model the product of Rician, Rayleigh and mixed fading STAR-RIS channels. We then derive exact closed-form expressions for the outage probability and diversity order per user in the proposed system model. Moreover, simulation results are provided to substantiate the analytical derived expressions. Our findings highlight a reliability trade-off associated with the number of grouped users per time slot, STAR-RIS elements, and the user targeted data rates. This balance is crucial for optimizing network performance.

Non-terrestrial networks (NTNs) play an essential role in the 6 G multi-layer architecture to provide ubiquitous coverage as well as guarantee heterogeneous requirements from vertical services. Compared to terrestrial gNodeB, flying base stations (F -BSs) in NTN are limited in terms of computation capability as well as energy budget. Therefore, it is of great importance for F-BSs to have computational and energy-efficient radio resource management (RRM) strategies. In this paper, we propose a low-complexity algorithm that jointly optimizes the transmit power and bandwidth allocation of OFDM-based multiuser downlink in NTN under flat fading scenario. The proposed algorithm exploits the flexible bandwidth design methodology to tackle the binary selection challenges, followed by a bandwidth adjustment step to force the allocated bandwidth to a multiplication of sub-channel bandwidth. More importantly, the proposed algorithm is robust against the channel estimation error. It is shown that the proposed algorithm retains the optimal solutions while significantly reduces the computational complexity, compared to both the optimal brand-and-bound (BnB) and the popular difference-of-convex (DC)-based sub-channel allocation solutions.

Satellite constellation systems are becoming more attractive to provide communication services worldwide, especially in areas without network connectivity. While optimizing satellite gateway placement is crucial for operators to minimize deployment and operating costs, reducing the number of gate-ways may require more inter-satellite link hops to reach the ground network, thereby increasing latency. Therefore, it is of significant importance to develop a framework that optimizes gateway placement, dynamic routing, and flow management in inter-satellite links to enhance network performance. To this end, we model an optimization problem as a mixed-integer problem with a cost function combining the number of gateways, flow allocation, and traffic latency, allowing satellite operators to set priorities based on their policies. Our simulation results indicate that the proposed approach effectively reduces the number of active gateways by selecting their most appropriate locations while balancing the trade-off between the number of gateways and traffic latency. Furthermore, we demonstrate the impact of different weights in the cost function on performance through comparative analysis.

This paper explores multi-connectivity (MC) techniques to enhance spectral efficiency (SE) of multi-tiered non-terrestrial networks (NTNs) in a pay-as-you-go (PAYG) service model, in which subscribers pay based on the volume of data consumed. The key objective is to maximize SE, and a resource allocation architecture is proposed to incorporate a multi-tier NTN with a hybrid gateway station (HGS) that manages the orbital satellites through co-located gateway antennas. This architecture operates with two different waveforms: 5G New Radio (NR) and DVB-S2X, both adapted into the 3rd Generation Partnership Project (3GPP) protocol stack, allowing for carrier capacity merging from all links with varying waveforms at the receiving user. To this end, a non-convex combinatorial opti-mization problem is formulated with inequality constraints and solved using a multi-agent reinforcement learning (MARL) aided resource allocation algorithm. This algorithm functions using the channel quality indicator (CQI) obtained for the different waveforms and under two channel conditions of clear sky (CS) and rain fading (RFD), to intelligently configure a resource allocation pattern which maximizes SE. The proposed algorithm is compared to proportional fairness (PF) and bottleneck max fairness (BMF) algorithms, and it outperforms in terms of SE by 11.16% and 24.15%, respectively.

This paper examines integrated satellite-terrestrial networks (ISTNs) in urban environments, where terrestrial networks (TNs) and non-terrestrial networks (NTNs) share the same frequency band in the C-band which is considered the promising band for both systems. The dynamic issues in ISTNs, arising from the movement of low Earth orbit satellites (LEOSats) and the mobility of users (UEs), are addressed. The goal is to maximize the sum rate by optimizing link selection for UEs over time. To tackle this challenge, an efficient iterative algorithm is developed. Simulations using a realistic 3D map provide valuable insights into the impact of urban environments on ISTNs and also demonstrates the effectiveness of the proposed algorithm.

In this paper, we analytically study the distribution of the main-lobe gain in a low-Earth orbit (LEO) satellite swarm scenario under imperfect phase synchronization, based on an open-loop process. The provided analytical framework determines the maximum tolerable error in phase estimation under which the desired beamforming gain can still be achieved. Consequently, we are able to determine the highest value of accuracy (worst-case scenario) in position estimation between satellites (inter-node ranging) required for phase synchronization. Notably, the analytical framework reveals that in millimeter-wave bands, the required maximum precision in inter-node ranging to guarantee just a small performance deterioration is in the order of millimetres. Finally, numerical results based on Monte Carlo simulations validate the analytical framework.

This paper delves into the dynamics of resource allocation in ultra-dense Low Earth Orbit (LEO) satellite networks within a cell-free massive MIMO framework, focusing on the impact of residual synchronization errors. We conduct various analyses to understand how these errors - encompassing time, phase, and frequency - influence the Signal-to-Interference-plus-Noise Ratio (SINR) and the average number of satellite links connected to each user. Our approach measures the effects of these remaining synchronization errors and uses these values to inform and optimize power and resource allocation decisions. The study reveals that as synchronization errors increase, the number of effective satellite links to users diminishes, consequently reducing the number of satellites actively connected to each user. This research not only highlights the critical impact of synchronization errors on network performance but also demonstrates how advanced knowledge of these error variances can significantly enhance resource allocation strategies and network efficiency in future ultra-dense LEO satellite systems.

The third generation partnership project (3GPP) has recently defined two frequency bands for direct access with satellites, which is a concrete step toward realizing the anticipated space-air-ground integrated networks. In addition, given the rapid increase in the numbers of satellites orbiting the Earth and emerging satellites applications, non-terrestrial networks (NTNs) might soon need to operate with integrated access and backhaul (lAB), which has been standardized for terrestrial networks to enable low-cost, flexible and scalable network densification. Therefore, this work investigates the performance of satellite lAB, where the same spectrum resources at a low earth orbit (LEO) satellite are utilized to provide access to a handheld user (UE) and backhaul via inter-satellite links. The UE is assumed to operate with frequency division duplex (FDD) as specified by the 3GPP, while both FDD and time division duplex (TDD) are investigated for backhauling. Our analysis demonstrate that the interference between access and backhaul links can significantly affect the performance under TDD backhauling, especially when the access link comes with high quality-of-service demands.

Coexisting satellite and terrestrial networks present a unique set of challenges and opportunities when the two networks share the same spectrum. One of these challenges is the desired signal recovery in such interference-limited scenario. In this work, we design a signal recovery scheme in coexisting satellite and terrestrial networks. We formulate an optimization problem and propose a diffusion model to perform signal recovery. The proposed diffusion model leverages the denoising mechanism to recover the signals from noisy and distorted signals. The proposed diffusion model consists of encoder to encode the input to the latent space, U-Net for denoising, attention block to integrate different relevant feature to create better context for signal recovery, and decoder to deliver the recovered signal.

In this article, we derive Rytov variance of Adaptive optics (AO) applied to atmospheric turbulence. We chose the modified von-Karman power spectrum, which covers the atmosphere's inner and outer scales. After analytical derivations, we plot Rytov variance against propagation distance and spatial frequency. We organize the plots to see the effect of atmospheric turbulence, scaling factor, and type of correction. Rytov variance is directly proportional to the propagation distance. On the other hand, it has an inverse relation between the ratio of spatial frequencies. Our results show that Rytov variance in adaptive optics corrected spectrum is low for a low turbulent regime. Moreover, we calculate the scintillation index using derived Rytov variance. We believe our results will be used to model adaptive optics corrected random phase screen approach, a model for turbulent channels in wave optics. This way, performance measurements for adaptive optics corrections could be more accurate.

The Internet of Vehicles (IoV) is undergoing swift advancements in capability and intelligence, poised to facilitate a diverse range of innovative applications. Within the IoV, edge learning empowers intelligent applications and services by leveraging data -driven tasks. Therefore, in this paper, we propose optimizing the edge learning error prediction within an edge-supported Non-Orthogonal Multiple Access (NOMA) in task-oriented communications. Specifically, we consider three autonomous vehicle perception tasks, called: object detection, traffic sign, and weather classification. For this purpose, we propose a novel approach based on the Particle Swarm Optimization (PSO) algorithm to jointly minimize the edge learning error and optimize power allocation variables. Moreover, we investigate alternative benchmark schemes, including Quantum Particle Swarm Optimization, Cuckoo Search, and Butterfly Op-timization algorithms. Satisfactorily, our simulations substantiate the superiority of the PSO algorithm over the baseline schemes, delivering superior performance with reduced computation time.

Due to the fact that large swathes on Earth still lack broadband communication coverage, especially in remote/rural areas and developing countries, there have been several attempts, starting from 3GPP Release 17, to lay out the architectural amendments needed for the integration of terrestrial networks with their non-terrestrial counterparts. Such attempts have led to recent projects regarding such integration that consider either 5G/5G-Advanced networks or more revolutionary approaches for the forthcoming 6G networks. In this manuscript, we give an overview of the architectural framework, technical innovations, and considered use cases of the Horizon Europe ETHER project.

Very High Throughput Satellite (VHTS) systems are typically deployed in Geostationary (GEO) orbit to benefit from the ubiquitous coverage of such orbits. Although the satellites deployed in GEO orbits appear as a static point in the sky from the on-ground user perspective, in practice, the GEO satellite experiences a north-south drift due to the influence of the sun and moon. Such movement may cause a small Doppler effect in the signals sent from geographically distributed cooperative gateways, which may cause significant performance loss when exploiting DVB-S2X SF-Pilot fields and for precoding purposes. This paper presents the first work investigating the effect of GEO Doppler shift in precoded cooperative multi-gateway satellite systems. In addition, to compensate for the frequency variations produced by the GEO movement, we present and test a user-gateway closed-loop compensation procedure. Results using software-defined radio (SDR) in the Lab are provided to validate the proposed method.

Low signal strength, resulting from the distance between satellites and the Earth's surface, remains a primary challenge in integrating them into terrestrial network systems. Nevertheless, the current density of satellite constellations presents an opportunity to design cost-effective receivers capable of utilizing diversity combining techniques to overcome this issue. However, any combining technique relies heavily upon time synchronism between the received signals, which until now has presented a limitation for practical applications in satellite communication systems. This paper proposes a procedure for compensating large symbol time misalignment and estimating the remaining fractional one between two DVB-S2X waveforms (from different satellites) overlapped in time, frequency, and under noise-limited scenarios. We propose a fractional-time-misalignment estimator using the data-aided early late gate (ELG) principle. We evaluate the proposed estimator using the Walsh-Hadamard-based pilot sequences available in the DVB-S2X waveforms. At the same time, we present an estimation of large misalignments based on the correlation properties of the start-of-super-frame (SOSF) field (which uses longer Walsh-Hadamard sequences than the pilots). Simulation results show that the proposed synchronization methods provide an unbiased estimation even when the noise power levels match that of the received signals.

To harness the benefits of non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RISs), we propose a novel RIS-empowered decode-and-forward (RIS-DF) relaying scheme tailored to cooperative NOMA transmissions. This integration is a promising direction for improving communication reliability, extending coverage, and enhancing the overall performance in sixth-generation (6G) wireless networks. This paper focuses on enhancing signal reception of the cell-edge users with weak channel conditions by deploying multiple RISs within cooperative NOMA systems. In this setting, we investigate system outage performance and derive closed-form analytical expressions for both cooperative NOMA and its orthogonal mul-tiple access (OMA) counterpart, which is used as a benchmark for comparison. To validate the proposed solutions, numerical results are provided to demonstrate the performance gains of the proposed scheme compared to the existing conventional DF relays developed for cooperative NOMA. Further, the impact of RIS placement within the system on performance is also examined, offering useful practical design insights.

Reflective Intelligent Surface (RIS)-assisted Ultra-Massive MIMO (Um-MIMO) systems in the terahertz (THz) spectrum are gaining attention for surpassing current wireless system limitations. However, limited diffraction at these frequen-cies typically results in low-rank Um-MIMO channels, completely reducing the potential for spatial multiplexing gains. In this work, we aim at promoting a new research direction towards the strategies for achieving high-rank Um-MIMO for RIS-assisted THz communications. The maximum achievable spatial multiplexing gain over the RIS-assisted channel is analyzed, and the optimal antennas and RIS elements placement strategy is proposed for achieving extremely high-rank Um-MIMO channels. Simulation results demonstrate that while conventional Um-MIMO THz systems display low-rank, the proposed approach enables achieving a rank on the order of hundreds for the Um-MIMO THz systems.

Developing wireless communication technologies is an ongoing process to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. Using non-orthogonal multiple access (NOMA) and backscatter communication (BC) has surfaced as an advantageous approach for enhancing energy efficiency (EE), maximizing sum rates, ensuring security, and optimizing resource allocation. NOMA permits multiple users to share time and frequency resources even without the requirement of antenna arrays, whereas BC employs ambient RF signals for low-power communication. By integrating the advantages of NOMA and BC, NOMA-based BC provides a solution for future energy-efficient and low-power networks. Despite its potential, there is a lack of a comprehensive overview of NOMA-BC, necessitating a systematic survey that covers its principles, applications, challenges, and future directions. This survey aims to bridge the gap by exploring NOMA-BC within B5G and 6G networks. We delve into its technical aspects, performance optimization techniques, and real-world applications to enhance understanding and knowledge. First, we cover topics such as enhancing EE, maximizing the sum rates, ensuring security, and analyzing performance. Our primary goal is to provide researchers and practitioners with valuable insights that enable them to grasp the capabilities and benefits of NOMA-BC. To achieve this, we comprehensively analyze the performance of various schemes by presenting detailed summary tables. These analyses cover a range of scenarios, methods, and objectives, focusing on emerging B5G technologies such as reconfigurable intelligent surfaces (RIS), visible light communication (VLC), and unmanned aerial vehicle (UAV) communication. By examining NOMA-BC’s effectiveness within these contexts, we aim to provide a holistic view of its potential and applicability in diverse technological domains. Moreover, our survey identifies and discusses open research challenges and proposes future directions to guide researchers toward unexplored areas and facilitate advancements in NOMA-BC.

This paper investigates the pilot assignment and power control problems for secure UAV communications in cell-free massive MIMO network with the user-centric scheme, where numerous distributed access points (APs) simultaneously serve multiple UAVs and terminal users. Meanwhile, there exists one UAV acting as an eavesdropper which can perform pilot spoofing attack. Considering a mixture of Rayleigh and Ricean fading channels, the APs respectively perform MMSE estimation and distributed conjugate beamforming for uplink training and downlink data transmission. Using random matrix theory, the closed-form expression for a tight lower bound on the achievable secrecy rate is derived, which enables the impact analysis of key parameters, such as power, antenna configuration, UAV height, etc. Taking into account both performance and complexity, a novel pilot assignment scheme is proposed by combining weighted graphic framework and genetic algorithm, which can actualize global search with limited iterations. The max-min power control with security constraints is then studied in parallel, which can not only enhance the network fairness but also ensure the security. Accordingly, successive convex approximation and fractional optimization are jointly utilized to solve this non-convex problem. Simulation results numerically verify the analytical results and indicate the superiority of the proposed pilot assignment and power control schemes.

Both space and ground communications have been proven effective solutions under different perspectives in Internet of Things (IoT) networks. This article investigates multiple-access scenarios, where plenty of IoT users are cooperatively served by a satellite in space and access points (APs) on the ground. Available users in each coherence interval are split into scheduled and unscheduled subsets to optimize limited radio resources. We compute the uplink ergodic throughput of each scheduled user under imperfect channel state information (CSI) and nonorthogonal pilot signals. As maximum-radio combining is deployed locally at the ground gateway and the APs, the uplink ergodic throughput is obtained in a closed-form expression. The analytical results explicitly unveil the effects of channel conditions and pilot contamination on each scheduled user. By maximizing the sum throughput, the system can simultaneously determine scheduled users and perform power allocation based on either a model-based approach with alternating optimization or a learning-based approach with the graph neural network. Numerical results manifest that integrated satellite-terrestrial cell-free massive multiple-input-multiple-output systems can significantly improve the sum ergodic throughput over coherence intervals. The integrated systems can schedule the vast majority of users; some might be out of service due to the limited power budget.

Orthogonal time frequency space (OTFS) has attracted a lot of attention as a feasible waveform applied in joint communication and radar (JCR) systems in contrast to orthogonal frequency division multiplexing (OFDM) waveform. To explore the advantages of OTFS waveform, first, a unified matrix (UM) expression is summarized by utilizing discrete fractional Fourier transform (DFrFT), and then a novel OTFS waveform based on UM expression is investigated in this article. The fractional order parameters of the proposed UM-OTFS waveform is set to the same values during preprocessing and Heisenberg transformation stages, and the UM-OTFS waveform can be converted into other waveform forms by undergoing different fractional order parameters. In addition, a three-stage sensing parameter estimation algorithm is developed for target velocity and range estimation through grid partitioning, coarse and fine estimation. Meanwhile, a low-complexity fractional zero force (ZF) or minimum mean square error (MMSE) equalizer based on lower-upper (LU) decomposition (LU-ZF/MMSE) is presented, which results in a log-linear order of complexity without any performance degradation of bite error ratio (BER) by analyzing sparsity and quasi-banded structure of the equivalent matrix. The simulation results indicate the superiority of the proposed UM-OTFS waveform in terms of sensing parameter estimation and BER performance compared with several advanced waveforms.

Sparse code multiple access (SCMA) and mobile edge computing (MEC) are two promising technologies for future Internet of Things (IoT) networks. SCMA enables large-scale connections, while MEC brings computing resources closer to user devices, resulting in faster response time and improved user experiences through task offloading. In this article, we investigate a large-scale grant-free (GF) SCMA enhanced MEC network. First, we propose the offloading protocol for the GF-SCMA enhanced MEC framework and describe the task offloading process using GF-SCMA in detail. Then, we model and analyze the performance of this network, deriving closed-form solutions for the offloading probability and SCMA ergodic rate using stochastic geometry. Additionally, we apply queueing theory to examine the impact of GF-SCMA on task latency and energy consumption in the MEC network. The accuracy of the theoretical expressions is confirmed by simulation results, demonstrating that SCMA outperforms orthogonal multiple access (OMA) in terms of increasing offloading probability and ergodic rate, as well as reducing task delay and energy consumption. Furthermore, this advantage becomes more pronounced with higher user density and task generation rate. Through parameter comparison, it is seen that increasing the pilot and codebook number of GF-SCMA can improve the performance of the proposed scheme in practical implementations.

We consider a task-effective quantization problem that arises when multiple agents are controlled via a centralized controller (CC). While agents have to communicate their observations to the CC for decision-making, the bit-budgeted communications of agent-CC links may limit the task-effectiveness of the system which is measured by the system’s average sum of stage costs/rewards. As a result, each agent should compress/quantize its observation such that the average sum of stage costs/rewards of the control task is minimally impacted. We address the problem of maximizing the average sum of stage rewards by proposing two different Action-Based State Aggregation (ABSA) algorithms that carry out the indirect and joint design of control and communication policies in the multi-agent system. While the applicability of ABSA-1 is limited to single-agent systems, it provides an analytical framework that acts as a stepping stone to the design of ABSA-2. ABSA-2 carries out the joint design of control and communication for a multi-agent system. We evaluate the algorithms -with average return as the performance metric -using numerical experiments performed to solve a multi-agent geometric consensus problem. The numerical results are concluded by introducing a new metric that measures the effectiveness of communications in a multi-agent system.

The demand for stringent interactive Quality of Service has intensified in both mobile-edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks in these systems necessitates adherence to specific deadlines or achieving extremely low latency. To optimize task scheduling performance, existing research has mainly focused on reducing the number of late jobs whose deadlines are not met. However, the primary challenge with these methods lies in the total search time and scheduling efficiency. In this article, we present the optimal job scheduling algorithm designed to determine the optimal task order for a given set of tasks. In addition, users are enabled to make informed decisions for offloading tasks based on the information provided by servers. The details of performance analysis are provided to show its optimality and low complexity with the linearithmic time$\mathcal {O}(n\log n)$, where n is the number of tasks. To tackle the uncertainty of the randomly arriving tasks, we further develop an online approach with fast outage detection that achieves rapid acceptance times with time complexity of$\mathcal {O}(n)$. Extensive numerical results are provided to demonstrate the effectiveness of the proposed algorithm in terms of the service ratio and scheduling cost.

This article investigates the combination of active reconfigurable intelligent surfaces (aRISs) with cognitive radio networks (CRns) enabled by nonorthogonal multiple access (NOMA) to enhance the capability of aRIS-based Internet of Things (IoT) systems. The proposed system model enhances overall performance and energy allocation by combining aRIS and NOMA. In this proposed system paradigm, the secondary source (SS) controls the information transmission to two secondary users (SUs) via the aRIS element. Transmission power limitations are put in place to lessen the impression that base stations are interfering with the main purpose. This article evaluates critical system performance indicators, such as outage probability (OP), achievable ergodic rate (AER), throughput, and energy efficiency (EE). Specifically, the impact of the distance between the aRIS unit and the base station on AER is explored. The examination of the correlation among SS-aRIS-Users in the presence of the Nakagami-m fading scenario is further investigated. Such discoveries offer significant perspectives for the enhancement and configuration of aRIS-NOMA frameworks in CRn, contributing to the advancement of adaptable communication infrastructures. In contrast to the traditional method that uses orthogonal multiple access (OMA), the aRIS-NOMA system proposed in CRn appears to be a promising way to improve the performance of IoT networks based on aRIS. Simulations have demonstrated significant improvements in spectral efficiency, with gains as high as 10%–20% observed. This suggests that performance has significantly improved, particularly for OP and AER. Finally, the Monte Carlo simulation confirms and strengthens these findings.

The regenerative capabilities of next-generation satellite systems offer a novel approach to design low earth orbit (LEO) satellite communication systems, enabling full flexibility in bandwidth and spot beam management, power control, and onboard data processing. These advancements allow the implementation of intelligent spatial multiplexing techniques, addressing the ever-increasing demand for future broadband data traffic. Existing satellite resource management solutions, however, do not fully exploit these capabilities. To address this issue, a novel framework called flexible resource management algorithm for LEO satellites (FLARE-LEO) is proposed to jointly design bandwidth, power, and spot beam coverage optimized for the geographic distribution of users. It incorporates multi-spot beam multicasting, spatial multiplexing, caching, and handover (HO). In particular, the spot beam coverage is optimized by using the unsupervised K-means algorithm applied to the realistic geographical user demands, followed by a proposed successive convex approximation (SCA)-based iterative algorithm for optimizing the radio resources. Furthermore, we propose two joint transmission architectures during the HO period, which jointly estimate the downlink channel state information (CSI) using deep learning and optimize the transmit power of the LEOs involved in the HO process to improve the overall system throughput. Simulations demonstrate superior performance in terms of delivery time reduction of the proposed algorithm over the existing solutions.

With the commercial deployment of low earth orbit (LEO) satellites, the future integrated 6G-satellite system represents an excellent solution for ubiquitous connectivity and high-throughput data service to massive users. Due to the heterogeneity of users’ traffic profiles, uneven traffic distribution among beams or users often occurs in LEO satellite systems. Conventional satellite payloads with fixed beam radiation patterns may result in large gaps between requested and allocated capacity. The advances of flexible satellite payloads with dynamic beamforming capabilities enable spot beams to adjust their coverage and adaptively schedule users, thus offering spatial-temporal domain flexibility. Motivated by this, as an early attempt, we investigate how adaptive beam patterns with flexible user scheduling schemes can help alleviate mismatches of requested-transmitted data in uneven-traffic and full-frequency reuse LEO systems. We formulate an optimization problem to jointly determine beam patterns, power allocation, user-LEO association, and user-slot scheduling. The problem is identified as mixed-integer nonconvex programming. We propose an efficient iterative algorithm to solve the problem by first determining beam patterns and user associations at the frame scale, followed by optimizing power allocation and user scheduling at the timeslot scale. The four-decision components are iteratively updated to improve the overall performance. Numerical results demonstrate the benefits brought by adaptive beam patterns and their effectiveness in reducing the mismatch effect in uneven-traffic LEO systems.

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold:$i$) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units;$ii$) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and$iii$) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

Wireless-based federated learning (FL), as an emerging distributed learning approach, has been widely studied for 6G systems. When the paradigm shifts from terrestrial to non-terrestrial networks (NTN), FL may need to address several open challenges, e.g., the limited service time of low earth orbit (LEO) satellites, the straggler issue in synchronous FL, and time-efficient uploading and aggregation for massive devices. In this work, we exploit the synergy of LEO and FL for future integrated 6G-satellite systems by taking advantage of ubiquitous wireless access provided by LEO and appealing characteristics of collaborative training and data privacy preservation in FL. The studied LEO-FL framework may need to improve multi-metric performance in practice. Different from most FL works, we simultaneously improve the communication-training efficiency and local training accuracy from a multi-objective optimization (MOO) perspective. To solve the problem, we propose a decomposition and meta-deep reinforcement learning based MOO algorithm for FL (DMMA-FL), aiming at adapting to the dynamic satellite-terrestrial environments, achieving efficient uploading and aggregation, and approaching Pareto optimal sets. Compared to single-objective optimization, heuristics-based, and learning-based MOO algorithms, the effectiveness and advantages of the proposed LEO-FL framework and DMMA-FL algorithm are assessed on MNIST and CIFAR-10 datasets.

Reconfigurable intelligent surface (RIS) has recently gained significant interest as an emerging technology for future wireless networks thanks to its potential for improving the coverage in challenging propagation environments. This paper studies an RIS-assisted communication system, where a source transmits data to a destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, we derive closed-form expressions for the coverage probability and ergodic rate, which explicitly unveil the impact of the propagation environment and the RIS on the system performance. Besides the optimization of the RIS phase profile, we formulate an RIS placement optimization problem with the aim of maximizing the coverage probability by relying only on partial channel state information. An efficient algorithm is proposed based on the gradient ascent method. Simulation results are illustrated in order to corroborate the analytical framework and findings. The proposed RIS phase profile is shown to outperform several heuristic benchmark schemes in terms of outage probability and ergodic rate. In addition, the proposed RIS placement strategy provides an extra degree of freedom that remarkably improves the system performance.

The rapid deployment of Low Earth Orbit (LEO) satellites, driven by technological advancements and cost reductions, has led to the emergence of mega-constellations for satellite-based Internet services. Among them, the networking problem has become a significant area of research. Specifically, breaking away from the traditional Grid-Mesh + topology schemes has been a focal point. Although some related studies have been conducted, there are still three major challenges in optimizing the topology of laser inter-satellite links: the lack of theoretical derivation for satellite visibility, the need for comprehensive modeling goals, and the absence of network simulation verification. To address these challenges, we introduce the theory of visibility analysis of laser terminals in real scenarios and propose a theoretical model of topologically feasible solutions for integer linear programming. Furthermore, a mathematical model is developed that considers time delay, hop count, and link load as optimization objectives. The Many-objective Non-Grid-Mesh Topology Optimization (M-NGTO) algorithm, based on Non-dominated Sorting Genetic Algorithm III (NSGA-III), is then designed to effectively optimize the topology. The optimized Non-Grid-Mesh topology is validated through packet-level simulations on the Hypatia platform. Additionally, consistency analysis is performed to establish agreement between theory and simulation results. The results of simulations conducted on two megaLEO satellite Internet constellations, GW and Starlink, demonstrate that the performance of the Non-Grid-Mesh topology in the above three optimization objectives is approximately 39.11% better than the average of the Grid-Mesh + topology, confirming the effectiveness of the M-NGTO algorithm. The findings have significant implications for enhancing the communication performance and load balancing of satellite Internet systems.

The advent of Satellite as a Service (SaaS) platforms has empowered satellite service providers (SPs) to rent portions of satellite capacity from infrastructure providers (IPs) to cater to the diverse demands of their users across multiple satellite services. To effectively manage costs and maintain a high Quality of Experience (QoE) for numerous concurrent connections, SPs should secure flexible capacity from IPs. However, the irregular and unpredictable nature of traffic demands from various applications complicates the capacity-renting framework. This study presents a dynamic capacity allocation framework that efficiently handles diverse traffic flows with varying arrival rates, aiming to minimize rental costs while meeting blocking probability and QoE requirements. Utilizing theMt/Mt/1 queuing model and a continuous-time Markov chain, the technical designs are framed as a statistical optimization problem. In this context, the system waiting-queue lengths are estimated using the transient probabilities of Kolmogorov equations. Subsequently, cumulative distribution functions are employed to re-formulate this stochastic optimization problem into a convex form, which can be tackled through the Lagrangian duality method.Through extensive simulations and numerical assessments, we illustrate our method’s efficacy, with the proposed algorithm outperforming benchmarks by reducing costs by up to 9.85% and 3.1%.

This paper works on the detection of physical random access channel (NPRACH) in Narrowband Internet of Things (NB-IoT) system. The frequency hopping preamble design and increasing number of IoT terminals lead to inter-cell interference among different cells, resulting in inevitable increase of false alarm rate. Due to the ambiguity between preamble and interference, it is a great challenge for NPRACH detection methods to achieve low false alarm rate when having strong interference. In this paper, we analyze the difference between preamble and interference in the propagation environments of NPRACH signals in the 2-dimensional Fast Fourier Transformation (2-D FFT) domain. Then we propose a deep learning-based NPRACH detection method, dubbed Mask Assisted Anti-Interference Universal Detection Scheme (MIUS), in the 2-D FFT domain for preamble detection with inter-cell interference in different repetition cases. In the proposed MIUS, the Mask-ResNet Block is designed as a building block to extract features distinguishing the preamble and interference based on masking operations. Our proposed MIUS utilizes the Mask-ResNet Block in a separate manner to detect the preambles in sequential repetitions across different repetition cases. Simulation results show that MIUS can simultaneously maintain the low false alarm rate and achieve high detection accuracy in low Signal to Interference and Noise Ratio (SINR) regime in all repetition cases.

This paper addresses the virtual network embedding (VNE) problem in integrated satellite-terrestrial networks (STNs). VNE consists of mapping virtual network functions (VNFs) in a service function chain (SFC) to physical nodes and mapping virtual links connecting the VNFs to physical links. Compared to terrestrial networks, VNE in STNs is challenging due to the movement of the non-geostationary orbit (NGSO) satellites and limited onboard processing resources. A static VNE strategy fails to efficiently address the diverse requirements of heterogeneous service requests in such a highly dynamic topology. In addition, existing solutions do not consider the capacity limitation and connectivity duration of inter-satellite links (ISLs) and ground-to-satellite links, which are essential parameters for deploying a VNE strategy in STNs. This work proposes a heuristic solution and reinforcement learning (RL)-based improved solution for the VNE scheme that can dynamically modify the existing VNF deployment strategy to maximize the average service acceptance rate and revenue. The RL agent selects a suitable VNE strategy for each service request considering the time-varying network topology and service’s requirements. The proposed scheme increases the service acceptance ratio by 19.95% compared to the benchmark TS-MAPSCH.

We consider the problem of estimating time-varying quantum noise. Specifically, we focus on Pauli qubit noise with time-variations and attempt to construct the most accurate instantaneous channel description. To this end, we propose an adaptive framework of simultaneous communication and parameter estimation (SCAPE) that efficiently and accurately estimates the time-varying Pauli channel while communicating reliably over the channel being estimated. This adaptive framework gives the informed control of communication rate–parameter estimation tradeoff to communicating parties. Interestingly, this adaptive SCAPE requires post-processing entirely on the receiver’s end and minimal feedback to the sender to increase, decrease, or continue with the same code rate of employed error correcting code. This procedure can be particularly useful in time-varying quantum channels with natural periodic deviations in channel conditions, e.g., in satellite communication channels.

Reconfigurable intelligent surfaces (RISs) have emerged as a game-changing technology to improve wireless network performance by intelligently manipulating and customizing the physical propagation environment. Such capability is especially important for the application of smart cities as it increases wireless service offers and quality to end-users. In this paper, we aim to maximize the number of served users in a challenging RIS-aided smart city street by jointly optimizing the multislot scheduling, precoding, and passive RIS-based beamforming design under quality of service and power constraints. Multislot scheduling is introduced in order to benefit from additional time diversity and thus better exploit the available degrees of freedom. The formulated problem is a mixed integer nonlinear programming, which is NP-hard. To solve the problem with affordable complexity, we develop an efficient iterative algorithm based on binary variable relaxation, alternating optimization, and successive convex approximation techniques. Simulation results demonstrate the superiority of the proposed design over the design without RIS and the design without scheduling, especially in the presence of a large number of users. In addition, results illustrate that by introducing a quality of service margin, the proposed design can improve its robustness to outdated channel state information in mobility scenarios.

Satellite Edge Computing (SEC) is seen as a promising solution for deploying network functions in orbit to provide ubiquitous services with low latency and bandwidth. Software Defined Networks (SDN) and Network Function Virtualization (NFV) enable SEC to manage and deploy services more flexibly. In this paper, we study a dynamic and topology-aware VNF mapping and scheduling strategy within an SDN/NFV-enabled SEC infrastructure. Our focus is on meeting the stringent requirements of mission-critical (MC) applications, recognizing their significance in both satellite-to-satellite and edge-to-satellite communications while ensuring service delay margin fairness across various time-sensitive service requests. We formulate the VNF mapping and scheduling problem as an Integer Nonlinear Programming problem (INLP), with the objective ofminimaxfairness among specified requests while considering dynamic satellite network topology, traffic, and resource constraints. We then propose two algorithms for solving theINLPproblem: Fairness-Aware Greedy Algorithm for Dynamic VNF Mapping and Scheduling (FAGD_MASC) and Fairness-Aware Simulated Annealing-Based Algorithm for Dynamic VNF Mapping and Scheduling (FASD_MASC) which are suitable for low and high service arrival rates, respectively. Our extensive simulations demonstrate that bothFAGD_MASCandFASD_MASCapproaches are very close to the optimization-based solution and outperform the benchmark solution in terms of service acceptance rates.

Next-generation (NextG) cellular networks are expected to evolve towards virtualization and openness, incorporating reprogrammable components that facilitate intelligence and real-time analytics. This paper builds on these innovations to address the network slicing problem in multi-cell open radio access wireless networks, focusing on two key services: enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communications (URLLC). A stochastic resource allocation problem is formulated with the goal of balancing the average eMBB data rate and its variance, while ensuring URLLC constraints. A distributed learning framework based on the Deep Reinforcement Learning (DRL) technique is developed following the Open Radio Access Networks (O-RAN) architectures to solve the formulated optimization problem. The proposed learning approach enables training a global machine learning model at a central cloud server and sharing it with edge servers for executions. Specifically, deep learning agents are distributed at network edge servers and embedded within the Near-Real-Time Radio access network Intelligent Controller (Near-RT RIC) to collect network information and perform online executions. A global deep learning model is trained by a central training engine embedded within the Non-Real-Time RIC (Non-RT RIC) at the central server using received data from edge servers. The performed simulation results validate the efficacy of the proposed algorithm in achieving URLLC constraints while maintaining the eMBB Quality of Service (QoS).

Non-Geostationary Orbit satellite (NGSO) is an essential element in 5G Non-Terrestrial Networks (NTNs), which can operate either independently or as complementary parts to terrestrial systems to boost the network capacity, coverage and resilience. Due to the highly dynamic topologies, one of the challenges in NGSO is how to harmonize the network virtualized resources to satisfy diverse quality of service requirements in an efficient manner. In this paper, we investigate Virtual Network Embedding (VNE) for integrated NGSO-terrestrial systems while considering dynamic topologies. We propose a Dynamic Topology-Aware VNE (DTA-VNE) algorithm which, given priori information about the network’s evolution over time, can plan the embedding for each Virtual Network Request (VNR) over its lifetime. In a highly dynamic environment, the VNE decision can be varying for different VNRs at the expense of a considerable cost of migrating traffic and reconfiguring resources. The proposed DTA-VNE aims at minimizing this migration cost and thus avoids unnecessary re-mappings. In numerical results, the effectiveness of the proposed DTA-VNE is demonstrated with much lower migration cost than the conventional implementations. We show the benefit of planning for more time slots, in terms of migration cost, and the impact on the computation time, due to the increasing problem complexity. The trade-off between these two performance metrics is studied. Furthermore, we verify the efficiency of DTA-VNE in a MultI-layer awaRe SDN-based testbed for SAtellite-Terrestrial networks (MIRSAT). Finally, the application of DTA-VNE to novel scenarios such as mega LEO constellations is discussed, highlighting the challenges and possible solutions.

Beam-hopping (BH) technology, integral to multi-beam satellite systems, adapts beam activation to the variable communication demands of terrestrial users. The optimization of power allocation and beam illumination scheduling constitutes the core design challenge in BH systems, especially under the constraint on a limited number of simultaneously active beams due to restricted radio frequency chain availability. This paper proposes a two-stage BH design solution, which minimizes energy consumption in BH satellite communications while accommodating the heterogeneous demands of users. The first stage addresses the coupling variables of power and beam status by recasting the allocation and scheduling problem through a statistical lens, thus breaking down the intricate relationship between variables. To manage the resulting non-convex challenge, we propose an iterative method that capitalizes on the optimality conditions inherent to this problem. This method is designed to procure a statistically-informed solution that aligns with our reformulated interpretation. Subsequently, the second stage maps this solution into a concrete beam illumination schedule, employing binary quadratic programming techniques. A penalty-based iterative method is applied, ensuring convergence to a locally optimal solution. Through numerical simulations, the proposed framework has been validated for its efficacy in improving energy efficiency and accurately matching demands.

This research exploits the applications of reconfigurable intelligent surface (RIS)-assisted multiple input multiple output (MIMO) systems, specifically addressing the enhancement of communication reliability with modulated signals. Specifically, we first derive the analytical downlink symbol error rate (SER) of each user as a multivariate function of both the phase-shift and beamforming vectors. The analytical SER enables us to obtain insights into the synergistic dynamics between the RIS and MIMO communication. We then introduce a novel average SER minimization problem subject to the practical constraints of the transmitted power budget and phase shift coefficients, which is NP-hard. By incorporating the differential evolution (DE) algorithm as a pivotal tool for optimizing the intricate active and passive beamforming variables in RIS-assisted communication systems, the non-convexity of the considered SER optimization problem can be effectively handled. Furthermore, an efficient local search is incorporated into the DE algorithm to overcome the local optimum, and hence offer low SER and high communication reliability. Monte Carlo simulations validate the analytical results and the proposed optimization framework, indicating that the joint active and passive beamforming design is superior to the other benchmarks.

This paper investigates the antenna selection problem in massive multiple-input multiple-out (MIMO) systems under incomplete channel state information (CSI), with a particular interest on risk-aware planning subjected to practical constraints such as transmit power budgets and quality of services (QoS). Due to a very large number of antennas, obtaining complete channel measurements becomes a cost-prohibitive, energy-inefficient and spectral-inefficient task. To reduce pilot overhead, incomplete CSI and antenna selection (AS) are expected in practical massive MIMO systems. However, most existing AS algorithms heavily rely on the complete CSI, which imposes a high probability of violating the practical constraints in the scenarios of our interests. Motivated by this, we propose a joint channel prediction and antenna selection framework (JCPAS) which efficiently performs AS and is robust against the incomplete CSI and practical constraints. The proposed framework comprises i) a channel tracker which estimates the channel dynamics based on historical incomplete observations, and ii) a risk-aware Monte Carlo tree search (RA-MCTS) algorithm which utilizes the estimated channel dynamics to select antennas in a risk-aware manner. Simulation results show that the proposed RA-MCTS not only achieves much lower energy consumption compared to the existing typical algorithms, but also significantly reduces the probability of violating the practical constraints.

The sixth-generation (6G) wireless network landscape is evolving toward enhanced programmability, virtualization, and intelligence to support heterogeneous use cases. The O-RAN Alliance is pivotal in this transition, introducing a disaggregated architecture and open interfaces within the 6G network. Our paper explores an intelligent traffic steering (TS) scheme within the Open radio access network (RAN) architecture, aimed at improving overall system performance. Our novel TS algorithm efficiently manages diverse services, improving shared infrastructure performance amid unpredictable demand fluctuations. To address challenges like varying channel conditions, dynamic traffic demands, we propose a multi-layer optimization framework tailored to different timescales. Techniques such as long-short-term memory (LSTM), heuristics, and multi-agent deep reinforcement learning (MADRL) are employed within the non-real-time (non-RT) RAN intelligent controller (RIC). These techniques collaborate to make decisions on a larger timescale, defining custom control applications such as the intelligent TS-xAPP deployed at the near-real-time (near-RT) RIC. Meanwhile, optimization on a smaller timescale occurs at the RAN layer after receiving inferences/policies from RICs to address dynamic environments. The simulation results confirm the system’s effectiveness in intelligently steering traffic through a slice-aware scheme, improving eMBB throughput by an average of 99.42% over slice isolation.

Integrating sparse code multiple access (SCMA) and mobile edge computing (MEC) into the Internet of Things (IoT) networks can enable efficient connectivity and timely computation for resource-limited IoT users. This paper studies the computation rate maximization problem under task deadline constraints in dynamic SCMA-MEC networks. Specifically, we propose a predictive deep Q-network for SCMA resource allocation and computation offloading (PQ-RACO) algorithm for single-cell scenarios, where IoT devices use long short-term memory (LSTM) networks to predict the states and actions of other agents. However, the PQ-RACO algorithm is not scalable for increasing numbers of IoT devices. To address this issue, an improved multi-agent deep Q-network for SCMA resource allocation and computation offloading algorithm (MQ-RACO) is proposed for multi-cell scenarios. The algorithm is a centralized training and decentralized execution (CTDE) multi-agent reinforcement learning (MARL) algorithm with explicit rewards, which is tailored to the special structure of joint rewards. Simulation results demonstrate that the proposed algorithm outperforms several state-of-the-art MARL algorithms and other benchmark schemes in terms of convergence speed and computation rate.

This paper investigates the uplink transmission of an integrated satellite-terrestrial network, wherein the low-earth-orbit (LEO) satellites provide backhaul services to isolated cellular base stations (BSs) for forwarding mobile user (UE) data to the core network. In this integrated system, the high mobility of LEO satellites (LEOSats) introduces significant challenges in managing radio resource allocation (RA), as well as the associations between UEs, BSs, and LEOSats for supporting users’ demands efficiently, while also dynamically balancing the capacity of UE-BS access and BS-LEO backhaul links. Regarding these critical issues, the paper aims to jointly optimize the two-tier UE-BS and BS-LEOSat association, sub-channel assignment, bandwidth allocation, and power control to meet users’ demands in the shortest transmission time. This optimization problem, however, falls into the category of mixed-integer non-convex programming, making it very challenging and requiring advanced solution techniques to find optimal solutions. To tackle this complex problem efficiently, we first develop an iterative centralized algorithm by utilizing convex approximation and compressed-sensing-based methods to deal with binary variables. Furthermore, for practical implementation and to offload computation from the central processing node, we propose a Dec-Alg that can be implemented in parallel at local controllers and achieve efficient solutions. Numerical results are also illustrated to strengthen the effectiveness of our proposed algorithms compared to traditional greedy and benchmark algorithms.

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% — 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

Stacked intelligent metasurfaces (SIM) is a revolutionary technology, which can outperform its single-layer counterparts by performing advanced signal processing relying on wave propagation. In this work, we exploit SIM to enable transmit precoding and receiver combining in holographic multiple-input multiple-output (HMIMO) communications, and we study the achievable rate by formulating a joint optimization problem of the SIM phase shifts at both sides of the transceiver and the covariance matrix of the transmitted signal. Notably, we propose its solution by means of an iterative optimization algorithm that relies on the projected gradient method, and accounts for all optimization parameters simultaneously. We also obtain the step size guaranteeing the convergence of the proposed algorithm. Simulation results provide fundamental insights such the performance improvements compared to the single-RIS counterpart and conventional MIMO system. Remarkably, the proposed algorithm results in the same achievable rate as the alternating optimization (AO) benchmark but with a less number of iterations.

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective solution to improve wireless communication performance through just passive reflection. Recently, the concept of simultaneously transmitting and reflecting RIS (STAR-RIS) has appeared but the study of minimum signal-to-interference-plus-noise ratio (SINR) and the impact of hardware impairments (HWIs) remain open. In addition to previous works on STAR-RIS, we consider a massive multiple-input multiple-output (mMIMO) base station (BS) serving multiple user equipments (UEs) at both sides of the RIS. Specifically, in this work, focusing on the downlink of a single cell, we derive the minimum SINR obtained by the optimal linear precoder (OLP) with HWIs in closed form. The OLP maximises the minimum SINR subject to a given power constraint for any given passive beamforming matrix (PBM). Next, we obtain deterministic equivalents (DEs) for the OLP and the minimum SINR, which are then used to optimise the PBM. Notably, based on the DEs and statistical channel state information (CSI), we optimise simultaneously the amplitude and phase shift by using a projected gradient ascent algorithm (PGAM) for both energy splitting (ES) and mode switching (MS) STAR-RIS operation protocols with reduced feedback, which is quite crucial for STAR-RIS systems that include the double number or variables compared to reflecting only RIS. Simulations verify the analytical results, shed light on the impact of HWIs, and demonstrate the better performance of STAR-RIS compared to conventional RIS. Also, a benchmark full instantaneous CSI (I-CSI) based design is provided and shown to result in higher SINR but lower net achievable sum-rate than the statistical CSI based design because of large overhead associated with the acquisition of full I-CSI acquisition. Thus, not only do we evaluate the impact of HWIs but we also propose a statistical CSI based design that provides higher net sum-rate with low overhead and complexity.

Reconfigurable intelligent surfaces (RIS)-assisted massive multiple-input multiple-output (mMIMO) is a promising technology for applications in next-generation networks. However, reflecting-only RIS provides limited coverage compared to a simultaneously transmitting and reflecting RIS (STAR-RIS). Hence, in this paper, we focus on the downlink achievable rate and its optimization of a STAR-RIS-assisted mMIMO system. Contrary to previous works on STAR-RIS, we consider mMIMO, correlated fading, and multiple user equipments (UEs) at both sides of the RIS. In particular, we introduce an estimation approach of the aggregated channel with the main benefit of reduced overhead links instead of estimating the individual channels. Next, leveraging channel hardening in mMIMO and the use-and-forget bounding technique, we obtain an achievable rate in closed-form that only depends on statistical channel state information (CSI). To optimize the amplitudes and phase shifts of the STAR-RIS, we employ a projected gradient ascent method (PGAM) that simultaneously adjusts the amplitudes and phase shifts for both energy splitting (ES) and mode switching (MS) STAR-RIS operation protocols. By considering large-scale fading, the proposed optimization can be performed every several coherence intervals, which can significantly reduce overhead. Considering that STAR-RIS has twice the number of controllable parameters compared to conventional reflecting-only RIS, this accomplishment offers substantial practical benefits. Simulations are carried out to verify the analytical results, reveal the interplay of the achievable rate with fundamental parameters, and show the superiority of STAR-RIS regarding its achievable rate compared to its reflecting-only counterpart.

With the emergence of ultra-reliable and low latency communication (URLLC) services, link adaptation (LA) plays a pivotal role in improving the robustness and reliability of communication networks via appropriate modulation and coding schemes (MCS). LA-based resource management schemes in both physical and medium access control layers can significantly enhance the system performance in terms of throughput, latency, reliability, and quality of service. Increasing the number of retransmissions will achieve higher reliability and increase transmission latency. In order to balance this trade-off with improved link performance for URLLC services, we study a joint subcarrier and power allocation problem to maximize the achievable sum-rate under an appropriate MCS. The formulated problem is mixed-integer nonconvex programming which is challenging to solve optimally. In addition, a direct application of standard optimization techniques is no longer applicable due to the complication of the effective signal-to-noise ratio (SNR) function. To overcome this challenge, we first relax the binary variables to continuous ones and introduce additional variables to convert the relaxed problem into a more tractable form. By leveraging the successive convex approximation method, we develop a low-complexity iterative algorithm that guarantees to achieve at least a locally optimal solution. Simulation results are provided to show the fast convergence of the proposed iterative algorithm and demonstrate the significant performance improvement in terms of the achievable sum-rate, compared with the conventional LA approach and existing retransmission policy.

This paper proposes a robust beamforming algorithm for massive multiple-input multiple-output (MIMO) low earth-orbit (LEO) satellite communications under uncertain channel conditions. Specifically, a Conditional Value at Risk (CVaR)-based stochastic optimization problem is formulated to optimize the hybrid digital and analog precoding aiming at maximizing the network data rate while considering the required Quality-of-Service (QoS) by each ground user. In particular, the CVaR is used as a risk measure of the downlink data rate to capture the high dynamic and random channel variations of the satellite network, achieving the required QoS under the worst-case scenario. Utilizing the decomposition and relaxation optimization techniques, an alternating optimization algorithm is developed to solve the formulated problem. Simulation results demonstrate the efficacy of the proposed approach in achieving the QoS requirements under uncertain satellite channel conditions.

This paper studies multiple-access scenarios where users are cooperatively served by the satellite and terrestrial access points (APs). We derive the uplink ergodic throughput of scheduled users under practical conditions where maximum-radio combining is exploited locally at the ground gateway and the APs. The analytical result explicitly unveils the effects of pilot contamination and channel conditions on the achievable throughput of each scheduled user in the uplink data transmission. The system can explicitly define the scheduled users and perform the power allocation by maximizing the sum throughput using either model-based or learning-based approaches. Numerical results demonstrate that the cooperation between space and ground systems brings superior throughput improvements over either space or ground networks. Even though most users can be simultaneously served, some may not be scheduled in each coherence interval due to limited radio resources.

Task-oriented communication design (TOCD) has gained significant attention from the research community due to its numerous promising applications in domains such as$\text{IoT}$and industry 4.0. This paper introduces an innovative approach to designing scalable task-oriented quantization and communications in cooperative multi-agent systems (MAS). Our proposed approach leverages the TOCD framework and the concept of the value of information$(\text{VoI})$to facilitate efficient communication of quantized observations among agents while maximizing the average return performance of the MAS-a metric that measures the task effectiveness of the MAS. Learning the VoI becomes a prohibitively large computational problem as the number of agents grows in the MAS. To address this challenge, we present a three-step framework. First, we employ reinforcement learning (RL) to learn the VoI for a two-agent, rather than for the original$N$-agent system, reducing the computational costs associated with obtaining the value of information. Next, we design the quantization policy for a MAS with N agents, utilizing the learned VoI across a range of bit-budgets. The resulting quantization strategy for agents' observations, ensures that more valuable observations are communicated with greater precision. Finally, we apply RL to learn the agents' control policies, while adhering to the quantization policies designed in the previous step. Our analytical results showcase the effectiveness of the proposed framework across a wide range of problems. Numerical experiments demonstrate improvements in reducing the computational complexity required for obtaining VoI by five orders of magnitude in TOCD for MAS problems while compromising less than 1% on the average return performance of the MAS.

In this paper, we aim to enable an intelligent traffic (TS) steering application in the open radio access network (O-RAN) by jointly optimizing the flow-split distribution, congestion control and scheduling (i.e. so-called JFCS). To do so, we develop a multi-layer optimization framework based on network utility maximization and stochastic optimization methods. The proposed algorithm provides fast convergence, long-term utility-optimality and significantly low latency compared to state-of-the-art RAN approaches. In particular, our main contributions are as follows: i) we propose the novel JFCS framework to efficiently and adaptively route traffic to indented users in appropriate radio units, and ii) we develop low-complexity algorithms to effectively solve the JFCS problem in different time scales, enabling a closed-loop control of the TS in the O-RAN context. The insights presented in this work will pave the way for 0- RAN that are completely automated, offering improved control and flexibility.

This paper proposes a novel transmission scheme that leverages the synergistic benefits of multiple reconfigurable intelligent surfaces (RISs) and cooperative non-orthogonal multi-ple access (NOMA) to improve both spectral and energy efficiency in 5G and beyond wireless systems. The proposed scheme involves selecting one of cell-center users to an access point (AP), which then relays the data to a cell-edge user without a direct connection to the AP with assistance of multiple distributed RISs. In this respect, we propose a cooperative NOMA scheme and develop a user selection strategy for two RIS exploitation scenarios: (i) the RIS selection scheme where the transmission between the selected cell-center and cell-edge users is performed via a selected RIS, and (ii) the distributed RIS scheme where all RISs assist in the end-to-end communications. The system outage performance is statistically characterized and its closed-form expressions are derived. Additionally, simulations are carried out to validate the analytical expressions and compare the performance of these two schemes to a single RIS-assisted network under different numbers of RISs, reflecting elements, and cell-center users. The results show that combining multiple RIS in cooperative NOMA can yield high spectral efficiency gains and improved outage performance, even with a low number of RIS elements.

Future generation of Wireless Communications encompasses massive connectivity of energy-starved and heavy-data driven billions of Internet-of-Things (IoT) devices. In this vein, Reconfigurable Intelligent Surface (RIS) holds great promise while providing improved performance and efficiency in terms of energy, cost and spectrum. Simultaneous Wireless Information and Power Transmission (SWIPT) in conjunction with RIS makes a great partnership to suffice the IoT demands. This paper examines a SWIPT-IoT system that utilizes power-splitting (PS) and non-linear energy harvesting (EH) model to achieve more data rates in constraint environment. The IoT node receives both energy and information from the base station via RIS. We present a combined problem that aims to optimize the individual objectives of rate, EH, and transmit power, while taking into account various sets of quality-of-service (QoS)-based constraints. We introduce a set of iterative optimization algorithm that utilize a divide-and-conquer approach to effectively solve the aforementioned problems. Based on our computational results, we confer that in order to reap the benefits of PS-based SWIPT-IoTs, it is imperative to increase the size of RIS and position them in optimal proximity to both the base station and the user.

In this paper, a novel robust beamforming for an intelligent reflecting surface (IRS) assisted FD system is presented. Since perfect channel state information (CSI) is often challenging to acquire in practice, we consider the case of imperfect CSI and adopt a statistically robust beamforming approach to maximize the ergodic weighted sum rate (WSR). We also analyze the achievable WSR of an IRS-assisted FD with imperfect CSI, for which the lower and the upper bounds are derived. The ergodic WSR maximization problem is tackled based on the expected Weighted Minimum Mean Squared Error (WMMSE), which is guaranteed to converge to a local optimum. The effectiveness of the proposed design is investigated with extensive simulation results. It is shown that our robust design achieves significant performance gain compared to the naive beamforming approaches and considerably outperforms the robust Half-Duplex (HD) system.

This paper investigates joint device identification, channel estimation, and signal detection for LEO satellite-enabled grant-free random access, where a multiple-input multiple-output (MIMO) system with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link (TSL). We divide the receiver structure into three modules: first, a linear module for identifying active devices, which leverages the generalized approximate message passing (GAMP) algorithm to eliminate inter-user interference in the delay-Doppler domain; second, a non-linear module adopting the message passing algorithm to jointly estimate channel and detect transmit signals; the third aided by Markov random field (MRF) aims to explore the three dimensional block sparsity of channel in the delay-Doppler-angle domain. The soft information is exchanged iteratively between these three modules by careful scheduling. Furthermore, the expectation-maximization algorithm is embedded to learn the hyperparameters in prior distributions. Simulation results demonstrate that the proposed scheme outperforms the conventional methods significantly in terms of activity error rate, channel estimation accuracy, and symbol error rate.

The sixth-generation networks envision the terahertz (THz) band as one of the key enabling technologies because of its ultrawide bandwidth. To combat the severe attenuation, the THz wireless systems employ large arrays, wherein the near-field beam-split (NB) severely degrades the accuracy of channel acquisition. Contrary to prior works that examine only either narrowband beamforming or far-field models, we estimate the wideband THz channel via an NB-aware orthogonal matching pursuit (NBA-OMP) approach. We design an NBA dictionary of near-field steering vectors by exploiting the corresponding angular and range deviation. Our OMP algorithm accounts for this deviation thereby ipso facto mitigating the effect of NB. Numerical experiments demonstrate the effectiveness of the proposed channel estimation technique for wideband THz systems.

Satellite edge computing (SEC) has emerged as a promising technology to deliver network services to remote users. Coupled with software-defined networking (SDN) and network function virtualization (NFV), SEC can provide flexibility, agility, and efficiency when allocating computing and storage resources. However, there still remain a number of technical challenges in terms of fairness and efficiency of the allocation of physical resources in service provisioning, especially in a satellite network with limited resources and dynamic traffic demands. In this paper, we investigate a dynamic virtual network function (VNF) mapping and scheduling in an SDN/NFV-enabled SEC environment to maximize the fairness between competing services in terms of the E2E delay safe margin to enhance the service acceptance rates in the network. We mathematically formulate the VNF mapping and scheduling problem as a nonlinear integer optimization problem, which is NP-hard. In order to effectively solve the problem, this paper proposes a two-stage heuristic dynamic VNF mapping and scheduling algorithm: i) the path selection algorithm returns all possible paths for a given service request with multiple VNFs, which are sorted in ascending order based on their E2E service delay and executed offline, and ii) the dynamic VNF mapping and scheduling algorithm performs online dynamic remapping and rescheduling of VNFs. Finally, numerical results are provided to demonstrate that the proposed algorithm offers a higher service acceptance rate, computing resource utilization efficiency, and higher fairness compared to a benchmark scheme.

Beam Placement (BP) is a well-known problem in Low-Earth Orbit (LEO) satellite communication (SatCom) systems, which can be modelled as an NP-hard clique cover problem. Recently, quantum computing has emerged as a novel technology which revolutionizes how to solve challenging optimization problems by formulating Quadratic Unconstrained Binary Optimization (QUBO), then preparing Hamiltonians as inputs for quantum computers. In this paper, we study how to use quantum computing to solve BP problems. However, due to limited hardware resources, existing quantum computers are unable to tackle large optimization spaces. Therefore, we propose an efficient Hamiltonian Reduction method that allows quantum processors to solve large BP instances encountered in LEO systems. We conduct our simulations on real quantum computers (D-Wave Advantage) using a real dataset of vessel locations in the US. Numerical results show that our algorithm outperforms commercialized solutions of D-Wave by allowing existing quantum annealers to solve 17.5 times larger BP instances while maintaining high solution quality. Although quantum computing cannot theoretically overcome the hardness of BP problems, this work contributes early efforts to applying quantum computing in satellite optimization problems, especially applications formulated as clique cover/graph coloring problems.

Quantum technologies have recently scaled up from laboratories into commercial applications thanks to the rapid technical developments and the growing investments in quantum computing. These developments open up the way for the emergence of the so-called noisy intermediate-scale quantum (NISQ) devices, where the quantum approximation optimization algorithms (QAOAs) represent a class of algorithms tailored for the NISQera computing for provisioning tangible quantum advantages. Meanwhile, wireless communications networks have become more complex over time and the pressure to conquer communications complexity is intense for both researchers and system designers. Specifically, a major optimization problem in this context is the resource allocation in modern communications where typically appears as an intricate 0/1 knapsack (0/1-KP) problem and finding its optimal solution using classical computers is prohibitively difficult. Thus, a parallel QAOA framework for optimizing the 0/1- KP problems is proposed in this paper. The proposal has the space complexity of$\mathcal{O} (n)$and pseudopolynomial time complexity of$\mathcal{O} (nW)$, where$W$is the knapsack's total capacity and$n$is the total number of items. However, the proposed QAOA solution is highly parallel and can be implemented on$M$NISQ devices of n-qubits each to obtain$\mathcal{O}(n W / M)$time complexity and$\mathcal{O} (nM)$space complexity. Numerical experiments show high approximation ratios even for shallow depth QAOA instances.

The reliability of command and control (C2) operation of the UAV is one of the crucial aspects for the success of UAV applications beyond 5G wireless networks. In this paper, we focus on the short-packet communication to maximize the coverage range of reliable UAV control. We quantify the reliability performance of the C2 transmission from a multi-antenna ground control station (GCS), which also leverages maximal-ratio transmission beamforming, by deriving the closed-form expression for the average block error rate (BLER). To obtain additional insights, we also derive the asymptotic expression of the average BLER in the high-transmit power regime and subsequently analyze the possible UAV configuration space to find the optimum altitude. Based on the derived average BLER, we formulate a joint optimization problem to maximize the range up to which a UAV can be reliably controlled from a GCS. The solution to this problem leads to the optimal resource allocation parameters including blocklength and transmit power while exploiting the vertical degrees of freedom for UAV placement. Finally, we present numerical and simulation results to corroborate the analysis and to provide various useful design insights.

Before the advent of High-Throughput Satellites (HTSs), the satellite capacity was not enough to accommodate a large amount of data. Thanks to HTS, beyond 5G networks will boost the cooperation between space, air and terrestrial networks. The coexistence of heterogeneous QoS traffic demands, (e.g., emergency services, In-Flight Connectivity (IFC), Earth Observation Missions (EOMs)), over a dynamic environment, such as Multi-layer satellite-terrestrial networks, made the routing complex to handle. Additionally, due to numerous Inter-Satellite Links (ISLs) and their frequent changes, a testbed with real network emulation is challenging to develop.It is relevant not only to optimize the dynamic routing, but also to emulate the network in a testbed. This allows to consider systems constraints such as communication and technology delays in the most realistic manner. This paper investigates the future coexistence of the mentioned use cases for integrated Non-Terrestrial Networks (NTN)-terrestrial networks. We use Software Defined Networking (SDN) to monitor the substrate network with traffic statistics and apply routing decisions, via traffic handovers, during unexpected situations (congestion, link unavailability), in a reactive manner. Furthermore, we show that, for traditional handovers due to loss of Line of Sight (LoS), the SDN controller manages the procedure proactively to minimize traffic losses.

To allow flexible and cost-efficient network densification and deployment, the integrated access and backhaul (IAB) was recently standardized by the third generation partnership project (3GPP) as part of the fifth-generation new radio (5G-NR) networks. However, the current standardization only defines the IAB for the terrestrial domain, while non-terrestrial networks (NTNs) are yet to be considered for such standardization efforts. In this work, we motivate the use of IAB in NTNs, and we discuss the compatibility issues between the 3GPP specifications on IAB in 5G-NR and the satellite radio regulations. In addition, we identify the required adaptation from the 3GPP and/or satellite operators for realizing an NTN-enabled IAB operation. A case study is provided for a low earth orbit (LEO) satellite-enabled in-band IAB operation with orthogonal and non-orthogonal bandwidth allocation between access and backhauling, and under both time- and frequency-division duplex (TDD/FDD) transmission modes. Numerical results demonstrate the feasibility of IAB through satellites, and illustrate the superiority of FDD over TDD transmission. It is also shown that in the absence of precoding, non-orthogonal bandwidth allocation between the access and the backhaul can largely degrades the network throughput.

The space communications industry is challenged to develop a technology that can deliver broadband services to user terminals equipped with miniature antennas, such as handheld devices. One potential solution to establish links with ground users is the deployment of massive antennas in one single spacecraft. However, this is not cost-effective. Aligning with recent NewSpace activities directed toward miniaturization, mass production, and a significant reduction in spacecraft launch costs, an alternative could be distributed beamforming from multiple satellites. In this context, we propose a distributed beamforming modeling technique for wideband signals. We also consider the statistical behavior of the relative geometry of the swarm nodes. The paper assesses the proposed technique via computer simulations, providing interesting results on the beamforming gains in terms of power and the security of the communication against potential eavesdroppers at non-intended pointing angles. This approach paves the way for further exploration of wideband distributed beamforming from satellite swarms in several future communication applications.

This paper explores how the condition number of the channel matrix affects the performance of different precoding techniques in non-terrestrial network (NTN) communications. Precoding is a technique that can improve the signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER) in massive multi-beam systems. However, the performance of precoding depends on the rank and condition number of the channel matrix, which measures how well-conditioned the matrix is for inversion. We compare three precoding techniques: zero-forcing (ZF), minimum mean square error (MMSE), and semi-linear precoding (SLP), and show that their performance degrades as the condition number increases. To mitigate this problem, we propose a user ordering approach that forms optimally conditioned channel matrices by selecting users with orthogonal channel vectors. We demonstrate that this approach improves the SINR and goodput of all the precoding techniques in full-frequency reuse NTN communications.

Medium Earth orbit (MEO) constellation is an appealing solution between geostationary equatorial orbit (GEO) and lower Earth orbit (LEO) in terms of latency and number of satellites required. On-board processing of digital beam-former in MEO satellites is an efficient solution for achieving wider bandwidth, increased flexibility, and lower latency. Power constraints, however, make it impractical to digitally create thousands of beams at once. In this paper, area-power efficient digital beamformer architectures are proposed considering key metrics of a typical MEO scenario. The proposed efficient digital beamformer is comprised of a sparse-matrix-based user selection, a 2D discrete Fourier transform (DFT)-based digital beam generation, which is implemented by a fast Fourier transform (FFT) algorithm, and a spatial windowing module for selecting the antenna pattern. Furthermore, architectures of digital beam-former using conventional 2D-FFT approach, fully unrolled 2D-FFT, and an area-power efficient twiddle factor (TF) quantized fully unrolled 2D-FFT are proposed. The spatial windowing architecture concerning 10 × 10 radio frequency chains and sparse matrix architecture for user selection is also proposed. The proposed architectures are implemented targeting Virtex ultrascale FPGA and the area-power utilization is reported. It is noticed that more than 50%-reduction in area and power is achieved with the beamformer incorporating the proposed TF quantized fully unrolled 2D-FFT.

Recently, non-geostationary orbit (NGSO) satellite communication constellations have regained popularity due to their ability to provide global coverage and lower-latency connectivity. However, the new wave of Low Earth Orbit (LEO) satellite constellations operate on the same spectral bands as legacy satellites in geosynchronous orbit (GSO), which concurrently access the electromagnetic spectrum. Even if international regulations are in place, such increased spectral congestion will result in interference events. Therefore, both regulator entities and GSO operators have a high interest in detecting illegal or unlicensed NGSO interference sources. In this work, we simulate a realistic downlink interference scenario by emulating an actual commercial NGSO orbit whose signal is eventually received in a GSO receiver that is pointed toward a specific GSO satellite. We design an autoencoder deep neural network and we evaluate its performance considering both time-series and frequency-domain series of the overall received samples. Extensive numerical results are presented, validating the interference detection accuracy and comparing both domains of inputs at the autoencoder.

Semi-grant-free non-orthogonal multiple access (semi-GF NOMA) has emerged as a promising technology for the fifth-generation new radio (5G-NR) networks supporting the coexistence of a large number of random connections with various quality of service requirements. However, implementing a semi-GF NOMA mechanism in 5G-NR networks with heterogeneous services has raised several resource management problems relating to unpredictable interference caused by the GF access strategy. To cope with this challenge, the paper develops a novel hybrid optimization and multi-agent deep (HOMAD) reinforcement learning-based resource allocation design to maximize the energy efficiency (EE) of semi-GF NOMA 5G-NR systems. In this design, a multi-agent deep Q network (MADQN) approach is employed to conduct the subchannel assignment (SA) among users. While optimization-based methods are utilized to optimize the transmission power for every SA setting. In addition, a full MADQN scheme conducting both SA and power allocation is also considered for comparison purposes. Simulation results show that the HOMAD approach outperforms other benchmarks significantly in terms of the convergence time and average EE.

Satellite swarms have recently gained attention in the space industry due to their ability to provide extremely narrow beamwidths at a lower cost than single satellite systems. This paper proposes a concept for a satellite swarm using a distributed subarray configuration based on a 2D normal probability distribution. The swarm comprises multiple small satellites acting as subarrays of a big aperture array limited by a radius of 20000λ0working at a central frequency of 19 GHz. The main advantage of this approach is that the distributed subarrays can provide extremely directive beams and beamforming capabilities that are not possible using a conventional antenna and satellite design. The proposed swarm concept is analyzed, and the simulation results show that the radiation pattern achieves a beamwidth as narrow as 0.0015° with a maximum side lobe level of 18.8 dB and a grating lobe level of 14.8 dB. This concept can be used for high data rates applications or emergency systems.

This paper proposes a novel resource allocation scheme for optimizing the downlink of a user-centric cell-free massive multiple-input multiple-output (MIMO) system. The proposed approach aims to optimize the number of users served by each access point based on channel conditions while adapting to variable packet error rate and modulation and coding schemes. To enhance the received signal-to-noise plus interference ratio, the authors use a precoding design approach called the local protective partial zero-forcing that categorizes users based on their channel gain. The problem is formulated as a joint optimization of user assignment, resource allocation, and the precoding design. Closed-form expressions for the data rate are derived, and a new algorithm for resource allocation is introduced that outperforms several different scenarios while keeping the computational complexity reasonable. Compared to fixed parameter schemes, the proposed approach provides an optimal selection of the number of users for each access point and has the potential to significantly improve the system throughput, making it a novel and impactful solution for the practical implementation of user-centric cell-free massive MIMO systems.

In this paper, we tackle the problem of joint symbol level precoding (SLP) and reconfigurable intelligent surface (RIS) phase shift design with constellation rotation in the finite block length regime. We aim to increase energy efficiency by minimizing the total transmit power while satisfying the quality of service constraints. The total power consumption can be significantly minimized through the exploitation of multiuser interference by symbol level precoding and by the intelligent manipulation of the propagation environment using reconfigurable intelligent surfaces. In addition, the constellation rotation per user contributes to energy efficiency by aligning the symbol phases of the users, thus improving the utilization of constructive interference. The formulated power minimization problem is non-convex and correspondingly difficult to solve directly. Hence, we employ an alternating optimization algorithm to tackle the joint optimization of SLP and RIS phase shift design. The optimal phase of each user’s constellation rotation is obtained via an exhaustive search algorithm. Through Monte-Carlo simulation results, we demonstrate that the proposed solution yields substantial power minimization as compared to conventional SLP, zero forcing precoding with RIS as well as the benchmark schemes without RIS.

Reconfigurable Intelligent surfaces (RIS) have the potential to significantly improve the performance of future 6G LEO satellite networks. In particular, RIS can improve the signal quality of ground terminal, reduce power consumption of satellite and increase spectral efficiency of overall network. This paper proposes an energy-efficient RIS-enabled NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals at LEO satellite and passive beamforming at RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the problem of energy efficiency maximization is formulated as non-convex. In practice, it is very challenging to obtain the optimal solution for such problems. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate efficient power for ground terminals at satellite using Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. To validate the proposed solution, numerical results are also provided to demonstrate the benefits of the proposed optimization framework.

This work considers the orthogonal frequency division multiple access (OFDMA) technology that enables multiple unmanned aerial vehicles (multi-UAV) communication systems to provide on-demand services. The main aim of this work is to derive the optimal allocation of radio resources, 3D placement of UAVs, and user association matrices. To achieve the desired objectives, we decoupled the original joint optimization problem into two sub-problems: i) 3D placement and user association and ii) sum-rate maximization for optimal radio resource allocation, which are solved iteratively. The proposed iterative algorithm is shown via numerical results to achieve fast convergence speed after less than 10 iterations. The benefits of the proposed design are demonstrated via superior sum-rate performance compared to existing reference designs. Moreover, the results declared that the optimal power and sub-carrier allocation helped mitigate the co-cell interference that directly impacts the system’s performance.

On-board processing of digital beamforming in satellites is an efficient solution for the higher data rates, more capacity, and lower latency, but the available on-board limited power makes it impractical to digitally create thousands of beams at once. A significant portion of the analog hardware in a satellite communications payload can be replaced with highly integrated digital components, which are often more affordable, lighter, smaller, and reprogrammable by employing digital beamforming. In comparison to matrix-by-vector multiplication beamforming, the discrete Fourier transform (DFT) beamformer enables the finer realization of real-time beamformers with reduced circuit complexity and lower power consumption. Fast Fourier transform (FFT) methods can further reduce the computing cost of the DFT computation. Therefore, in this paper, area-power efficient two-dimensional (2D) FFT digital beamforming techniques are analyzed and implemented. The major implementation challenge is to produce N samples per cycle with lower area-power consumption. Fully unrolled 4-bit twiddle factor (TF) quantized FFT is proposed in this regard. The optimization techniques through quantization, truncation, and complex multipliers are thoroughly discussed for efficient implementation. The behavioral and post-route timing simulations are validated, and implementation results like area and power consumption are estimated and compared among conventional , fully unrolled, and the proposed 4-bit TF quantized 2D-FFT.

In this paper, we propose to remove the analog stage of hybrid beamforming (HYBF) in the millimeter wave (mmWave) full-duplex (FD) systems. Such a solution is highly desirable as the analog stage suffers from high insertion loss and high power consumption. Consequently, the mmWave FD nodes can operate with a fewer number of antennas, instead of relying on a massive number of antennas, and to tackle the propagation challenges of the mmWave band we propose to use near-field intelligent reflecting surfaces (NF-IRSs). The objective of the NF-IRSs is to simultaneously and smartly control the uplink (UL) and downlink (DL) channels while assisting in shaping the SI channel: this to obtain very strong passive SI cancellation. A novel joint active and passive beamforming design for the weighted sum-rate (WSR) maximization for the NF-IRSs-assisted mmWave point-to-point FD system is presented. Results show that the proposed solution fully reaps the benefits of the IRSs, only when they operate in the NF, which leads to considerably higher gains compared to the conventional massive MIMO (mMIMO) mmWave FD and half duplex (HD) systems.

Proliferating applications of unmanned aerial vehicles (UAVs) impose new service requirements, leading to several challenges. One of the crucial challenges in this vein is to facilitate the autonomous navigation of UAVs. Concretely, the UAV needs to individually process the visual data and subsequently plan its trajectories. Since the UAV has limited onboard storage constraints, its computational capabilities are often restricted and it may not be viable to process the data locally for trajectory planning. Alternatively, the UAV can send the visual inputs to the ground controller which, in turn, feeds back the command and control signals to the UAV for its safe navigation. However, this process may introduce some delays, which is not desirable for autonomous UAVs’ safe and reliable navigation. Thus, it is essential to devise techniques and approaches that can potentially offer low-latency solutions for planning the UAV’s flight. To this end, this paper analyzes a multi-access edge computing aided UAV and aims to minimize the latency of the task processing. More specifically, we propose an offloading strategy for a UAV by optimally designing the offloading parameter, local computational resources, and altitude of the UAV. The numerical and simulation results are presented to offer various design insights, and the benefits of the proposed strategy are also illustrated in contrast to the other baseline approaches.

Location-based services are witnessing a rise in popularity owing to their key features of delivering personalized digital experience. The recent developments in wireless sensing techniques make the realization of device-free localization (DFL) feasible within wireless sensor network (WSN) architectures. The DFL is an emerging technology that utilizes radio signal information for detecting and positioning a passive movable target without attached devices. However, determining the characteristics of the massive raw signals and extracting meaningful discriminative features relevant to the localization are highly intricate tasks due to the different patterns associated with different locations. To overcome these issues, deep learning (DL) techniques can be utilized here owing to their remarkable performance gains in similar practical problems. In this direction, we propose a DFL framework consists of multiple convolutional neural network (CNN) layers along with deep autoencoders based on the restricted Boltzmann machines (RBM) to construct a convolutional deep belief network (CDBN) for features recognition and extracting. Each CNN layer has stochastic pooling to sample down the feature map and reduced the dimensions of the required data without losing important information. This dimensionality reduction can alleviate the heavy computation while ensuring precise localization. The proposed framework is validated using real experimental dataset. The results show that the proposed model is able to achieve a high accuracy of 98% with reduced data dimensions and low signal-to-noise ratios (SNRs).

We consider a distributed quantization problem that arises when multiple edge devices, i.e., agents, are controlled via a centralized controller (CC). While agents have to communicate their observations to the CC for decision-making, the bit-budgeted communications of agent-CC links may limit the task-effectiveness of the system which is measured by the system's average sum of stage costs/rewards. As a result, each agent, given its local processing resources, should compress/quantize its observation such that the average sum of stage costs/rewards of the control task is minimally impacted. We address the problem of maximizing the average sum of stage rewards by proposing two different Action-Based State Aggregation (ABSA) algorithms that carry out the indirect and joint design of control and communication policies in the multi-agent system (MAS). While the applicability of ABSA-1 is limited to single-agent systems, it provides an analytical framework that acts as a stepping stone to the design of ABSA-2. ABSA-2 carries out the joint design of control and communication for an MAS. We evaluate the algorithms - with average return as the performance metric - using numerical experiments performed to solve a multi-agent geometric consensus problem.

The recent development of metasurfaces, which may enable several use cases by modifying the propagation environment, is anticipated to substantially affect the performance of sixth-generation (6G) wireless communications. Metasurface elements can produce passive subwavelength scattering to enable a smart radio environment. Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which refers to reconfigurable intelligent surfaces (RISs) that can transmit and reflect concurrently (STAR), is gaining popularity. In contrast to the widely studied RIS, which can only reflect the wireless signal and serve users on the same side as the transmitter, the STAR-RIS can reflect and refract (transmit), enabling 360° wireless coverage, thus serving users on both sides of the transmitter. This article presents a comprehensive review of the STAR-RIS, focusing on the most recent schemes for diverse use cases in 6G networks, resource allocation, and performance evaluation. We begin by laying the foundation for RIS (passive, active, and STAR-RIS), and then discuss the STAR-RIS protocols, advantages, and applications. In addition, we categorize the approaches within the domain of use scenarios, which include increasing coverage, enhancing physical-layer security (PLS), maximizing sum rate, improving energy efficiency (EE), and reducing interference. Next, we will discuss the various strategies for resource allocation and measures for performance evaluation. We aimed to elaborate, compare, and evaluate the literature regarding setup, channel characteristics, methodology, and objectives. In conclusion, we examine this field’s open research problems and potential future prospects.

Satellite-enabled Internet of Things (IoT) (SatIoT) has drawn increasing attentions due to the ubiquitous coverage, high capacity and massive connectivity. The inherent openness and broadcast nature of the SatIoT are vulnerable to security threats, particularly the jamming attacks for interrupting transmissions. Nonorthogonal multiple access (NOMA) scheme has the potential to be applied in anti-jamming communication for SatIoT due to the characteristic of resource sharing. The severely jammed users can get more allocated power by forming NOMA groups with other users, and both parties can improve the spectrum efficiency by frequency sharing. In this article, we aim to improve the performance of sum rate for SatIoT under the jamming environments. An anti-jamming transmission scheme is developed by jointly considering the NOMA-based user grouping and the power allocation (PA) for each NOMA group. Specifically, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as an anti-jamming coalition formation game, and the equilibrium solution is proved by the exact potential game theory. Moreover, in order to further improve NOMA performance, we derive the PA solution for multiuser NOMA by considering the imperfect successive interference cancellation. Finally, simulation results briefly highlight some details of the proposed approaches.

In this paper, we study joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the end-to-end (e2e) latency of computational tasks among multiple industrial Internet of Things (IIoT) devices by jointly optimizing offloading probabilities, processing rates, user association policies and power control subject to their service delay and energy consumption requirements as well as queueing stability conditions. The formulated JCCO problem belongs to a difficult class of mixed-integer non-convex optimization problem, making it computationally intractable. In addition, a strong coupling between binary and continuous variables and the large size of hierarchical edge-cloud systems make the problem even more challenging to solve optimally. To address these challenges, we first decompose the original problem into two subproblems based on the unique structure of the underlying problem and leverage the alternating optimization (AO) approach to solve them in an iterative fashion by developing newly convex approximate functions. To speed up optimal user association searching, we incorporate a penalty function into the objective function to resolve uncertainties of a binary nature. Two sub-optimal designs for given user association policies based on channel conditions and random user associations are also investigated to serve as state-of-the-art benchmarks. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the e2e latency and convergence speed.

Low earth orbit (LEO) satellite constellation-enabled communication networks are expected to be an important part of many Internet of Things (IoT) deployments due to their unique advantage of providing seamless global coverage. In this paper, we investigate the random access problem in massive multiple-input multiple-output-based LEO satellite systems, where the multi-satellite cooperative processing mechanism is considered. Specifically, at edge satellite nodes, we conceive a training sequence padded multi-carrier system to overcome the issue of imperfect synchronization, where the training sequence is utilized to detect the devices’ activity and estimate their channels. Considering the inherent sparsity of terrestrial-satellite links and the sporadic traffic feature of IoT terminals, we utilize the orthogonal approximate message passing-multiple measurement vector algorithm to estimate the delay coefficients and user terminal activity. To further utilize the structure of the receive array, a two-dimensional estimation of signal parameters via rotational invariance technique is performed for enhancing channel estimation. Finally, at the central server node, we propose a majority voting scheme to enhance activity detection by aggregating backhaul information from multiple satellites. Moreover, multi-satellite cooperative linear data detection and multi-satellite cooperative Bayesian dequantization data detection are proposed to cope with perfect and quantized backhaul, respectively. Simulation results verify the effectiveness of our proposed schemes in terms of channel estimation, activity detection, and data detection for quasi-synchronous random access in satellite systems.

A whole suite of innovative technologies and architectures have emerged in response to the rapid growth of wireless traffic. This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite for enhancing the network’s spectral efficiency.As for our analytical contributions, upon coherently combing the signals received by the central processing unit (CPU) from the users through the space and terrestrial links, we first mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. Our generic achievable throughput expression is applicable for arbitrary received signal detection techniques employed at the APs and the satellite under realistic imperfect channel estimates. A closed-form expression is then obtained for the ergodic UL data throughput, when maximum ratio combining is utilized for detecting the desired signals.As for our resource allocation contributions, we formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics for performing power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated relying on a closed-form expression. Our integrated hybrid network concept allows users to be served that may not otherwise be accommodated due to the excessive data demands. The algorithms proposed allow us to address the congestion issues appearing when at least one user is served at a rate below his/her target. The mathematical analysis is also illustrated with the aid of our numerical results that show the added benefits of considering the space links in terms of improving the ergodic data throughput. Furthermore, the proposed algorithms smoothly circumvent any potential congestion, especially in face of high rate requirements and weak channel conditions.

Automotive-Industry 5.0 will use beyond fifth-generation (B5G) technologies to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other devices. Recently, ambient backscatter communications (AmBC) have gained significant interest in the research community for providing battery-free communications. AmBC can modulate useful data and reflect it towards near devices using the energy and frequency of existing RF signals. However, obtaining channel state information (CSI) for AmBC systems would be very challenging due to no pilot sequences and limited power. As one of the latest members of multiple access technology, non-orthogonal multiple access (NOMA) has emerged as a promising solution for connecting large-scale devices over the same spectral resources in B5G wireless networks. Under imperfect CSI, this paper provides a new optimization framework for energy-efficient transmission in AmBC enhanced NOMA cooperative vehicle-to-everything (V2X) networks. We simultaneously minimize the total transmit power of the V2X network by optimizing the power allocation at BS and reflection coefficient at backscatter sensors while guaranteeing the individual quality of services. The problem of total power minimization is formulated as non-convex optimization and coupled on multiple variables, making it complex and challenging. Therefore, we first decouple the original problem into two sub-problems and convert the nonlinear rate constraints into linear constraints. Then, we adopt the iterative sub-gradient method to obtain an efficient solution. For comparison, we also present a conventional NOMA cooperative V2X network without AmBC. Simulation results show the benefits of our proposed AmBC enhanced NOMA cooperative V2X network in terms of total achievable energy efficiency.

Non-Geostationary Orbit satellite (NGSO) is an essential element in 5G Non-Terrestrial Networks (NTNs), which can operate either independently or as complementary parts to terrestrial systems to boost the network capacity, coverage and resilience. Due to the highly dynamic topologies, one of the challenges in NGSO is how to harmonize the network virtualized resources to satisfy diverse quality of service requirements in an efficient manner. In this paper, we investigate Virtual Network Embedding (VNE) for integrated NGSO-terrestrial systems while considering dynamic topologies. We propose a Mixed Binary Linear Programming (MBLP) formulation for a Dynamic Topology-Aware VNE (DTA-VNE) algorithm. Given priori information about the network’s evolution over time, DTA-VNE plans the embedding for each Virtual Network Request (VNR) over its lifetime. In a highly dynamic environment, the VNE decision can be varying for different VNRs at the expense of a considerable cost of migrating traffic and reconfiguring resources. DTA-VNE aims at minimizing this migration cost to avoid unnecessary re-mappings. To tackle the exponential complexity of the MBLP, we propose an efficient algorithm based on relaxation approaches (DTA-R) to solve large-scale problems. In numerical results, the effectiveness of the proposed DTA-R is demonstrated with much lower migration cost than the conventional implementations. The trade-off between the computation time and migration cost of DTA-R is studied. Finally, we test DTA-R and the baselines in our developed MultI-layer awaRe SDN-based testbed for SAtellite-Terrestrial networks (MIRSAT) to precisely quantify the packet lost for each migration. DTA-R proved to reduce the packet lost by ~2.5-5% compared to baselines.

Beam Hopping (BH) is a popular technique considered for next-generation multi-beam satellite communication system which allows a satellite focusing its resources on where they are needed by selectively illuminating beams. While beam illumination plan can be adjusted according to its needs, the main limitation of convectional BH is the adjacent beam avoidance requirement needed to maintain acceptable levels of interference. With the recent maturity of precoding technique, a natural way forward is to consider a dynamic beam illumination scheme with selective precoding, where large areas with high-demand can be covered by multiple active precoded beams. In this paper, we mathematically model such beam illumination design problem employing an interference-based penalty function whose goal is to avoid precoding whenever possible subject to beam demand satisfaction constraints. The problem can be written as a binary quadratic programming (BQP). Next, two convexification frameworks are considered namely: (i) A Semi-Definition Programming (SDP) approach particularly targeting BQP type of problems, and (ii) Multiplier Penalty and Majorization-Minimization (MPMM) based method which guarantees to converge to a local optimum. Finally, a greedy algorithm is proposed to alleviate complexity with minimal impact on the final performance. Supporting results based on numerical simulations show that the proposed schemes outperform the relevant benchmarks in terms of demand matching performance while minimizing the use of precoding.

Open radio access network (ORAN) Alliance offers a disaggregated RAN functionality built using open interface specifications between blocks. To efficiently support various competing services,namelyenhanced mobile broadband (eMBB) and ultra-reliable and low-latency (uRLLC), the ORAN Alliance has introduced a standard approach toward more virtualized, open, and intelligent networks. To realize the benefits of ORAN in optimizing resource utilization, this paper studies an intelligent traffic steering (TS) scheme within the proposed disaggregated ORAN architecture. For this purpose, we propose a joint intelligent traffic prediction, flow-split distribution, dynamic user association, and radio resource management (JIFDR) framework in the presence of unknown dynamic traffic demands. To adapt to dynamic environments on different time scales, we decompose the formulated optimization problem into two long-term and short-term subproblems, where the optimality of the latter is strongly dependent on the optimal dynamic traffic demand. We then apply a long-short-term memory (LSTM) model to effectively solve the long-term subproblem, aiming to predict dynamic traffic demands, RAN slicing, and flow-split decisions. The resulting non-convex short-term subproblem is converted to a more computationally tractable form by exploiting successive convex approximations. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms compared to several well-known benchmark schemes.

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

A cell-free (CF) massive multiple-input-multiple-output (mMIMO) system can provide uniform spectral efficiency (SE) with simple signal processing. On the other hand, a recently introduced technology called hybrid relay-reflecting intelligent surface (HR-RIS) can customize the physical propagation environment by simultaneously reflecting and amplifying radio waves in preferred directions. Thus, it is natural that incorporating HR-RIS into CF mMIMO can be a symbiotic convergence of these two technologies for future wireless communications. This motivates us to consider an HR-RIS-aided CF mMIMO system to utilize their combined benefits. We first model the uplink/downlink channels and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis of SE performance of the considered system. Specifically, we derive closed-form expressions for the uplink and downlink SE. The results reveal important observations on the performance gains achieved by HR-RISs compared to conventional systems. The presented analytical results are also valid for conventional CF mMIMO systems and those aided by passive reconfigurable intelligent surfaces. Such results play an important role in designing new transmission strategies and optimizing HR-RIS-aided CF mMIMO systems. Finally, we provide extensive numerical results to verify the analytical derivations and the effectiveness of the proposed system design under various settings.

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) for seamless global coverage. We propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA) paradigm with the emerging orthogonal time frequency space (OTFS) modulation to accommodate the massive IoT access, and mitigate the long round-trip latency and severe Doppler effect of terrestrial–satellite links (TSLs). On this basis, we put forward a two-stage successive active terminal identification (ATI) and channel estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. Specifically, at the first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to further refine the CE performance. Finally, a least square based parallel time domain SD method is developed to detect the OTFS signals with relatively low complexity. Simulation results demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of ATI, CE, and SD performance confronted with the long round-trip latency and severe Doppler effect.

The new era of space exploration demands a significant increase in the number of human and robotic missions, thus resulting in novel communication and service requirements. To satisfy such requirements, the fifth generation of mobile communication systems (5G), despite providing connectivity on Earth, has the potential to serve as a communication standard for space resource missions, particularly the ones targeting the Moon. In fact, 5G non-terrestrial networks (NTNs) are already in the standardization process and new techniques are being proposed in order to counteract the peculiarities of the non-terrestrial channel. However, going one step ahead and deploying constellations of satellites around the Earth or the Moon, requires first a detailed analysis and testing of the validity of the proposed techniques. Therefore, in this paper, we introduce the 5G Space Communications Lab, which has been developed with the purpose of simulating space-based 5G communications. The designed testbed proposed here increases the technology readiness level (TRL) of NTN-based 5G systems, demonstrating over a laboratory environment successful 5G communication via space links.

Federated Learning (FL), as an effective decentral-ized approach, has attracted considerable attention in privacy-preserving applications for wireless edge networks. In practice, edge devices are typically limited by energy, memory, and computation capabilities. In addition, the communications be-tween the central server and edge devices are with constrained resources, e.g., power or bandwidth. In this paper, we propose a joint sparsification and optimization scheme to reduce the energy consumption in local training and data transmission. On the one hand, we introduce sparsification, leading to a large number of zero weights in sparse neural networks, to alleviate devices' computational burden and mitigate the data volume to be uploaded. To handle the non-smoothness incurred by sparsification, we develop an enhanced stochastic gradient descent algorithm to improve the learning performance. On the other hand, we optimize power, bandwidth, and learning parameters to avoid communication congestion and enable an energy-efficient transmission between the central server and edge devices. By collaboratively deploying the above two components, the numerical results show that the overall energy consumption in FL can be significantly reduced, compared to benchmark FL with fully-connected neural networks.

This paper proposes a distributed learning framework for network slicing in multi-cell open radio access networks providing two services: Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). In particular, a resource allocation optimization problem is formulated with an objective to maximize the average eMBB data rate while considering URLLC constraints and the data rate variance among eMBB users. A multi-agent Deep Reinforcement Learning (DRL) based algorithm is developed to solve the formulated problem, where network components collaboratively train a global machine learning model and then share learning parameters for distributed executions at network edges. Specifically, DRL agents are installed at Near-Real-Time Radio access network Intelligent Controllers (Near-RT RICs) located in the network edge servers to provide online resource allocation decisions while the training process is performed offline at the Non-Real-Time RIC (Non-RT RIC) located in a regional cloud server. The achieved simulation results show that the proposed algorithm can ensure the required URLLC reliability while keeping the Quality-of-Service (QoS) requirements of the eMBB service.

This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite. By coherently combing the signals received by the central processing unit from the users through the space and terrestrial links, we mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. A closed-form expression is obtained when maximum ratio combining is employed to detect the desired signals. We formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics to perform power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated based on a closed-form expression. The mathematical analysis is validated with numerical results showing the added benefits of considering a satellite link in terms of improving the ergodic data throughput.

This paper aims to determine linear precoding (LP) vectors, beam hopping (BH), and discrete DVB-S2X transmission rates jointly for the GEO satellite communication systems to minimize the payload power consumption and satisfy ground users' demands within a time window. Regarding constraint on the maximum number of illuminated beams per time slot, the technical requirement is formulated as a sparse optimization problem in which the hardware-related beam illumination energy is modeled in a sparsity form of the LP vectors. To cope with this problem, the compressed sensing method is employed to transform the sparsity parts into the quadratic form of pre-coders. Then, an iterative window-based algorithm is developed to update the LP vectors sequentially to an efficient solution. Additionally, two other two-phase frameworks are also proposed for comparison purposes. In the first phase, these methods aim to determine the MODCOD transmission schemes for users to meet their demands by using a heuristic approach or DNN tool. In the second phase, the LP vectors of each time slot will be optimized separately based on the determined MODCOD schemes.

Low Earth orbit (LEO) satellite communication has drawn particular attention recently due to its high data rate services and low round-trip latency. It is low-cost to launch and can provide global coverage. However, the spectrum scarcity might be one of the critical challenges in the growth of LEO satellites, impacting severe restrictions on the development of ground-space integrated networks. To address this issue, we propose rate splitting multiple access (RSMA) for cognitive radio (CR) enabled nongeostationary orbit (GEO)-LEO coexisting satellite network. In particular, this work aims to maximize the system's sum rate by simultaneously optimizing the power allocation and sub carrier beam assignment of LEO satellite communication while restricting the interference temperature to GEO satellite users. The problem of sum rate maximization is formulated as non-convex and a Global optimal solution is challenging to obtain. Therefore, we first employ the successive convex approximation technique to reduce the complexity and make the problem more tractable. Then for the power allocation, we exploit Karush-Kuhn-Tucker (KKT) condition and adopt an efficient algorithm based on the greedy approach for subcarrier beam assignment. We also propose two suboptimal schemes with fixed power allocation and random sub carrier beam assignment as the benchmark. Results demonstrate the benefits of the proposed scheme compared to the benchmark schemes.

We consider a novel hybrid active-passive reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) air-ground communications system. Unlike the conventional passive RIS, the hybrid RIS is equipped with a few active elements to not only reflect but also amplify the incident signals for significant performance improvement. Towards a fairness design, our goal is to maximize the minimum rate among users through jointly optimizing the location and power allocation of the UAV and the RIS reflecting/amplifying coefficients. The formulated optimization problem is nonconvex and challenging, which is efficiently solved via block coordinate descend and successive convex approximation. Our numerical results show that a hybrid RIS requires only 4 active elements and a power budget of 0 dBm to achieve an improvement of 52.08% in the minimum rate, while that achieved by a conventional passive RIS with the same total number of elements is only 18.06%.

In this work, we propose a time- and a unit cell-splitting protocol for supplying the energy needs of reconfigurable intelligent surfaces (RISs) through wireless energy harvesting (EH) from information signals. We first compute the RIS energy consumption per frame that is common for both protocols and incorporates the energy burden for channel estimation. Based on it, we subsequently formulate an optimization problem that maximizes the average rate under the constraint of meeting the RIS long-term energy consumption demands. In addition, closed-form solutions regarding the optimal allocation of resources are provided for both protocols in the case of deterministic channel gains for the transmitter-RIS links and a methodology to obtain such a solution in the general case of random channels. Finally, for the optimal resource allocation for both protocols numerical results based on Monte-Carlo simulations reveal that the unit cell-splitting protocol exhibits a superior performance compared to its time-splitting counterpart.

This paper studies the non-orthogonal multicast and unicast coordinated beamforming design for integrated terrestrial and satellite networks (ITSN), when the channel state information at the transmitter (CSIT) is imperfect. In order to mitigate the interference induced by simultaneous multicast and unicast links along with the spectrum coexisting mechanism for integrated terrestrial and satellite transmissions, we consider a two-layer layered division multiplexing (LDM) structure where the mul-ticast and unicast services are provided in different layers. We formulate a coordinated beamforming problem with the objective to minimize the transmit power under individual quality of service (QoS) constraints. With regard to the unknown convexity of the transmit power minimization problem, we transform the original infeasible optimization into a deterministic optimization form with linear matrix inequality (LMI) by utilizing S-procedure and semi-definite relaxation (SDR) methods. Then, we introduce a penalty function and propose an iterative algorithm with guaranteed convergence to obtain optimal solutions. Simulation results demonstrate the superiority of the proposed coordinated beamforming scheme, especially for the case of imperfect CSIT, while our LDM based coordinated beamforming scheme signifi-cantly outperforms the conventional ones in terms of sum rate.

The low earth orbit (LEO) satellite system is one of the promising solutions to provide broadband services to a wide-coverage area for future integrated LEO-6G networks, where users' demands vary with time and geographical locations. Conventional satellites with fixed beam pattern and footprint planning may not be capable of meeting such dynamic requests and irregular traffic distributions. As the development of flexible satellite payload with beamforming capabilities, spot beams with flexible size and shape are considered potential solutions to this issue. As an early investigation, in this paper, we consider the scenarios where satellite payloads are equipped with multiple beam patterns and study the optimal beam pattern selection. We exploit the potential synergies of joint resource optimization between adaptive beam patterns and non-orthogonal multiple access (NOMA) in a LEO satellite system, where NOMA is employed to reduce intra-beam interference and flexible beam pattern is adopted to mitigate inter-satellite interference. The formulated problem is to minimize the capacity-demand gap of terminals, which falls into mixed-integer nonconvex pro-gramming (MINCP). To tackle the discrete variables and non-convexity, we design a joint approach to allocate power and select beam patterns. Numerical results show that the proposed scheme achieves capacity-demand gap reduction of 37.8% over conventional orthogonal multiple access (OMA) and 42.5% over the fixed-beam-pattern scheme.

Reconfigurable intelligent surfaces (RISs) have recently gained significant interest as an emerging technology for future wireless networks. This paper studies an RIS-assisted propagation environment, where a single-antenna source transmits data to a single-antenna destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, closed-form expressions for the coverage probability and ergodic rate are derived. We use numerical simulations to validate the obtained analytical framework. Also, we show that the considered optimal phase shift designs outperform several heuristic benchmarks.

The key advantage of successive relaying (SR) networks is their ability to mimic the full-duplex (FD) operation with half-duplex (HD) relays. However, the main challenge that comes with such schemes is the associated inter-relay interference (IRI). In this work, we propose a reconfigurable intelligent surface (RIS)-enhanced SR network, where one RIS is deployed near each of the two relay nodes to provide spatial suppression of IRI, and to maximize the gain of desired signals. The resultant max-min optimization problem with joint phase-shift design for both RISs is first tackled via the semidefinite programming (SDP) approach. Then, a lower-complexity solution suitable for real-time implementation is proposed based on particle swarm optimization (PSO). Numerical results demonstrate that even relatively small RISs can provide significant gains in achievable rates of SR networks, and the proposed PSO scheme can achieve a near optimal performance.

In this work, we investigate the decode-and-forward (DF) relay-aided double reconfigurable intelligent surface (RIS)-assisted networks, where the signal is subject to reflections from two RISs before reaching the destination. Different relay-aided network architectures are considered for maximum achievable rate under a total power constraint. Phase optimization for the double-RIS channels is tackled via the alternating optimization and majorization-minimization (MM) schemes. Moreover, closed-form solutions are obtained for each case. Numerical results indicate that the deployment of two relays, one near each RIS, achieves higher rates at low and medium signal-to-noise ratios (SNRs) compared to placing a single relay between the two RISs; while at high SNRs, the latter approach achieves higher rates only if the inter-relay interference for the former case is considerably high.

We consider in this work a cell-free (CF) massive multiple-input-multiple-output (mMIMO) system where multiple hybrid relay-reflecting intelligent surfaces (HR-RIS) are deployed to assist communication between access points and users. We first present the signal model and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis for the considered HR-RIS-aided CF mMIMO system, where the closed-form expression of the downlink throughput is derived. The presented analytical results are also valid for conventional CF mMIMO systems, i.e., CF mMIMO systems with and without passive reconfigurable intelligent surfaces. Finally, the analytical derivations are verified by extensive numerical results.

The next generation multibeam satellites open up a new way to design satellite communication channels with the full flexibility in bandwidth, transmit power and beam coverage management. In this paper, we exploit the flexible multibeam satellite capabilities and the geographical distribution of users to improve the performance of satellite-assisted edge caching systems. Our aim is to jointly optimize the bandwidth allocation in multibeam and caching decisions at the edge nodes to address two important problems: i) cache feeding time minimization and ii) cache hits maximization. To tackle the non-convexity of the joint optimization problem, we transform the original problem into a difference-of-convex (DC) form, which is then solved by the proposed iterative algorithm whose convergence to at least a local optimum is theoretically guaranteed. Furthermore, the effectiveness of the proposed design is evaluated under the realistic beams coverage of the satellite SES-14 and Movielens data set. Numerical results show that our proposed joint design can reduce the caching feeding time by 50% and increase the cache hit ratio (CHR) by 10% to 20% compared to existing solutions. Furthermore, we examine the impact of multispot beams and multicarrier wide-beam on the joint design and discuss potential research directions.

Towards the next generation networks, low earth orbit (LEO) satellites have been considered as a promising component for beyond 5G networks. In this paper, we study downlink LEO-5G communication systems in a practical scenario, where the integrated LEO-terrestrial system is over-loaded by serving a number of terminals with high-volume traffic requests. Our goal is to optimize resource scheduling such that the amount of undelivered data and the number of unserved terminals can be minimized. Due to the inherent hardness of the formulated quadratic integer programming problem, the optimal algorithm requires unaffordable complexity. To solve the problem, we propose a near-optimal algorithm based on alternating direction method of multipliers (ADMM-HEU), which saves computational time by taking advantage of the distributed ADMM structure, and a low-complexity heuristic algorithm (LC-HEU), which is based on estimation and greedy methods. The results demonstrate the near-optimality of ADMM-HEU and the computational efficiency of LC-HEU compared to the benchmarks.

The commercial low earth orbiting (LEO) satellite constellations have shown unprecedented growth. Accordingly, the risk of generating harmful interference to the geostationary orbit (GSO) satellite services increases with the number of satellites in such mega-constellations. As the GSO arc encompasses the primary and existing satellite assets providing essential fixed and broadcasting satellite services, the interference avoidance for this area is of the utmost importance. In particular, non-geostationary orbit (NGSO) operators should comply with the regulations set up both by their national regulators and by the International Telecommunications Union (ITU) to minimize the impact of emissions on existing GSO and non-GSO systems. In this paper, we first provide an overview of the most recent radio regulations that dictate the NGSO-GSO spectral co-existence. Next, we analyze the NGSO-GSO radio frequency interference for the downlink scenario, following the so-called time-simulation methodology introduced by ITU. The probability distribution of aggregated power flux-density for NGSO co-channel interference is evaluated and assessed, adopting different degrees of exclusion angle strategy for interference avoidance. We conclude the paper by discussing the resulting implications for the continuity of operation and service provision and we provide remarks for future work.

Through precoding, the spectral efficiency of the system can be improved; thus, more users can benefit from 5G and beyond broadband services. However, complete precoding (using all precoding coefficients) may not be possible in practice due to the high signal processing complexity involved in calculating a large number of precoding coefficients and combining them with symbols for transmission. In this paper, we propose an energy-efficient sparse precoding design, where only a few precoding coefficients are used with lower transmit power consumption depending on the demand. In this context, we formulate an optimization problem that minimizes the number of in-use precoding coefficients and the system power consumption while matching the per beam demand. This problem is non-convex. Hence, we apply Lagrangian relaxation and successive convex approximation to convexify it. The proposed solution outperforms the benchmark schemes in energy efficiency and demand satisfaction with the additional advantage of sparse precoding design.

Satellite communications industry is currently going through a rapid and profound transformation to adapt to the recent innovations and developments in the realm of non-geostationary orbit (NGSO) satellites. The growing popularity of NGSO systems, with cheap manufacturing and launching costs, has set to revolutionize the internet market. In this context, accurate channel characterization is crucial for the performance optimization and designing efficient NGSO communications, especially considering the dynamic propagation environment. While the Third Generation Partnership Project (3GPP) has provided some guidelines in Release 15, we observed certain divergence on the channel models considered in the literature, each with different assumptions and peculiarities. This paper provides an extensive review of the existing methods proposed for NGSO channel modeling that consider different orbits, frequency bands, user equipment, use-case and scenario peculiarities. The provided review discusses the channel modeling efforts from a contemporary perspective through trade-off analyses, classifications, and highlighting their advantages and pitfalls. The main goal is to provide a comprehensive overview of NGSO channel models to facilitate the selection of the most appropriate channel based on the scenario requirements to be evaluated and/or analysed.

To ensure quality of service to the users within the coverage area, time-flexible satellite system needs to design a beam illumination strategy, i.e. a time-space transmission pattern that is periodically repeated. The beam activation dwells just long enough to satisfy the traffic demand. The beam illumination pattern design is typically a combinatorial problem with a non-convex structure due to the presence of inter-beam interference. The computational complexity of existing solutions addressing this problem are unbearable for practical systems. In this paper, we propose a low-complexity beam illumination design which splits the task into two sequential sub-problems: (i) Estimation of number of time-slots to be allocated to each geographical area in order to satisfy its demand; (ii) Assignment of illumination slots over the time domain. Note that the outcome of step (ii) determines the resulting interference environment and, as a consequence, the resulting offered capacity. The latter is, at the same time, an input needed for step (i). For this reason, we propose an adaptive system where the two steps are iteratively executed until convergence. Furthermore, we show that a random assignment for step (ii) significantly reduces the complexity without a major impact on the performance. The proposed design is validated and compared with existing schemes using numerical results.

Reconfigurable intelligent surface (RIS), non-orthogonal multiple access (NOMA), and underwater optical wireless communication (UOWC) are paradigms of technologies that drive the development of next generation communication systems. In this paper, we investigate the performance of a NOMA-based RIS-assisted hybrid radio frequency (RF)-UOWC system. The ship works as a relay that redirects the received signal to two underwater destinations simultaneously. Due to the interruption of the direct link between the base station and the ship floating on the surface of the water, communication will be carried out via an RIS fixed to an intermediate building. In this paper, we provide new analytical expressions for the outage probability (OP), asymptotic analyses of the OP, and diversity order (D) to gain insights into the system performance. The results showed that the diversity order depends on the UOWC receiver detection technique. In the end, we illustrated that the NOMA-based RIS-assisted system significantly improves the outage performance of hybrid RF-UWOC systems over a benchmark system.

Efficiently utilizing the network resources to minimize the operation costs while satisfying customer’s Quality-of-Experience (QoE) related requirement as well as dynamic demands is a challenging task of all network operators. This paper aims to develop a stationary capacity allocation method that anticipates time-varying demand and keeps the network operating under constraints on a stochastic blocking probability. Queuing delay requirement is also regarded as an QoE-oriented practical design. Employing an approximation of time-varying queuing model and continuous time Markov chain (CTMC) for queue length, the technical designs are stated as a convex stochastic optimization based on which a dynamic capacity allocation is proposed by using Lagrangian and gradient descent searching method. Numerical studies confirm that our proposed framework can efficiently and dynamically allocate optimal capacity for a blocking probability of less than 1% and the probability of violating the queuing-delay requirement is less than 5%.

Backscatter communications (BC) has emerged as a promising technology for providing low-powered transmissions in nextG (i.e., beyond 5G) wireless networks. The fundamental idea of BC is the possibility of communications among wireless devices by using the existing ambient radio frequency signals. Non-orthogonal multiple access (NOMA) has recently attracted significant attention due to its high spectral efficiency and massive connectivity. This paper proposes a new optimization framework to minimize total transmit power of BC-NOMA cooperative vehicle-to-everything networks (V2XneT) while ensuring the quality of services. More specifically, the base station (BS) transmits a superimposed signal to its associated roadside units (RSUs) in the first time slot. Then the RSUs transmit the superimposed signal to their serving vehicles in the second time slot exploiting decode and forward protocol. A backscatter device (BD) in the coverage area of RSU also receives the superimposed signal and reflect it towards vehicles by modulating own information. Thus, the objective is to simultaneously optimize the transmit power of BS and RSUs along with reflection coefficient of BDs under perfect and imperfect channel state information. The problem of energy efficiency is formulated as non-convex and coupled on multiple optimization variables which makes it very complex and hard to solve. Therefore, we first transform and decouple the original problem into two sub-problems and then employ iterative sub-gradient method to obtain an efficient solution. Simulation results demonstrate that the proposed BC-NOMA V2XneT provides high energy efficiency than the conventional NOMA V2XneT without BC.

Reflecting intelligent surfaces (RIS) is a low-cost and energy-efficient solution to achieve high spectral efficiency in sixth-generation (6G) networks. The basic idea of RIS is to smartly reconfigure the signal propagation by using passive reflecting elements. On the other side, the demand of high throughput geostationary (GEO) satellite communications (SatCom) is rapidly growing to deliver broadband services in inaccessible/insufficient covered areas of terrestrial networks. This paper proposes a GEO SatCom network, where a satellite transmits the signal to a ground mobile terminal using multicarrier communications. To enhance the effective gain, the signal delivery from satellite to the ground mobile terminal is also assisted by RIS which smartly shift the phase of the signal towards ground terminal. We consider that RIS is mounted on a high building and equipped With multiple re-configurable passive elements along with smart controller. We jointly optimize the power allocation and phase shift design to maximize the channel capacity of the system. The joint optimization problem is formulated as nonconvex due to coupled variables which is hard to solve through traditional convex optimization methods. Thus, we propose a new $\epsilon-$ optimal algorithm which is based on Mesh Adaptive Direct Search to obtain an efficient solution. Simulation results unveil the benefits of RIS-assisted SatCom in terms of system channel capacity.

Very High Throughput Satellite (VHTS) systems are characterized by a multi-beam footprint covering wide areas and providing service to large numbers of users. Multicasting comes naturally to exploit the multiuser diversity in VHTS systems, where the data of different users are multiplexed in a single PHY frame. Following the DVB-S2(X) standard, the resulting PHY frame is encoded using a single codeword. The latter brings some practical implementation challenges when precoding is considered, as the precoder can no longer be designed on a user-by-user basis. Avoiding overambitious and impractical precoding designs, our work focuses on the low-complexity MMSE-based precoding, which has been considered as the baseline for early satellite over-the-air precoding tests. While the multicast scheduling has been widely investigated in the literature, we will show in this work that its performance is significantly impacted by the equivalent multicast channel calculation. Therefore, in this paper, we analyze and report the impact of the user scheduling (i.e., selection of users to be multiplexed together in a single PHY frame) as well as the methodology employed for the equivalent multicast channel considered for the precoding computation.

In this paper, we examine the potential for a reconfigurable intelligent surface (RIS) to be powered by energy harvested from information signals. This feature might be key to reap the benefits of RIS technology’s lower power consumption compared to active relays. We first identify the main RIS power-consuming components and then propose an energy harvesting and power consumption model. Furthermore, we formulate and solve the problem of the optimal RIS placement together with the amplitude and phase response adjustment of its elements in order to maximize the signal-to-noise ratio (SNR) while harvesting sufficient energy for its operation. Finally, numerical results validate the autonomous operation potential and reveal the range of power consumption values that enables it.

Satellite communication systems can provide seamless wireless coverage directly or through complementary ground-terrestrial components and are projected to be incorporated into future wireless networks, particularly 5G and beyond networks. Increased capacity and flexibility in telecom satellite payloads based on classic radio frequency technology have traditionally translated into increased power consumption and dissipation. Much of the analog hardware in a satellite communications payload can be replaced with highly integrated digital components that are often smaller, lighter, and less expensive, as well as software reprogrammable. Digital beamforming of thousands of beams simultaneously is not practical due to the limited power available onboard satellite processors. Reduced digital beamforming power consumption would enable the deployment of a full digital payload, resulting in comprehensive user applications. Beamforming can be implemented using matrix multiplication, hybrid methodology, or a discrete Fourier transform (DFT). Implementing DFT via fast Fourier transform (FFT) reduces the power consumption, process time, hardware requirements, and chip area. Therefore, in this paper, area-power efficient FFT architectures for digital beamforming are analyzed. The area in terms of look up tables (LUTs) is estimated and compared among conventional FFT, fully unrolled FFT, and a 4-bit quantized twiddle factor (TF)FFT. Further, for the typical satellite scenarios, area, and power estimation are reported.

Ambient backscatter communication (AmBC) technology can potentially offer spectral- and energy-efficient solutions for future wireless systems. This paper proposes a novel design to facilitate the spectrum sharing between a secondary system and a primary system based on the AmBC technique in intelligent reflective surface (IRS)-assisted unmanned aerial vehicle (UAV) networks. In particular, an IRS-aided UAV cooperatively relays the transmission from a terrestrial primary source node to a user equipment on the ground. On the other hand, leveraging on the AmBC technology, a terrestrial secondary node transmits its information to a terrestrial secondary receiver by modulating and backscattering the ambient relayed radio frequency (RF) signals from the UAV-IRS. The performance of such a system setup is analyzed by deriving the expressions of outage probability and ergodic spectral efficiency. Finally, we present the numerical results to provide useful insights into the system design and also validate the derived theoretical results using Monte Carlo simulations.

Reducing waiting time due to scheduling process and exploiting multi-access transmission, grant-free non-orthogonal multiple access (GF-NOMA) has been considered as a promising access technology for URLLC-enabled 5G system with strict requirements on reliability and latency. However, GF-NOMA-based systems can suffer from severe interference caused by the grant-free (GF) access manner which may degrade the system performance and violate the URLLC-related requirements. To overcome this issue, the paper proposes a novel reinforcement-learning (RL)-based random access (RA) protocol based on which each device can learn from the previous decision and its corresponding performance to select the best subchannels and transmit power level for data transmission to avoid strong cross-interference. The learning-based framework is developed to maximize the system access efficiency which is defined as the ratio between the number of successful transmissions and the number of subchannels. Simulation results show that our proposed framework can improve the system access efficiency significantly in overloaded scenarios.

Reconfigurable Intelligent Surfaces (RIS) are planar structures connected to electronic circuitry, which can be employed to steer the electromagnetic signals in a controlled manner. Through this, the signal quality and the effective data rate can be substantially improved. While the benefits of RIS-assisted wireless communications have been investigated for various scenarios, some aspects of the network design, such as coverage, optimal placement of RIS, etc., often require complex optimization and numerical simulations, since the achievable effective rate is difficult to predict. This problem becomes even more difficult in the presence of phase estimation errors or location uncertainty, which can lead to substantial performance degradation if neglected. Considering randomly distributed receivers within a ring-shaped RIS-assisted wireless network, this paper mainly investigates the effective rate by taking into account the above-mentioned impairments. Furthermore, exact closed-form expressions for the effective rate are derived in terms of Meijer’s G-function, which (i) reveals that the location and phase estimation uncertainty should be well considered in the deployment of RIS in wireless networks; and (ii) facilitates future network design and performance prediction.

In this paper, we address the virtual network embedding (VNE) problem in non-terrestrial networks (NTNs) enabling dynamic changes in the virtual network function (VNF) deployment to maximize the service acceptance rate and service revenue. NTNs such as satellite networks involve highly dynamic topology and limited resources in terms of rate and power. VNE in NTNs is a challenge because a static strategy under-performs when new service requests arrive or the network topology changes unexpectedly due to failures or other events. Existing solutions do not consider the power constraint of satellites and rate limitation of inter-satellite links (ISLs) which are essential parameters for dynamic adjustment of existing VNE strategy in NTNs. In this work, we propose a dynamic VNE algorithm that selects a suitable VNE strategy for new and existing services considering the time-varying network topology. The proposed scheme, D-ViNE, increases the service acceptance ratio by 8.51% compared to the benchmark scheme TS-MAPSCH.

This article presents a closed-loop differential phase compensation system for a precoding-enabled multibeam satellite forward link and its validation by live experiments on a GEO satellite scenario. The precoding operation avoids inter-beam interference and maximizes the spectrum efficiency by full frequency reuse as an alternative to the traditional two-color or four-color reuse methods proposed in the DVB-S2 standard. However, the satellite payload introduces differential phase and frequency impairments, which can degrade the precoding performance. This work describes the implementation of the differential phase and frequency tracking and compensation loop in an end-to-end testbed over a multibeam satellite system with independent local oscillators. The developed system performs end-to-end real-time communication over the satellite link, including channel measurements and precompensation. Results are validated by an over-the-air demonstration using two beams of the SES-14 multibeam satellite. Each beam is transmitted by independent transponders, which results in differential frequency and phase offsets due to the transponder undisciplined local oscillators. This phase offset makes it impossible to use precoding without the phase compensation loop. We prove that the implemented system can successfully track and compensate the differential phase and frequency to improve precoding performance.

Among a plethora of new wireless communication technologies, reconfigurable intelligent surface (RIS) emerges as one of the revolutionary solutions to provide energy- and cost-efficient signal transmissions. RIS is capable of reflecting electromagnetic signals in a controlled manner. In this paper, we jointly design the active beamforming at the base station and passive beamforming at the RIS to maximize the number of served users in a practical Smart City street scenario, subject to quality of service (QoS) and power constraints. The formulated problem belongs to the difficult class of mixed-integer non-convex programming, which is NP-hard. To arrive at a low-complexity solution, we first decompose the original problem into two subproblems and then propose an alternating optimization algorithm based on successive convex approximation (SCA) to solve them in an iterative manner. Simulation results are provided to verify the performance improvement of the proposed algorithm as compared to baseline schemes.

The satellite component is recognized as a promising solution to complement and extend the coverage of future Internet of Things (IoT) terrestrial networks (TNs). In this context, a study item to integrate satellites into narrowband-IoT (NB-IoT) systems has been approved within the 3rd Generation Partnership Project (3GPP) standardization body. However, as NB-IoT systems were initially conceived for TNs, their basic design principles and operation might require some key modifications when incorporating the satellite component. These changes in NB-IoT systems, therefore, need to be carefully implemented in order to guarantee a seamless integration of both TN and nonterrestrial network (NTN) for a global coverage. This article addresses this adaptation for the random access (RA) step in NB-IoT systems, which is in fact the most challenging aspect in the NTN context, for it deals with multiuser time-frequency synchronization and timing advance for data scheduling. In particular, we propose an RA technique which is robust to typical satellite channel impairments, including long delays, significant Doppler effects, and wide beams, without requiring any modification to the current NB-IoT RA waveform. Performance evaluations demonstrate the proposal’s capability of addressing different NTN configurations recently defined by 3GPP for the 5G new radio system.

In this article, we focus on the use of low-Earth orbit (LEO) satellites providing the narrowband Internet of Things (NB-IoT) connectivity to the on-ground user equipments (UEs). Conventional resource allocation algorithms for the NB-IoT systems are particularly designed for terrestrial infrastructures, where devices are under the coverage of a specific base station (BS) and the whole system varies very slowly in time. The existing methods in the literature cannot be applied over LEO satellite-based NB-IoT systems for several reasons. First, with the movement of the LEO satellite, the corresponding channel parameters for each user will quickly change over time. Delaying the scheduling of a certain user would result in a resource allocation based on outdated parameters. Second, the differential Doppler shift, which is a typical impairment in communications over LEO, directly depends on the relative distance among users. Scheduling at the same radio frame users that overcome a certain distance would violate the differential Doppler limit supported by the NB-IoT standard. Third, the propagation delay over an LEO satellite channel is around 4–16 times higher compared to a terrestrial system, imposing the need for message exchange minimization between the users and the BS. In this work, we propose a novel uplink resource allocation strategy that jointly incorporates the new design considerations previously mentioned together with the distinct channel conditions, satellite coverage times, and data demands of various users on Earth. The novel methodology proposed in this article can act as a framework for future works in the field.

Radio-frequency (RF) energy harvesting (EH) in wireless relaying networks has attracted considerable recent interest, especially for supplying energy to relay nodes in the Internet of Things (IoT) systems to assist the information exchange between a source and a destination. Moreover, limited hardware, computational resources, and energy availability of IoT devices have raised various security challenges. To this end, physical-layer security (PLS) has been proposed as an effective alternative to cryptographic methods for providing information security. In this study, we propose a PLS approach for simultaneous wireless information and power transfer (SWIPT)-based half-duplex (HD) amplify-and-forward (AF) relaying systems in the presence of an eavesdropper. Furthermore, we take into account both static power splitting relaying (SPSR) and dynamic power splitting relaying (DPSR) to thoroughly investigate the benefits of each one. To further enhance secure communication, we consider multiple friendly jammers to help prevent wiretapping attacks from the eavesdropper. More specifically, we provide a reliability and security analysis by deriving closed-form expressions of outage probability (OP) and intercept probability (IP), respectively, for both the SPSR and DPSR schemes. Then, simulations are also performed to validate our analysis and the effectiveness of the proposed schemes. Specifically, numerical results illustrate the nontrivial tradeoff between reliability and security of the proposed system. In addition, we conclude from the simulation results that the proposed DPSR scheme outperforms the SPSR-based scheme in terms of OP and IP under the influences of different parameters on system performance.

Simultaneous wireless information and power transfer (SWIPT) and full-duplex (FD) communications have emerged as prominent technologies in overcoming the limited energy resources in Internet of Things (IoT) networks and improving their spectral efficiency (SE). This article investigates the outage and throughput performance for a decode-and-forward (DF) relay SWIPT system, which consists of one source, multiple relays, and one destination. The relay nodes in this system can harvest energy from the source’s signal and operate in the FD mode. A suboptimal, low-complexity, yet efficient relay selection scheme is also proposed. Specifically, a single relay is selected to convey information from a source to a destination so that it achieves the best channel from the source to the relays. An analysis of outage probability (OP) and throughput performed on two relaying strategies, termed static power splitting-based relaying (SPSR) and optimal dynamic power splitting-based relaying (ODPSR), is presented. Notably, we considered independent and nonidentically distributed (i.n.i.d.) Rayleigh fading channels, which pose new challenges in obtaining analytical expressions. In this context, we derived exact closed-form expressions of the OP and throughput of both SPSR and ODPSR schemes. We also obtained the optimal power splitting ratio of ODPSR for maximizing the achievable capacity at the destination. Finally, we present extensive numerical and simulation results to confirm our analytical findings. Both simulation and analytical results show the superiority of ODPSR over SPSR.

Malicious jamming attacks have been regarded as a serious threat to Internet of Things (IoT) networks, which can significantly degrade the Quality of Service (QoS) of users. This article utilizes an intelligent reflecting surface (IRS) to enhance anti-jamming performance due to its capability in reconfiguring the wireless propagation environment via dynamically adjusting each IRS reflecting elements. To enhance the communication performance against jamming attacks, a robust beamforming optimization problem is formulated in a multiuser IRS-assisted anti-jamming communications scenario with or without imperfect jammer’s channel state information (CSI). In addition, we further consider the fact that the jammer’s transmit beamforming can not be known at BS. Specifically, with no knowledge of jammers transmit beamforming, the total transmit power minimization problems are formulated subject to the outage probability requirements of legitimate users with the jammer’s statistical CSI, and signal-to-interference-plus-noise ratio requirements of legitimate users without the jammer’s CSI, respectively. By applying the decomposition-based large deviation inequality, Bernstein-type inequality, Cauchy–Schwarz inequality, and penalty nonsmooth optimization method, we efficiently solve the initial intractable and nonconvex problems. Numerical simulations demonstrate that the proposed anti-jamming approaches achieve superior anti-jamming performance and lower power-consumption compared to the non-IRS scheme and reveal the impact of key parameters on the achievable system performance.

High-throughput satellite communication systems are growing in strategic importance thanks to their role in delivering broadband services to mobile platforms and residences and/or businesses in rural and remote regions globally. Although precoding has emerged as a prominent technique to meet ever-increasing user demands, there is a lack of studies dealing with congestion control. This paper enhances the performance of multi-beam high throughput geostationary satellite systems under congestion, where the users’ quality of service (QoS) demands cannot be fully satisfied with limited resources. In particular, we propose congestion control strategies, relying on simple power control schemes. We formulate a multi-objective optimization framework balancing the system sum-rate and the number of users satisfying their QoS requirements. Next, we propose two novel approaches that effectively handle the proposed multi-objective optimization problem. The former is a model-based approach that relies on the weighted sum method to enrich the number of satisfied users by solving a series of the sum-rate optimization problems in an iterative manner. The latter is a data-driven approach that offers a low-cost solution by utilizing supervised learning and exploiting the optimization structures as continuous mappings. The proposed general framework is evaluated for different linear precoding techniques, for which the low computational complexity algorithms are designed. Numerical results manifest that our proposed framework effectively handles the congestion issue and brings superior improvements of rate satisfaction to many users than previous works. Furthermore, the proposed algorithms show low run-time and make them realistic for practical systems.

This paper investigates the downlink (DL) transmit design for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication systems, where only the slow-varying statistical channel state information is exploited at the transmitter. The channel model for the DL massive MIMO LEO satellite system is established, in which both the satellite and the user terminals (UTs) are equipped with uniform planar arrays. Observing the rank-one property of the channel matrices, we show that the single-stream precoding for each UT is the optimal choice that maximizes the ergodic sum rate. This favorable result simplifies the complicated design of transmit covariance matrices into that of precoding vectors without any loss of optimality. Then, an efficient algorithm is devised to compute the precoding vectors. Furthermore, we formulate an approximate transmit design based on the upper bound on the ergodic sum rate, for which the optimality of single-stream precoding still holds. We show that, in this case, the design of precoding vectors can be simplified into that of scalar variables, for which an effective algorithm is developed. In addition, a low-complexity learning framework is proposed for optimizing the scalar variables. Simulation results demonstrate that the proposed approaches can achieve significant performance gains over the existing schemes.

The integration of aerial platforms to provide ubiquitous coverage and connectivity for densely deployed terrestrial networks is expected to be a reality in the emerging sixth-generation networks. Energy-effificient and secure transmission designs are two important components for integrated terrestrial-aerial networks (ITAN). Inlight of the potential of reconfigurable intelligent surface (RIS) for significantly reducing the system power consumption and boosting information security, this paper proposes a multi-layer RIS-assisted secure ITAN architecture to defend against simultaneous jamming and eavesdropping attacks, and investigates energy-efficient hybrid beamforming for it. Specifically, with the availability of imperfect angular channel state information (CSI), we propose a block coordinate descent (BCD) framework for the joint optimization of the user’s received decoder, the terrestrial and aerial digital precoder, and the multi-layer RIS analog precoder to maximize the system energy efficiency (EE) performance. For the design of the received decoder, a heuristic beamforming scheme is proposed to convert the worst-case design problem into a min-max one and facilitate the developing a closed-form solution. For the design of the digital precoder, we propose an iterative sequential convex approximation approach via capitalizing the auxiliary variables and first-order Taylor series expansion. Finally, a monotonic vertex-update algorithm with a penalty convex-concave procedure (P-CCP) is proposed to obtain the analog precoder with satisfactory performance. Numerical results show the superiority and effectiveness of the proposed optimization framework and architecture over various benchmark schemes.

We consider an aeronautical ad-hoc network relying on aeroplanes operating in the presence of a spoofer. The aggregated signal received by the terrestrial base station is considered as “clean” or “normal”, if the legitimate aeroplanes transmit their signals and there is no spoofing attack. By contrast, the received signal is considered as “spurious” or “abnormal” in the face of a spoofing signal. An autoencoder (AE) is trained to learn the characteristics/features from a training dataset, which contains only normal samples associated with no spoofing attacks. The AE takes original samples as its input samples and reconstructs them at its output. Based on the trained AE, we define the detection thresholds of our spoofing discovery algorithm. To be more specific, contrasting the output of the AE against its input will provide us with a measure of geometric waveform similarity/dissimilarity in terms of the peaks of curves. To quantify the similarity betweenunknowntesting samples and thegiventraining samples (including normal samples), we first propose a so-calleddeviation-based algorithm. Furthermore, we estimate the angle of arrival (AoA) from each legitimate aeroplane and propose a so-calledAoA-based algorithm. Then based on a sophisticated amalgamation of these two algorithms, we form our final detection algorithm for distinguishing the spurious abnormal samples from normal samples under a strict testing condition. In conclusion, our numerical results show that the AE improves the trade-off between the correct spoofing detection rate and the false alarm rate as long as the detection thresholds are carefully selected.

In this paper, we propose a novel economic-efficiency framework for an electric vehicle (EV) network to maximize the profits (i.e., the amount of money that can be earned) for charging stations (CSs). To that end, we first introduce an energy demand prediction method for CSs leveraging federated learning approaches, in which each CS can train its own energy transactions locally and exchange its learned model with other CSs to improve the learning quality while protecting the CS's information privacy. Based on the predicted energy demands, each CS can reserve energy from the smart grid provider (SGP) in advance to optimize its profit. Nonetheless, due to the competition among the CSs as well as unknown information from the SGP, i.e., the willingness to transfer energy, we develop a multi-principal one-agent (MPOA) contract-based method to address these issues. In particular, we formulate the CSs’ profit maximization as a non-collaborative energy contract problem under the SGP's unknown information and common constraints as well as other CSs’ contracts. To solve this problem, we transform it into an equivalent low-complexity optimization problem and develop an iterative algorithm to find the optimal contracts for the CSs. Through simulation results using a real CS dataset, we demonstrate that our proposed framework can enhance energy demand prediction accuracy up to 24.63 percent compared with other machine learning algorithms. Furthermore, our proposed framework can outperform other economic models by 48 and 36 percent in terms of the CSs’ utilities and social welfare (i.e., the total profits of all participating entities) of the network, respectively.

This paper studies the joint multislot design of user scheduling and precoding to minimize the time needed to serve all the users for unicast and multicast transmission in single-cell multiuser MISO downlink systems. In the literature, the joint design of scheduling and precoding is typically undertaken based on feedback from previous slots. In a system with time-varying channels and QoS requirements, joint multislot designs can achieve better performance since they have the flexibility to schedule users over multiple slots and also can split users across slots efficiently. Further, a joint multislot design can provide a feasible solution even when the sequential design fails. In this paper, scheduling is represented by a binary matrix where the rows represent users, columns represent slots and entries represent scheduling of users in the slots. Noticing that the users may not be permuted across slots for time-varying channels, service time needed for scheduling is rendered as the highest column index corresponding to non-zero columns. With the help of binary scheduling matrix, service time minimization is formulated as a structured mixed-Boolean fractional programming. Further, by exploiting the hidden convex-concave structure in the problem, a convex-concave procedure-based iterative algorithm is proposed. Finally, we vindicate the necessity and illustrate the superiority in performance of joint multislot design over the sequential solution through Monte-Carlo simulations.

Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works on Massive MIMO have been focused on the system performance with complex Gaussian channel impulse responses under rich-scattering environments. In contrast, this paper investigates the uplink ergodic spectral efficiency (SE) of each user under the double scattering channel model. We derive a closed-form expression of the uplink ergodic SE by exploiting the maximum ratio (MR) combining technique based on imperfect channel state information. We further study the asymptotic SE behaviors as a function of the number of antennas at each base station (BS) and the number of scatterers available at each radio channel. We then formulate and solve a total energy optimization problem for the uplink data transmission that aims at simultaneously satisfying the required SEs from all the users with limited data power resource. Notably, our proposed algorithms can cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of the closed-form ergodic SE over Monte-Carlo simulations. Besides, the system can still provide the required SEs to many users even under congestion.

Cell-Free Massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) are two promising technologies for application to beyond-5G networks. This paper considers Cell-Free Massive MIMO systems with the assistance of an RIS for enhancing the system performance under the presence of spatial correlation among the engineered scattering elements of the RIS. Distributed maximum-ratio processing is considered at the access points (APs). We introduce anaggregated channelestimation approach that provides sufficient information for data processing with the main benefit of reducing the overhead required for channel estimation. The considered system is studied by using asymptotic analysis which lets the number of APs and/or the number of RIS elements grow large. A lower bound for the channel capacity is obtained for a finite number of APs and engineered scattering elements of the RIS, and closed-form expressions for the uplink and downlink ergodic net throughput are formulated in terms of only the channel statistics. Based on the obtained analytical frameworks, we unveil the impact of channel correlation, the number of RIS elements, and the pilot contamination on the net throughput of each user. In addition, a simple control scheme for optimizing the configuration of the engineered scattering elements of the RIS is proposed, which is shown to increase the channel estimation quality, and, hence, the system performance. Numerical results demonstrate the effectiveness of the proposed system design and performance analysis. In particular, the performance benefits of using RISs in Cell-Free Massive MIMO systems are confirmed, especially if the direct links between the APs and the users are of insufficient quality with high probability.

This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals’ dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve.

Nowadays, Ambient Backscatter Communication (AmBC) systems have emerged as a green communication technology to enable massive self-sustainable wireless networks by leveraging Radio Frequency (RF) Energy Harvesting (EH) capability. A Full-duplex Ambient Backscatter Communication (FAmBC) network with a Full-duplex Access Point (AP), a dedicated Legacy User (LU), and several Backscatter Devices (BDs) is considered in this study. The AP with two antennas transfers downlink Orthogonal Frequency Division Multiplexing (OFDM) information and energy to the dedicated LU and several BDs, respectively, while receiving uplink backscattered information from BDs at the same time. One of the key aims in AmBC networks is to ensure fairness among BDs. To address this, we propose the Multi-objective Lexicographical Optimization Problem (MLOP), which aims to maximize the minimum BD’s throughput while enhancing overall BDs’ throughput, subject to the AP’s subcarrier power, BDs’ reflection coefficients, and backscatter time allocation. Owe to the MLOP is non-convex, we propose Difference Convex Algorithm (DCA) using Exterior Penalty Function Method (EPFM)—an inventive non-convex optimization method— to reach the optimal solution. The most critical advantage of applying this proposed approach is finding the globally optimal solution. The effectiveness of the proposed method supported by theoretical analysis confirms its superiority compared to some of the investigated suboptimal algorithms with the same computational complexity.

This work addresses the issue of interference generated by co-channel users in downlink multi-antenna multicarrier systems with frequency-packed faster-than-Nyquist (FTN) signaling. The resulting interference stems from an aggressive strategy for enhancing the throughput via frequency reuse across different users and the squeezing of signals in the time-frequency plane beyond the Nyquist limit. The spectral efficiency is proved to be increasing with the frequency packing and FTN acceleration factors. The lower bound for the FTN sampling period that guarantees information losslesness is derived as a function of the transmitting-filter roll-off factor, the frequency-packing factor, and the number of subcarriers. Space-time-frequency symbol-level precoders (SLPs) that trade off constructive and destructive interblock interference (IBI) at the single-antenna user terminals are proposed. Redundant elements are added as guard interval to cope with vestigial destructive IBI effects. The proposals can handle channels with delay spread longer than the multicarrier-symbol duration. The receiver architecture is simple, for it does not require digital multicarrier demodulation. Simulations indicate that the proposed SLP outperforms zero-forcing precoding and achieves a target balance between spectral and energy efficiencies by controlling the amount of added redundancy from zero (full IBI) to half (destructive IBI-free) the group delay of the equivalent channel.

Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of enabling FL over a wireless fog-cloud system (e.g., non-i.i.d. data, users’ heterogeneity), we first propose an efficient FL algorithm based on Federated Averaging (called$\mathsf {FedFog}$) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud. Next, we employ$\mathsf {FedFog}$in wireless fog-cloud systems by investigating a novel network-aware FL optimization problem that strikes the balance between the global loss and completion time. An iterative algorithm is then developed to obtain a precise measurement of the system performance, which helps design an efficient stopping criteria to output an appropriate number of global rounds. To mitigate the straggler effect, we propose a flexible user aggregation strategy that trains fast users first to obtain a certain level of accuracy before allowing slow users to join the global training updates. Extensive numerical results using several real-world FL tasks are provided to verify the theoretical convergence of$\mathsf {FedFog}$. We also show that the proposed co-design of FL and communication is essential to substantially improve resource utilization while achieving comparable accuracy of the learning model.

Intelligent reflecting surface (IRS), consisting of low-cost passive elements, is a promising technology for improving the spectral and energy efficiency of the fifth-generation (5G) and beyond networks. It is also noteworthy that an IRS can shape the reflected signal propagation. Most works in IRS-assisted systems have ignored the impact of the inevitable residual hardware impairments (HWIs) at both the transceiver hardware and the IRS while any relevant works have addressed only simple scenarios, e.g., with single-antenna network nodes and/or without taking the randomness of phase noise at the IRS into account. In this work, we aim at filling up this gap by considering a general IRS-assisted multi-user (MU) multiple-input single-output (MISO) system with imperfect channel state information (CSI) and correlated Rayleigh fading. In parallel, we present a general computationally efficient methodology for IRS reflecting beamforming (RB) optimization. Specifically, we introduce an advantageous channel estimation (CE) method for such systems accounting for the HWIs. Moreover, we derive the uplink achievable spectral efficiency (SE) with maximal-ratio combining (MRC) receiver, displaying three significant advantages being: 1) its closed-form expression, 2) its dependence only on large-scale statistics, and 3) its low training overhead. Notably, by exploiting the first two benefits, we achieve to perform optimization with respect to the RB that can take place only per several coherence intervals, and thus, reduces significantly the computational cost compared to other methods based on instantaneous CSI which require frequent phase optimization. Among the insightful observations, we highlight that the unrealistic assumption of uncorrelated Rayleigh fading does not allow optimization of the SE, which makes the application of an IRS ineffective. Also, in the case that the phase drifts, describing the distortion of the phases in the RBM, are uniformly distributed, the presence of an IRS provides no advantage. The analytical results outperform previous works and are verified by Monte-Carlo (MC) simulations.

Wireless communications are increasingly vulnerable to simultaneous jamming and eavesdropping attacks due to the inherent broadcast nature of wireless channels. With this focus, due to the potential of reconfigurable intelligent surface (RIS) in substantially saving power consumption and boosting information security, this paper is the first work to investigate the effect of the RIS-assisted wireless transmitter in improving both the spectrum efficiency and the security of multi-user cellular network. Specifically, with the imperfect angular channel state information (CSI), we aim to address the worst-case sum rate maximization problem by jointly designing the receive decoder at the users, both the digital precoder and the artificial noise (AN) at the base station (BS), and the analog precoder at the RIS, while meeting the minimum achievable rate constraint, the maximum wiretap rate requirement, and the maximum power constraint. To address the non-convexity of the formulated problem, we first propose an alternative optimization (AO) method to obtain an efficient solution. In particular, a heuristic scheme is proposed to convert the imperfect angular CSI into a robust one and facilitate the developing a closed-form solution to the receive decoder. Then, after reformulating the original problem into a tractable one by exploiting the majorization-minimization (MM) method, the digital precoder and AN can be addressed by the quadratically constrained quadratic programming (QCQP), and the RIS-aided analog precoder is solved by the proposed price mechanism-based Riemannian manifold optimization (RMO). To further reduce the computational complexity of the proposed AO method and gain more insights, we develop a low-complexity monotonic optimization algorithm combined with the dual method (MO-dual) to identify the closed-form solution. Numerical simulations using realistic RIS and communication models demonstrate the superiority and validity of our proposed schemes over the existing benchmark schemes.

Unmanned aerial vehicle (UAV) communication has emerged as a prominent technology for emergency communications (e.g., natural disaster) in the Internet of Things (IoT) networks to enhance the ability of disaster prediction, damage assessment, and rescue operations promptly. A UAV can be deployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer it to a ground gateway (GW). In general, the latency constraint at IoT devices and UAV’s limited storage capacity highly hinder practical applications of UAV-assisted IoT networks. In this paper, full-duplex (FD) radio is adopted at the UAV to overcome these challenges. In addition, half-duplex (HD) scheme for UAV-based relaying is also considered to provide a comparative study between two modes (viz., FD and HD). Herein, a device is considered to be successfully served if its data is collected by the UAV and conveyed to GW timely during flight time. In this context, we aim to maximize the number of served IoT devices by jointly optimizing bandwidth, power allocation, and the UAV trajectory while satisfying each device’s requirement and the UAV’s limited storage capacity. The formulated optimization problem is troublesome to solve due to its non-convexity and combinatorial nature. Towards appealing applications, we first relax binary variables into continuous ones and transform the original problem into a more computationally tractable form. By leveraging inner approximation framework, we derive newly approximated functions for non-convex parts and then develop a simple yet efficient iterative algorithm for its solutions. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. Finally, numerical results show that the proposed algorithms significantly outperform benchmark approaches in terms of the number of served IoT devices and system throughput.

Multiuser techniques play a central role in the fifth-generation (5G) and beyond 5G (B5G) wireless networks that exploit spatial diversity to serve multiple users simultaneously in the same frequency resource. It is well known that a multi-antenna base station (BS) can efficiently serve a number of users not exceeding the number of antennas at the BS via precoding design. However, when there are more users than the number of antennas at the BS, conventional precoding design methods perform poorly because inter-user interference cannot be efficiently eliminated. In this paper, we investigate the performance of a highly-loaded multiuser system in which a BS simultaneously serves a number of users that is larger than the number of antennas. We propose a dynamic bandwidth allocation and precoding design framework and apply it to two important problems in multiuser systems: i) User fairness maximization and ii) Transmit power minimization, both subject to predefined quality of service (QoS) requirements. The premise of the proposed framework is to dynamically assign orthogonal frequency channels to different user groups and carefully design the precoding vectors within every user group. Since the formulated problems are non-convex, we propose two iterative algorithms based on successive convex approximations (SCA), whose convergence is theoretically guaranteed. Furthermore, we propose a low-complexity user grouping policy based on the singular value decomposition (SVD) to further improve the system performance. Finally, we demonstrate via numerical results that the proposed framework significantly outperforms existing designs in the literature.

Next-generation satellite systems require more flexibility in resource management such that available radio resources can be dynamically allocated to meet time-varying and non-uniform traffic demands. Considering potential benefits of beam hopping (BH) and non-orthogonal multiple access (NOMA), we exploit the time-domain flexibility in multi-beam satellite systems by optimizing BH design, and enhance the power-domain flexibility via NOMA. In this paper, we investigate the synergy and mutual influence of beam hopping and NOMA. We jointly optimize power allocation, beam scheduling, and terminal-timeslot assignment to minimize the gap between requested traffic demand and offered capacity. In the solution development, we formally prove the NP-hardness of the optimization problem. Next, we develop a bounding scheme to tightly gauge the global optimum and propose a suboptimal algorithm to enable efficient resource assignment. Numerical results demonstrate the benefits of combining NOMA and BH, and validate the superiority of the proposed BH-NOMA schemes over benchmarks.

Low earth orbit (LEO) satellite-assisted communications have been considered as one of the key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose an exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve a massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. We first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL’s effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods.

Due to the emergence of non-terrestrial platforms with extensive coverage, flexible deployment, and reconfigurable characteristics, the hybrid satellite-aerial-terrestrial networks (HSATNs) can accommodate a great variety of wireless access services in different applications. To effectively reduce the transmission latency and facilitate the frequent update of files with improved spectrum efficiency, we investigate the performance of cache-enabled HSATN, where the user retrieves the required content files from the cache-enabled aerial node (AN) or the satellite with the non-orthogonal multiple access (NOMA) scheme. If the required content files of the user are cached in the AN, the cache-enabled node would serve directly. Otherwise, the user would retrieve the content file from the satellite system, where the satellite system seeks opportunities for proactive content pushing to ANs during the user content delivery phase. Specifically, taking into account the uncertainty of the number and location of ANs, along with the channel fading of terrestrial users, the outage probability and hit probability of the considered network are, respectively, derived based on stochastic geometry. Numerical results unveil the effectiveness of the cache-enabled HSATN with the NOMA scheme and proclaim the influence of key factors on the system performance. The realistic, tractable, and expandable framework, as well as associated methodology, provide both useful guidance and a solid foundation for evolved networks with advanced configurations in the performance of cache-enabled HSATN.

Smart radio resource allocation combined with the recent advances of digital payloads will allow to control the transmit power and bandwidth of the satellites depending on the demand and the channel conditions of users. The system flexibility is important not only to handle divergent demand requirements but also to efficiently utilize the limited and expensive satellite resources. In this paper, we propose a demand-aware smart radio resource allocation technique, where the transmit power and the bandwidth of the GEO satellite are minimized while satisfying the user demand. The formulated optimization problem is non-convex mixed-integer nonlinear program which is difficult to solve. Hence, we apply a quadratic transform to solve the problem iteratively. The numerical results showed that the proposed scheme outperforms the benchmark schemes in terms of bandwidth utilization while accurately providing capacity-on-demand.

This paper considers Cell-Free Massive Multiple Input Multiple Output (MIMO) systems with the assistance of an RIS for enhancing the system performance. Distributed maximum-ratio combining (MRC) is considered at the access points (APs). We introduce an aggregated channel estimation method that provides sufficient information for data processing. The considered system is studied by using asymptotic analysis which lets the number of APs and/or the number of RIS elements grow large. A lower bound for the channel capacity is obtained for a finite number of APs and engineered scattering elements of the RIS, and closed-form expression for the uplink ergodic net throughput is formulated. In addition, a simple scheme for controlling the configuration of the RIS scattering elements is proposed. Numerical results verify the effectiveness of the proposed system design and the benefits of using RISs in Cell-Free Massive MIMO systems are quantified.

In this paper, we investigate the multiplexing of grant-based (GB) and grant-free (GF) device transmissions in an uplink heterogeneous network (HetNet), namely GB-GF HetNet, where the devices transmit their information using low-rate short data packets. Specifically, GB devices are granted unique time-slots for their transmissions. In contrast, GF devices can randomly select time-slots to transmit their messages utilizing the GF non-orthogonal multiple access (NOMA), which has emerged as a promising enabler for massive access and reducing access latency. However, random access (RA) in the GF NOMA can cause collisions and severe interference, leading to system performance degradation. To overcome this issue, we propose a multiple access (MA) protocol based on reinforcement learning for effective RA slots allocation. The proposed learning method aims to guarantee that the GF devices do not cause any collisions to the GB devices and the number of GF devices choosing the same time-slot does not exceed a predetermined threshold to reduce the interference. In addition, based on the results of the RA slots allocation using the proposed method, we derive the approximate closed-form expressions of the average decoding error probability (ADEP) for all devices to characterize the system performance. Our results presented in terms of access efficiency (AE), collision probability (CP), and overall ADEP (OADEP), show that our proposed method can ensure a smooth operation of the GB and GF devices within the same network while significantly minimizing the collision and interference among the device transmissions in the GB-GF HetNet.

In this paper, we apply non-orthogonal multiple access (NOMA) in satellite systems to assist data transmission for services with latency constraints. We investigate a problem to minimize the transmission time by jointly optimizing power allocation and terminal-timeslot assignment for accomplishing a transmission task in NOMA-enabled satellite systems. The problem appears non-linear/non-convex with integer variables and can be equivalently reformulated in the format of mixed-integer convex programming (MICP). Conventional iterative methods may apply but at the expenses of high computational complexity in approaching the optimum or near-optimum. We propose a combined learning and optimization scheme to tackle the problem, where the primal MICP is decomposed into two learning-suited classification tasks and a power allocation problem. In the proposed scheme, the first learning task is to predict the integer variables while the second task is to guarantee the feasibility of the solutions. Numerical results show that the proposed algorithm outperforms benchmarks in terms of average computational time, transmission time performance, and feasibility guarantee.

In large-scale antenna array (LSAA) wireless communication systems employing analog beamforming architectures, the placement or selection of a subset of antennas can significantly reduce the power consumption and hardware complexity. In this work, we propose a joint design of analog beamforming with antenna selection (AS) or antenna placement (AP) for an analog beamforming system. We approach this problem from a beampattern matching perspective and formulate a sparse unit-modulus least-squares (SULS) problem, which is a nonconvex problem due to the unit-modulus and the sparsity constraints. To that end, we propose an efficient and scalable algorithm based on the majorization-minimization (MM) framework for solving the SULS problem. We show that the sequence of iterates generated by the algorithm converges to a stationary point of the problem. Numerical results demonstrate that the proposed joint design of analog beamforming with AS outperforms conventional array architectures with fixed inter-antenna element spacing.

We consider a multi-group (MG) multicasting (MC) system wherein a multi-antenna transmitter serves heterogeneous users capable of either information decoding (ID) or energy harvesting (EH), or both. In this context, we investigate a precoder design framework to explicitly serve the ID and EH users categorized within certain MC and EH groups. Specifically, the ID users are categorized within multiple MC groups while the EH users are a part of single (last) group. We formulate a problem to optimize the energy efficiency in the considered scenario under a quality-of-service (QoS) constraint. An algorithm based on Dinkelback method, slack-variable replacement, and second-order conic programming (SOCP)/semi-definite relaxation (SDR) is proposed to obtain a suitable solution for the above-mentioned fractional-objective dependent non-convex problem. Simulation results illustrate the benefits of proposed algorithm under several operating conditions and parameter values, while drawing a comparison between the two proposed methods.

In this paper, the performance of an RF energy harvesting scheme for multi-user massive multiple-input multiple-output (MIMO) is investigated in the presence of multiple active jammers. The key idea is to exploit the jamming transmissions as an energy source to be harvested at the legitimate users. To this end, the achievable uplink sum rate expressions are derived in closed-form for two different antenna configurations. An optimal time-switching policy is also proposed to ensure user-fairness in terms of both harvested energy and achievable rate. Besides, the essential trade-off between the harvested energy and achievable sum rate are quantified in closed-form. Our analysis reveals that the massive MIMO systems can make use of RF signals of the jamming attacks for boosting the amount of harvested energy at the served users. Numerical results illustrate the effectiveness of the derived closed-form expressions over Monte-Carlo simulations.

This paper considers a massive MIMO system under the double scattering channels. We derive a closed-form expression of the uplink ergodic spectral efficiency (SE) by exploiting the maximum-ratio combining technique with imperfect channel state information. We then formulate and solve a total uplink data power optimization problem that aims at simultaneously satisfying the required SEs from all the users with limited power resources. We further propose algorithms to cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of our proposed power optimization. More importantly, our proposed congestion-handling algorithms can guarantee the required SEs to many users under congestion, even when the SE requirement is high.

This paper address the design of a phase tracking block for the DVB-S2X user terminals in a satellite precoding system. The spectral characteristics of the phase noise introduced by the oscillator, the channel, and the thermal noise at the receiver are taken into account. Using the expected phase noise mask, the optimal parameters for a second-order PLL intended to track channel variations from the pilots are calculated. To validate the results a Simulink model was implemented considering the characteristics of the hardware prototype. The performance of the design was evaluated in terms of the accuracy and stability for the frame structure of superframe Format 2, as described in Annex E of DVB-S2X.

Although the intelligent reflecting surface (IRS) has attracted significant interest, existing works have not addressed adequately the impact of its inevitable phase errors by taking into account their randomness. In this work, we focus on covering this gap by considering a general large IRS-assisted multi-user (MU) multiple-input single-output (MISO) system with imperfect CSI and correlated Rayleigh fading. On this ground, we perform a beneficial channel estimation (CE), and we obtain the achievable sum spectral efficiency (SE) in closed-form in terms of the large-scale channel statistics. The whole approach suggests a novel computationally efficient method for reflect beamforming matrix (RBM) optimization of IRS-assisted large multi-antenna systems that can take place at every several coherence intervals. Monte-Carlo simulations verify the analytical insightful results. Among the observations, we highlight that if the IRS phase noise follows the uniform distribution or if independent Rayleigh fading is assumed, the use of the IRS has no benefit.

Distributed antenna arrays, fractionated payloads and cooperative platforms can provide unprecedented performance in the next generation of spaceborne communications and remote sensing systems. Remote phase synchronization of physically separated oscillators is the first step towards a coherent operation of distributed systems. This work shows the preliminary results of a TDD remote phase synchronization algorithm with a master-follower architecture. Herein, we describe the implementation and validation of the proposed algorithm. The implementation has been conducted in a Cubesat-ready software defined radio and validated at the end-to-end satellite communications testbed available at the University of Luxembourg.

This paper studies the Gateway Diversity concept in a satellite scenario, using ONOS, Mininet and OpenSAND. ONOS is used as SDN controller, while Mininet and OpenSAND are used for network emulation and satellite network emulation, respectively. The ability and efficiency of ONOS SDN controller to switch the traffic, for example due to weather conditions, in real-time between two Gateways is presented. Furthermore, a method for allowing automatic instantiation of Gateway and restart of the OpenSAND simulation process promptly is analyzed. Finally, simulation results are shown for the evaluation of these technologies.

The evolution of 5G wireless networks poses significant research challenges such as securing the user data, maintaining certain latency and reliability requirements etc. However, it can be challenging to simultaneously meet these performance requisites, which may lead to resort to a trade-off among different metrics. This paper investigates the secrecy-reliability performance trade-off (SRPT) for non-orthogonal multiple access (NOMA)-based millimeter wave (mmWave) networks. Herein, we consider two end-users, namely primary and secondary, which are served by an mmWave base station using downlink NOMA. Besides, a passive eavesdropper lying in the vicinity of these end-users attempts to intercept their legitimate message signals. For this set-up, we derive the closed-form expressions of the outage probability (OP) of a targeted end-user and intercept probability (IP) of the eavesdropper to analyze the SRPT of the system. We further propose a low-complexity average channel state information (CSI)-based power allocation strategy to improve the reliability of a targeted user while maintaining its information secrecy. Moreover, we obtain the condition under which NOMA guarantees superior secrecy performance than that of orthogonal multiple access (OMA) scheme. We corroborate our theoretical analysis via simulation results presented in terms of IP and OP.

In massive machine-type communications (mMTC) networks, the ever-growing number of MTC devices and the limited radio resources have caused a severe problem of random access channel (RACH) congestion. To mitigate this issue, several potential multiple access (MA) mechanisms including sparse code MA (SCMA) have been proposed. Besides, the short-packet transmission feature of MTC devices requires the design of new transmission and congestion avoidance techniques as the existing techniques based on the assumption of infinite data-packet length may not be suitable for mMTC networks. Therefore, it is important to find novel solutions to address RACH congestion in mMTC networks while considering SCMA and short-packet communications (SPC). In this paper, we propose an SCMA-based random access (RA) method, in which Q-learning is utilized to dynamically allocate the SCMA codebooks and time-slot groups to MTC devices with the aim of minimizing the RACH congestion in SPC-based mMTC networks. To clarify the benefits of our proposed method, we compare its performance with those of the conventional RA methods with/without Q-learning in terms of RA efficiency and evaluate its convergence. Our simulation results show that the proposed method outperforms the existing methods in overloaded systems, i.e., the number of devices is higher than the number of available RA slots. Moreover, we illustrate the sum rate comparison between SPC and long-packet communications (LPC) when applying the proposed method to achieve more insights on SPC.

The quest for finding a small-sized energy supply to run the small-scale wireless gadgets, with almost an infinite lifetime, has intrigued humankind since past several decades. In this context, the concept of Quantum batteries has come into limelight more recently to serve the purpose. However, the main issue revolving around the closed-system design of Quantum batteries is to ensure a loss-less environment, which is extremely difficult to realize in practice. In this paper, we present the modeling and optimization aspects of a Radio-Frequency (RF) Energy Harvesting (EH) assisted Quantum battery, wherein several EH modules (in the form of micro- or nano- sized integrated circuits (ICs)) help each of the involved Quantum sources achieve the so-called quasi-stable state. Specifically, a micro-controller manages the overall harvested energy from the RF-EH ICs and a photon emitting device, such that the emitted photons are absorbed by the electrons in the Quantum sources. In order to precisely model and optimize the considered framework, we formulate a transmit power minimization problem for an RF-based wireless system to optimize the number of RF-EH ICs under the given EH constraints at the Quantum battery-enabled wireless device. We obtain an analytical solution to the above-mentioned problem using a rational approach, while additionally seeking another solution obtained via a non-linear program solver. The effectiveness of the proposed technique is reported in the form of numerical results by taking a range of system parameters into account.

Satellite Communications offer complementary benefits to terrestrial 5G/6G infrastructure, covering a wide range of use cases in need of ubiquitous coverage and reliability. However, to be as competitive as the terrestrial counterpart in terms of supplied throughput, satellite communications require a highly efficient use of the limited available spectrum. Linear precoding has demonstrated the ability to boost the spectral efficiency in the satellite domain, but raising a new issue: the bandwidth requirements of the feeder link. Deployment of several gateways, each of which precoding an independent cluster of beams causes performance degradation. Therefore, in this paper, we investigate the centralized gateway concept, where all digital baseband processes (including precoding) are implemented in a remote server connected via high speed fibers to the distributed remote gateways responsible for the downlink and uplink of the satellite radio frequency signals. In particular, we highlight the main technical challenges and provide a preliminary vision of potential solutions.

This paper investigates the effective rate when the instantaneous received signal-to-noise ratio (SNR) could be modeled as the mixture Gamma (MG), mixture of Gaussian (MoG), and Fox’s H-function distributed random variable (RV), respectively. Three closed-form expressions are correspondingly derived in terms of the Fox’s H-function. The obtained analytical results are further examined by the Monte-Carlo simulation. One can observe that (i) the analytical solutions provide an excellent match with the Monte-Carlo simulation results; (ii) the MG and MoG approaches provide highly approximated solutions, and the MG is better due to a simpler form; and (iii) the Fox’s H-function solution is exact and offers a unified, general and flexible framework for the effective rate analysis.

This paper addresses the radio access network (RAN) resource slicing problem in the context of the joint allocation of transmit powers and time-frequency resource blocks (RBs) in the 5G system consisting of ultra-reliable and low-latency communication (URLLC) and enhanced mobile broadband (eMBB) users. Specifically, we formulate a modulation and coding scheme (MCS) based optimization problem to maximize the sum goodput of eMBB users while satisfying URLLC and eMBB users' QoS requirements. The proposed scheme considers the impact of imperfect channel state information (CSI) and the active user's queue status for the dynamic assignment of radio resources to the heterogeneous users according to its demand. The resulting mixed-integer non-convex problem is first transformed into a tractable form by exploiting the probabilistic to non-probabilistic conversion, Big-M theory, and difference-of-convex (DC) programming. Later, the transformed problem is solved using the successive convex approximation (SCA) based iterative algorithm. Our simulation results illustrate the superiority of the proposed algorithm compared to the baseline methods in terms of eMBB rate, latency in delivering the URLLC packets, and total power consumption.

We investigate the downlink (DL) transmit strategy for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication (SATCOM) systems, in which only the slow-varying statistical channel state information is known at the transmitter side. First, we derive the massive MIMO LEO satellite channel model, when the uniform planar arrays are deployed at both the satellite and user terminals (UTs). Building on the rank-one property of the satellite channel matrices, we show that transmitting a single data stream to each UT is optimal in the sense that the ergodic sum rate is maximized. This result is of great importance for massive MIMO LEO SATCOM systems, since the sophisticated design of transmit covariance matrices is turned into that of precoding vectors, without loss of optimality. Furthermore, we develop an algorithm to compute the precoding vectors. Simulation results show the significant performance gains of the proposed approaches over the existing schemes.

The ever-increasing number of machine-type communications (MTC) devices and the limited available radio resources are leading to a crucial issue of radio access network (RAN) congestion in upcoming 5G and beyond wireless networks. Thus, it is crucial to investigate novel techniques to minimize RAN congestion in massive MTC (mMTC) networks while taking the underlying short-packet communications (SPC) into account. In this paper, we propose an adaptive Q-learning (AQL) algorithm based on block error rate (BLER), an important metric in SPC, for a non-orthogonal multiple access (NOMA) based mMTC system. The proposed method aims to efficiently accommodate MTC devices to the available random access (RA) slots in order to significantly reduce the possible collisions, and subsequently to enhance the system throughput. Furthermore, in order to obtain more practical insights on the system design, the scenario of imperfect successive interference cancellation (ISIC) is considered as compared to the widely-used perfect SIC assumption. The performance of the proposed AQL method is compared with the recent Q-learning solutions in the literature in terms of system throughput over a range of parameters such as the number of devices, blocklength, and residual interference caused by ISIC, along with its convergence evaluation. Our simulation results illustrate the superiority of the proposed method over the existing techniques, in the scenarios where the number of devices is higher than the number of available RA time-slots.

In this paper, the limits in terms of offered capacity for a non-precoded geostationary (GEO) satellite communication system is investigated. In particular, we focus on the smart radio resource assignment as a technique to manage the interference across the multi-beam pattern of the GEO system. In this context, a joint power and carrier allocation problem is formulated to maximize the capacity of the system. The formulated optimization problem is non-convex and difficult to solve. Hence, we propose to address the frequency allocation first by assuming an equal power distribution, followed by the optimal power assignment to maximize the sum-capacity. Numerical evaluations are presented comparing the proposed method with a precoded-based system and with benchmark resource allocation schemes, showing the benefits of the proposed technique and identifying the limits of a non-precoded GEO satellite communications system.

The growing demand for broadband applications has driven the satellite communication service providers to investigate High Throughput Satellite (HTS) solutions. While precoding has been identified as the most promising technique to boost the satellite spectral efficiency, new advanced solutions focus on re-configurable demand-driven systems, where throughput delivered aligns with the time and geographical variations of the traffic demand. For such goal, conventional user scheduling algorithms fail to meet the uneven user traffic demand. In this paper, we propose a novel unicast scheduling algorithm that takes into account both the channel orthogonality required for precoding along with the particular user demands. We name such technique as Weighted Semi-Orthogonal Scheduling (WSOS) methodology. Supporting numerical results are provided that validate the effectiveness of the proposed scheduling and quantify the benefits over conventional scheduling techniques.

In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design a hard decoding scheme by using precoded pilot symbols as training data. Within this, we propose an ML framework for a multi-antenna hard decoder that allows an Eve to decode the transmitted message with decent accuracy. We show that MU-MISO systems are vulnerable to such an attack when conventional block-level precoding is used. To counteract this attack, we propose a novel symbol-level precoding scheme that increases the bit-error rate at Eve by obstructing the learning process. Simulation results validate both the ML-based attack as well as the countermeasure, and show that the gain in security is achieved without affecting the performance at the intended users.

In this paper, we investigate potential synergies of non-orthogonal multiple access (NOMA) and beam hopping (BH) for multi-beam satellite systems. The coexistence of BH and NOMA provides time-power-domain flexibilities in mitigating a practical mismatch effect between offered capacity and requested traffic per beam. We formulate the joint BH scheduling and NOMA-based power allocation problem as mixed-integer non-convex programming. We reveal the exponential-conic structure for the original problem, and reformulate the problem to the format of mixed-integer conic programming (MICP), where the optimum can be obtained by exponential-complexity algorithms. A greedy scheme is proposed to solve the problem on a timeslot-by-timeslot basis with polynomial-time complexity. Numerical results show the effectiveness of the proposed efficient suboptimal algorithm in reducing the matching error by 62.57% in average over the OMA scheme and achieving a good trade-off between computational complexity and performance compared to the optimal solution.

Abstract Hybrid processing in millimeter wave (mmWave) communication has been proposed as a solution to reduce the cost and energy consumption by reducing the number of radio‐frequency (RF) chains. However, the impact of the inevitable residual transceiver hardware impairments (RTHIs), including the residual additive transceiver hardware impairments (RATHIs) and the amplified thermal noise (ATN), has not been sufficiently studied in mmWave hybrid processing. In this work, the hybrid precoder and combiner are designed, which include both digital and analog processing by taking into account the RATHIs and the ATN. In particular, a thorough study is provided to shed light on the degradation of the spectral efficiency (SE) of the practical system. The outcomes show the steady degradation of the performance by the ATN across all SNR values, which becomes increasingly critical for higher values of its variance. Furthermore, it is shown that RATHIs result in degradation of the system only in the high SNR regime. Hence, their impact in mmWave system operating at low SNRs might be negligible. Moreover, an increase concerning the number of streams differentiates the impact between the transmit and receive RATHIs with the latter having a more severe effect.

The narrowband Internet-of-Things (NB-IoT) standard is a new cellular wireless technology, which has been introduced by the 3rd generation partnership project (3GPP) with the goal to connect massive low-cost, low-complexity and long-life IoT devices with extended coverage. In order to improve power efficiency, 3GPP proposed a new random access (RA) waveform for NB-IoT based on a single-tone frequency-hopping scheme. RA handles the first connection between user equipments (UEs) and the base station (BS). Through this, UEs can be identified and synchronized with the BS. In this context, receiver methods for the detection of the new waveform should satisfy the requirements on the successful user detection as well as the timing synchronization accuracy. This is not a trivial task, especially in the presence of radio impairments like carrier frequency offset (CFO) which constitutes one of the main radio impairments besides the noise. In order to tackle this problem, we propose a new receiver method for NB-IoT physical RA channel (NPRACH). The method is designed to eliminate perfectly the CFO without any additional computational complexity and supports all NPRACH preamble formats. The associated performance has been evaluated under 3GPP conditions. We observe a very high performance compared both to 3GPP requirements and to the existing state-of-the-art methods in terms of detection accuracy and complexity.

Mobile-edge computing (MEC) has emerged as a prominent technology to overcome sudden demands on computation-intensive applications of the Internet of Things (IoT) with finite processing capabilities. Nevertheless, the limited energy resources also seriously hinder IoT devices from offloading tasks that consume high power in active RF communications. Despite the development of energy harvesting (EH) techniques, the harvested energy from surrounding environments could be inadequate for power-hungry tasks. Fortunately, backscatter communications (Backcom) is an intriguing technology to narrow the gap between the power needed for communication and harvested power. Motivated by these considerations, this article investigates a backscatter-assisted data offloading in OFDMA-based wireless-powered (WP) MEC for IoT systems. Specifically, we aim at maximizing the sum computation rate by jointly optimizing the transmit power at the gateway (GW), backscatter coefficient, time-splitting (TS) ratio, and binary decision-making matrices. This problem is challenging to solve due to its nonconvexity. To find solutions, we first simplify the problem by determining the optimal values of transmit power of the GW and backscatter coefficient. Then, the original problem is decomposed into two subproblems, namely, TS ratio optimization with given offloading decision matrices and offloading decision optimization with given TS ratio. Especially, a closed-form expression for the TS ratio is obtained which greatly enhances the CPU execution time. Based on the solutions of the two subproblems, an efficient algorithm, termed the fast-efficient algorithm (FEA), is proposed by leveraging the block coordinate descent method. Then, it is compared with exhaustive search (ES), the bisection-based algorithm (BA), edge computing (EC), and local computing (LC) used as reference methods. As a result, the FEA is the best solution which results in a near-globally-optimal solution at a much lower complexity as compared to benchmark schemes. For instance, the CPU execution time of FEA is about 0.029 s in a 50-user network, which is tailored for ultralow latency applications of IoT networks.

Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication overhead. However, FL still faces a number of challenges such as nonindependent and identically distributed data and heterogeneity of user equipments (UEs). Enabling a large number of UEs to join the training process in every round raises a potential issue of the heavy global communication burden. To address these issues, we generalize the current state-of-the-art federated averaging (FedAvg) by adding a weight-based proximal term to the local loss function. The proposed FL algorithm runs stochastic gradient descent in parallel on a sampled subset of the total UEs with replacement during each global round. We provide a convergence upper bound characterizing the tradeoff between convergence rate and global rounds, showing that a small number of active UEs per round still guarantees convergence. Next, we employ the proposed FL algorithm in wireless Internet-of-Things (IoT) networks to minimize either total energy consumption or completion time of FL, where a simple yet efficient path-following algorithm is developed for its solutions. Finally, numerical results on unbalanced data sets are provided to demonstrate the performance improvement and robustness on the convergence rate of the proposed FL algorithm over FedAvg. They also reveal that the proposed algorithm requires much less training time and energy consumption than the FL algorithm with full user participation. These observations advocate the proposed FL algorithm for a paradigm shift in bandwidth-constrained learning wireless IoT networks.

In this article, we investigate the multichannel cooperative spectrum sharing in hybrid satellite-terrestrial Internet of Things (IoT) networks with the auction mechanism, which is designed to reduce the operational expenditure of the satellite-based IoT (S-IoT) network while alleviating the spectrum scarcity issues of terrestrial-based IoT (T-IoT) network. The cluster heads of selected T-IoT networks assist the primary satellite users transmission through cooperative relaying techniques in exchange for spectrum access. We propose an auction-based optimization problem to maximize the sum transmission rate of all primary S-IoT receivers with the appropriate secondary network selection and corresponding radio resource allocation profile by the distributed implementation while meeting the minimum transmission rate of secondary receivers of each T-IoT network. Specifically, the one-shot Vickrey-Clarke-Groves (VCG) auction is introduced to obtain the maximum social welfare, where the winner determination problem is transformed into an assignment problem and solved by the Hungarian algorithm. To further reduce the primary satellite network decision complexity, the sequential Vickrey auction is implemented by sequential fashion until all channels are auctioned. Due to incentive compatibility with those two auction mechanisms, the secondary T-IoT cluster yields the true bids of each channel, where both the nonorthogonal multiple access (NOMA) and time division multiple access (TDMA) schemes are implemented in cooperative communication. Finally, simulation results validate the effectiveness and fairness of the proposed auction-based approach as well as the superiority of the NOMA scheme in secondary relays selection. Moreover, the influence of key factors on the performance of the proposed scheme is analyzed in detail.

Conventional GEO satellite communication systems rely on a multibeam foot-print with a uniform resource allocation to provide connectivity to users. However, applying uniform resource allocation is inefficient in presence of non-uniform demand distribution. To overcome this limitation, the next generation of broadband GEO satellite systems will enable flexibility in terms of power and bandwidth assignment, enabling on-demand resource allocation. In this paper, we propose a novel satellite resource assignment design whose goal is to satisfy the beam traffic demand by making use of the minimum transmit power and utilized bandwidth. The motivation behind the proposed design is to maximize the satellite spectrum utilization by pushing the spectrum reuse to affordable limits in terms of tolerable interference. The proposed problem formulation results in a non-convex optimization structure, for which we propose an efficient tractable solution. We validate the proposed method with extensive numerical results, which demonstrate the efficiency of the proposed approach with respect to benchmark schemes.

Herein, a precoding scheme is developed for orthogonal frequency division multiplexing (OFDM) transmission in multiple-input multiple-output (MIMO) systems that use one-bit digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) at the transmitter and receiver, respectively, as a means to reduce the power consumption. Two different one-bit architectures are presented. In the first, a single user MIMO system is considered where the DACs and ADCs of the transmitter and the receiver are assumed to be one-bit and in the second, a network of analog phase shifters is added at the receiver as an additional analog-only processing step with the view to mitigate some of the effects of coarse quantization. The precoding design problem is formulated and then split into two NP-hard optimization problems, which are solved by an algorithmic solution based on the Cyclic Coordinate Descent (CCD) framework. The design of the analog post-coding matrix for the second architecture is decoupled from the precoding design and is solved by an algorithm based on the alternating direction method of multipliers (ADMM). Numerical results show that the proposed precoding scheme successfully mitigates the effects of coarse quantization and the proposed systems achieve a performance close to that of systems equipped with full resolution DACs/ADCs.

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with$M$RF chains and$N$antennas, where$M < N$. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of$M$antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.

One of the most dominant applications of Ultra Reliable Low Latency Communication of 5G-NR is V2X communication. For such latency-critical V2X communication, the distributed resource allocation using Semi-Persistent Scheduling (SPS) algorithm designed for out-of-coverage (Mode-4) scenario leads to a high collision probability and requires profuse sensing processes. Therefore, a need for more efficient distributed resource allocation scheme is compelled. In this paper, we investigate the 3GPP proposed SPS scheduling algorithm for its performance and formulate the problem under partial sensing systems. Furthermore, we propose a Foreseeing Semi-Persistent Scheduling (F-SPS) algorithm as an enhancement to the existing methodology, and conclusively, simulations are presented to illustrate the improved performance of the proposed F-SPS scheme in terms of reduced collision probability with an optimised number of sensing processes.

A collaborative task is assigned to a multiagent system (MAS) in which agents are allowed to communicate. The MAS runs over an underlying Markov decision process and its task is to maximize the averaged sum of discounted one-stage rewards. Although knowing the global state of the environment is necessary for the optimal action selection of the MAS, agents are limited to individual observations. The inter-agent communication can tackle the issue of local observability, however, the limited rate of the inter-agent communication prevents the agents from acquiring the precise global state information. To overcome this challenge, agents need to communicate their observations in a compact way such that the MAS compromises the minimum possible sum of rewards. We show that this problem is equivalent to a form of rate-distortion problem which we call the task-based information compression. State Aggregation for Information Compression (SAIC) is introduced here to perform the task-based information compression. The SAIC is shown, conditionally, to be capable of achieving the optimal performance in terms of the attained sum of discounted rewards. The proposed algorithm is applied to a rendezvous problem and its performance is compared with two benchmarks; (i) conventional source coding algorithms and the (ii) centralized multiagent control using reinforcement learning. Numerical experiments confirm the superiority and fast convergence of the proposed SAIC.

Constant Envelope (CE) precoding is an efficient technique for systems based on massive antenna arrays since the constant amplitude of the transmit signal facilitates the use of power efficient non-linear transmitter circuitry, such as power amplifiers (PAs). On the other hand, Orthogonal frequency-division multiplexing (OFDM) is a well-known multicarrier transmission scheme which is used to mitigate the effects of multipath propagation, but usually leads to high peak-to-average-power ratio (PAPR). Herein, the problem of CE MIMO-OFDM precoding for transmission over frequency selective channels is tackled. First, a novel efficient formulation is proposed, where the precoding problem is formulated as an unconstrained nonlinear least-squares problem. Next, using the new formulation the problem is solved using the Gauss-Newton algorithm. Numerical results show that the proposed solution is more efficient than the current state of the art techniques both from the aspect of computational complexity and the overall system performance.

This paper tackles the problem of both multi-user and intersymbol interference stemming from co-channel users transmitting at a faster- than-Nyquist (FTN) rate in multi-antenna downlink transmissions. We propose a framework for redundant block-based symbol-level precoders enabling the trade-off between constructive and destructive multi-user and interblock interference (IBI) effects at the singleantenna user terminals. Redundant elements are added as guard interval to handle IBI destructive effects. It is shown that, within this framework, accelerating the transmissions via FTN signaling improves the error-free spectral efficiency, up to a certain acceleration factor beyond which the transmitted information cannot be perfectly recovered by linear filtering followed by sampling. Simulation results corroborate that the proposed spatiotemporal symbol-level precoding can change the amount of added redundancy from zero (full IBI) to half (IBI-free) the equivalent channel order, so as to achieve a target balance between spectral and energy efficiencies.

Carrier Aggregation is one of the essential approaches to achieve several orders of magnitude increase in peak data rates. While carrier aggregation benefits have been extensively studied in terrestrial wireless systems, its application to satellite has not been substantially explored. Carrier aggregation can be a prominent solution to address the issue of the spatially-heterogeneous satellite data traffic demand. This paper studies introducing carrier aggregation into satellite systems from a link layer perspective. The proposed modifications at the link layer have been carefully designed to make carrier aggregation transparent to the other layers. However, deployment of carrier aggregation in satellite systems with the combination of multiple carriers that have different characteristics requires effective scheduling schemes for reliable communications. Since channel awareness is indispensable for any efficient resource allocation schemes, we have proposed a perceptive scheduling algorithm that takes into account channel properties along with the instantaneous available resources to ensure that the received data packets are delivered without perturbing the original transmission order. Simulation results are given to validate our analysis and demonstrate the design tradeoffs, and thus, our results provide useful insights to practical scheduler design.

During the last few years, the interest around Machine-Type Communications (MTC) and Internet of Things (IoT) is growing exponentially. The Third Generation Partnership Project (3GPP), acknowledging the importance of this trend, has introduced a number of key features to support IoT. Specifically they provided progressively improved support for Low Power Wide Area Network (LPWAN) introducing the so called Narrowband IoT (NB-IoT) in 2017 for better serving IoT use cases. Furthermore, the integration of Non Terrestrial Networks (NTN) in the New Radio (NR) is playing an important role, enabling many different use cases for the forthcoming 5G system. This work aims at analyzing the feasibility of integrating NB- IoT with satellite communication systems; the main issues are investigated with a particular focus on the impact of doppler shift on NB-IoT waveform. Possible solutions are proposed, empowering the utilization of the NB-IoT terrestrial standard within satellite channel.

We propose a novel technique for total transmit power minimization and optimal precoder design in wireless multi-group (MG) multicasting (MC) systems. The considered framework consists of three different systems capable of handling heterogeneous user types viz., information decoding (ID) specific users with conventional receiver architectures, energy harvesting (EH) only users with non-linear EH module, and users with joint ID and EH capabilities having separate units for the two operations, respectively. Each user is categorized under unique group(s), which can be of MC type specifically meant for ID users, and/or an energy group consisting of EH explicit users. The joint ID and EH users are a part of the (last) EH group as well as any one of the MC groups distinctly. In this regard, we formulate an optimization problem to minimize the total transmit power with optimal precoder designs for the three aforementioned scenarios, under constraints on minimum signal-to-interference-plus-noise ratio and harvested energy by the users with respective demands. The problem may be adapted to the well-known semi-definite program, which can be typically solved via relaxation of rank-l constraint. However, the relaxation of this constraint may in some cases lead to performance degradation, which increases with the rank of the solution obtained from the relaxed problem. Hence, we develop a novel technique motivated by the feasible-point pursuit and successive convex approximation method in order to address the rank-related issue. The benefits of the proposed method are illustrated under various operating conditions and parameter values, with comparison between the three above-mentioned scenarios.

This paper investigates the combined benefits of full-duplex (FD) and cell-free massive multiple-input multiple-output (CF-mMIMO), where a large number of distributed access points (APs) having FD capability simultaneously serve numerous uplink and downlink user equipments (UEs) on the same time-frequency resources. To enable the incorporation of FD technology in CF-mMIMO systems, we propose a novel heap-based pilot assignment algorithm, which not only can mitigate the effects of pilot contamination but also reduce the involved computational complexity. Then, we formulate a robust design problem for spectral efficiency (SE) maximization in which the power control and AP-UE association are jointly optimized, resulting in a difficult mixed-integer nonconvex programming. To solve this problem, we derive a more tractable problem before developing a very simple iterative algorithm based on inner approximation method with polynomial computational complexity. Numerical results show that our proposed methods with realistic parameters significantly outperform the existing approaches in terms of the quality of channel estimate and SE.

Cell-free massive multiple-input multiple-output (CF-MIMO) is a promising technological enabler for fifth generation (5G) networks in which a large number of access points (APs) jointly serve the users. Each AP applies conjugate beamforming to precode data, which is based only on the AP’s local channel state information. However, by having the nature of a (very) large number of APs, the operation of CF-MIMO can be energy inefficient. In this paper, we investigate the energy efficiency performance of CF-MIMO by considering a practical energy consumption model which includes both the signal transmit energy as well as the static energy consumed by hardware components. In particular, a joint power allocation and AP selection design is proposed to minimize the total energy consumption subject to given quality of service (QoS) constraints. In order to deal with the combinatorial complexity of the formulated problem, we employ norm $l_{2,1}-$based block-sparsity and successive convex optimization to leverage the AP selection process. Numerical results show significant energy savings obtained by the proposed design, compared to all-active APs scheme and the large-scale based AP selection.

Remote Sensing from distributed platforms has become attractive for the community in the last years. Phase, frequency, and time synchronization are a crucial requirement for many such applications as multi-static remote sensing and also for distributed beamforming for communications. The literature on the field is extensive, and in some cases, the requirements an complexity of the proposed synchronization solution may surpass the ones set by the application itself. Moreover, the synchronization solution becomes even more challenging when the nodes are flying or hovering on aerial or space platforms. In this work, we discuss the synchronization considerations for the implementation of distributed remote sensing applications. The general framework considered is based on a distributed collection of autonomous nodes that synchronize their clocks with a common reference using inter-satellite links. For this purpose, we implement a synchronization link between two nodes operating in a full-duplex fashion. The experimental testbed uses commercially available SDR platforms to emulate two satellites, two targets, and the communication channel. The proposal is evaluated considering phase and frequency errors for different system parameters.

Current multi-beam GEO satellite systems operate under a limited frequency reuse configuration and considering uniform power assignment across beams. The latter has been shown to be inefficient in matching the geographic distribution of the traffic demand. In this context, next generation of broadband GEO satellite systems will be equipped with more flexible and reconfigurable payloads, facilitating on-demand resource allocation. In this paper, we consider both carrier and power assignment to match the requested beam demands while minimizing the total transmit power and the total utilized bandwidth. A novel optimization problem is formulated and, given its non-convex structure, we divide the problem into two tractable sub-problems. First, we estimate the number of adjacent frequency carriers required for each beam to satisfy its demand and, subsequently, we optimize the power allocation based on the previously assigned carriers. We validate the proposed method with extensive numerical results, which demonstrate its efficiency with respect to benchmark strategies.

Edge caching is a promising technology to face the stringent latency requirements and back-haul traffic overloading in 5G wireless networks. However, acquiring the contents and modeling the optimal cache strategy is a challenging task. In this work, we use an active learning approach to learn the content popularities since it allows the system to leverage the trade-off between exploration and exploitation. Exploration refers to caching new files whereas exploitation use known files to cache, to achieve a good cache hit ratio. In this paper, we mainly focus to learn popularities as fast as possible while guaranteeing an operational cache hit ratio constraint. The effectiveness of proposed learning and caching policies are demonstrated via simulation results as a function of variance, cache hit ratio and used storage.

Ultrareliable uplink communication based on random access poses novel research challenges for the receiver design. Here, the uncertainty imposed by the random access and a large amount of interfering transmissions is the limiting factor for the system performance. Recently, this type of communication has been addressed in context of simultaneous wireless information and power transfer (SWIPT). The need to adapt the power splitting to the signal states according to the underlying random access has been tackled by introducing a predictor, which determines the valid states of the received signal based on the long-term observation. Hence, the power splitting factor is scaled accordingly in order to guarantee ultrareliable communication and maximized harvested energy.In this work, we extend the considered SWIPT scenario by introducing multiple antennas at the receiver side. Through this, the received energy can be substantially increased, if the energy harvesting parameters and the spatial filter coefficients are jointly optimized. Hence, we propose an optimization procedure, which aims at maximizing the harvested energy under the ultrareliability constraint. The mentioned prediction method is then combined with the optimization solution and the resulting system performance is numerically evaluated.

In this paper, we aim to improve the performance of proactive caching policies by presenting an accurate content request prediction algorithm. We develop a Bayesian dynamical model through which a latent temporal trend structure in the content request can be accurately tracked and predicted. The dynamical model also leverages tensor train decomposition to capture content-location interactions to further enhance the accuracy of predictions. We derive an approximation of the posterior distribution based on variational Bayes (VB) and Kalman smoother algorithms to infer the model’s parameters. Moreover, using a real-world dataset, we examine the impact of prediction accuracy of our proposed scheme on a designed cooperative caching policy. The numerical results show that our algorithm substantially outperforms reference methods which ignore the temporal trends and content-location interactions.

Despite the deleterious effect of hardware impairments (HWIs) on wireless systems, most prior works in cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems have not accounted for their impact. In particular, the effect of phase noise (PN) has not been investigated at all in CF systems. Moreover, there is no work investigating HWIs in scalable CF (SCF) mMIMO systems, encountering the prohibitively demanding fronthaul requirements of large networks with many users. Hence, we derive the uplink spectral efficiency (SE) under HWIs with minimum mean-squared error (MMSE) combining in closed-form by means of the deterministic equivalent (DE) analysis. Notably, previous works, accounted for MMSE decoding, studied the corresponding SE only by means of simulations. Numerical results illustrate the performance loss due to HWIs and result in insightful conclusions.

The increasing demand for green wireless communications and the benefits of the promising cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems towards their optimal energy efficiency (EE) are the focal points of this work. Specifically, despite previous works assuming a uniform placement for the access points (APs), we consider that their locations follow a Poisson point process (PPP) which approaches their opportunistic spatial randomness. Based on stochastic geometry, we derive a lower bound on the average spectral efficiency, and under a realistic power consumption model for CF mMIMO systems, we formulate an EE maximization problem achieving to obtain in closed form the optimal EE per unit area in terms of the pilot reuse factor and the AP density. Note that we have defined the EE per unit area and not just the EE to characterize the energy in systems with multi-point transmission. Thus, we provide important design insights for energy-efficient CF mMIMO systems.

Integrating massive multiple-input multiple-output (MIMO) into satellite network is regarded as an effective strategy to improve the spectral efficiency as well as the coverage of satellite communication. However, the inevitable intra-system and inter-system interference deteriorate the total performance of system. In this paper, we consider precoding in the 5G Satellite Integrated Network (5GSIN) with the deployment of Massive MIMO and propagation of shared millimeter-wave (mmWave) link. Taking the requirements of both frequency efficiency and energy assumption into account, a hybrid analog and digital pre-coding scheme in the specific scenario of 5GSIN is proposed. We model sum rate maximization problem for both of satellite and terrestrial system that incorporates maximum power constrains and minimum achievable rate requirements and formulate to a convex power allocation problem with Minimum Mean Square Error (MMSE) norm and Logarithmic Linearization method. In order to balance between performance and complexity, we propose an analog and digital separated hybrid precoding algorithm to mitigate intra-system interference. Moreover, an iterative power allocation with interference mitigation algorithm is also devised to mitigate interference from satellite to terrestrial link so that power allocation can be executed by generalized iterative algorithm. Simulation results show that our proposed hybrid precoding algorithm in 5GSIN can improve the overall spectral efficiency with a small amount of iterations.

Data-driven approaches, e.g., deep learning (DL),have been widely studied in terrestrial wireless communications fields, proving the benefits and potentials of such techniques. In comparison, DL for satellite networks is studied to a limited extent in the literature. In this paper, we develop a DL assisted approach to facilitate efficient beam hopping (BH) in multibeam satellite systems. BH is adopted to provide a high level of flexibility to manage irregular and time variant traffic requests in the satellite coverage area. Conventional iterative optimization approaches and typical data-driven techniques may have their respective limitations in achieving timely and satisfactory performance. We herein explore a combined learning-and-optimization approach to provide a fast, feasible, and near-optimal solution for BH scheduling. Numerical study shows that in the proposed solution, the learning component is able to largely accelerate the procedure of BH pattern selection and allocation, while the optimization component can guarantee the solution's feasibility and improve the overall performance.

In this paper, we investigate the performance of simultaneous wireless information and power transfer (SWIPT) multiuser systems, in which a base station serves a set of users with both information and energy simultaneously via a power splitting (PS) mechanism. To capture realistic scenarios, a nonlinear energy harvesting (EH) model is considered. In particular, we jointly design the PS factors and the beamforming vectors in order to maximize the total harvested energy, subjected to rate requirements and a total transmit power budget. To deal with the inherent non-convexity of the formulated problem, an iterative optimization algorithm is proposed based on the inner approximation method and semide-finite relaxation (SDR), whose convergence is theoretically guaranteed. Numerical results show that the proposed scheme significantly outperforms the baseline max-min based SWIPT multicast and fixed-power PS designs.

Aerial base stations (BSs) attached to unmanned aerial vehicles (UAVs) constitute a new paradigm for next-generation cellular communications. However, the flight range and communication capacity of aerial BSs are usually limited due to the UAVs' size, weight, and power (SWAP) constraints. To address this challenge, in this paper, we consider dynamic cooperative transmission among multiple aerial BSs for power-efficient aerial communications. Thereby, a central controller intelligently selects the aerial BSs navigating in the air for cooperation. Consequently, the large virtual array of moving antennas formed by the cooperating aerial BSs can be exploited for low-power information transmission and navigation, taking into account the channel conditions, energy availability, and user demands. Considering both the fronthauling and the data transmission links, we jointly optimize the trajectories, cooperation decisions, and transmit beamformers of the aerial BSs for minimization of the weighted sum of the power consumptions required by all BSs. Since obtaining the global optimal solution of the formulated problem is difficult, we propose a low-complexity iterative algorithm that can efficiently find a Karush-Kuhn-Tucker (KKT) solution to the problem. Simulation results show that, compared with several baseline schemes, dynamic multi-UAV cooperation can significantly reduce the communication and navigation powers of the UAVs to overcome the SWAP limitations, while requiring only a small increase of the transmit power over the fronthauling links.

In-band full-duplex (FD) operation is practically more suited for short-range communications such as WiFi and small-cell networks, due to its current practical limitations on the self-interference cancellation. In addition, cell-free massive multiple-input multiple-output (CF-mMIMO) is a new and scalable version of MIMO networks, which is designed to bring service antennas closer to end user equipments (UEs). To achieve higher spectral and energy efficiencies (SE-EE) of a wireless network, it is of practical interest to incorporate FD capability into CF-mMIMO systems to utilize their combined benefits. We formulate a novel and comprehensive optimization problem for the maximization of SE and EE in which power control, access point-UE (AP-UE) association and AP selection are jointly optimized under a realistic power consumption model, resulting in a difficult class of mixed-integer nonconvex programming. To tackle the binary nature of the formulated problem, we propose an efficient approach by exploiting a strong coupling between binary and continuous variables, leading to a more tractable problem. In this regard, two low-complexity transmission designs based on zero-forcing (ZF) are proposed. Combining tools from inner approximation framework and Dinkelbach method, we develop simple iterative algorithms with polynomial computational complexity in each iteration and strong theoretical performance guaranteed. Furthermore, towards a robust design for FD CF-mMIMO, a novel heap-based pilot assignment algorithm is proposed to mitigate effects of pilot contamination. Numerical results show that our proposed designs with realistic parameters significantly outperform the well-known approaches (i.e., small-cell and collocated mMIMO) in terms of the SE and EE. Notably, the proposed ZF designs require much less execution time than the simple maximum ratio transmission/combining.

Herein, we focus on an end-to-end design of a proactive cooperative caching strategy for a multi-cell network. The design is challenging as it involves two interrelated problems: the ability to predict future content popularity and to meet network operation characteristics. To this end, we first formulate a cooperative content caching in order to optimize the aggregated network cost for delivering contents to users. An efficient proactive caching policy requires an accurate prediction of time-varying content popularity. Content popularity has temporal and spatial dependencies and therefore, we develop a probabilistic dynamical model for content popularity prediction by exploiting its spatiotemporal correlations. To achieve an accurate tracking and prediction of content popularity evolution, the proposed dynamical model is non-linear and incorporates non-Gaussian distributions. We use Variational Bayes (VB) approach for estimating the model parameters. The VB provides mathematical tractability. We then develop an online VB method that works with streaming data where content request arrives sequentially. Using extensive simulations study on a real-world dataset, we show that our online VB based dynamical model provides improved performance compared to conventional content caching policies.

The average performance of the MISO downlink channel, with a large number of users compared to transmit antennas of the base station, depends on the interference management which necessitates the joint design of scheduling and precoding. Unlike the previous works which do not offer a truly joint design, this paper focuses on formulating a problem amenable for the joint update of scheduling and precoding. Novel optimization formulations are investigated to reveal the hidden difference of convex/ concave structure for three classical criteria (weighted sum rate, max-min signal-to-interference plus noise ratio, and power minimization) and associated constraints are considered. Thereafter, we propose a convex-concave procedure framework based iterative algorithm where scheduling and precoding variables are updated jointly in each iteration. Finally, we show the superiority in performance of joint solution over the state-of-the-art designs through Monte-Carlo simulations.

Herein, we consider constant envelope precoding in a multiple-input multiple-output orthogonal frequency division multiplexing system (CE MIMO-OFDM) for frequency selective channels. In CE precoding the signals for each transmit antenna are designed to have constant amplitude regardless of the channel realization and the information symbols that must be conveyed to the users. This facilitates the use of power-efficient components, such as phase shifters (PS) and nonlinear power amplifiers, which are key for the feasibility of large-scale antenna array systems because of their low cost and power consumption. The CE precoding problem is firstly formulated as a least-squares problem with a unit modulus constraint and solved using an algorithm based on coordinate descent. The large number of optimization variables in the case of the MIMO-OFDM system motivates the search for a more computationally efficient solution. To tackle this, we reformulate the CE precoding design into an unconstrained nonlinear least-squares problem, which is solved efficiently using the Gauss-Newton algorithm. Simulation results underline the efficiency of the proposed solutions and show that they outperform state of the art techniques.

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to co-channel interference. One possibility to address this issue is the use of orthogonal multiple access (OMA) techniques. However, due to a potentially very long duty cycle, OMA is not well suited for such schemes. Instead, random medium access (RMA) appears more promising. An RMA scheme is based on transmission of short data packets with random scheduling, which is typically unknown to the receiver. The received signal, which consists of the overlapping packets, can be used for energy harvesting and powering of a relay device. Such an energy harvesting relay may utilize the energy for further information processing and uplink transmission. In this paper, we address the design of a simultaneous information and power transfer scheme based on randomly scheduled packet transmissions and reliable symbol detection. We formulate a prediction problem with the goal to maximize the harvested power for an RMA scenario. In order to solve this problem, we propose a new prediction method, which shows a significant performance improvement compared to the straightforward baseline scheme. Furthermore, we investigate the complexity of the proposed method and its vulnerability to imperfect channel state information.

Mobile edge caching (MEC) has received much attention as a promising technique to overcome the stringent latency and data hungry requirements in future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit in the same time/frequency block simultaneously. In this paper, we investigate the delivery time performance of full-duplex enabled MEC (FD-MEC) systems, in which the users are served by distributed edge nodes (ENs), which operate in FD mode and are equipped with a limited storage memory. Firstly, we analyse the FD-MEC with different levels of cooperation among the ENs and take into account a realistic model of self-interference cancellation. Secondly, we propose a framework to minimize the system delivery time of FD-MEC under both linear and optimal precoding designs. Thirdly, to deal with the non-convexity of the formulated problems, two iterative optimization algorithms are proposed based on the inner approximation method, whose convergence is analytically guaranteed. Finally, the effectiveness of the proposed designs are demonstrated via extensive numerical results. It is shown that the cooperative scheme mitigates inter-user and self interference significantly better than the distributed scheme at an expense of inter-EN cooperation. In addition, we show that minimum mean square error (MMSE)-based precoding design achieves the best performance-complexity trade-off, compared with the zero-forcing and optimal designs.

The efficiency of cache-placement algorithms in edge-caching networks depends on the accuracy of the content request’s statistical model and the estimation method based on the postulated model. This paper studies these two important issues. First, we introduce a new model for content requests in stationary environments. The common approach to model the requests is through the Poisson stochastic process. However, the Poisson stochastic process is not a very flexible model since it cannot capture the correlations between contents. To resolve this limitation, we instead introduce the Poisson Factor Analysis (PFA) model for this purpose. In PFA, the correlations are modeled through additional random variables embedded in a low dimensional latent space. The correlations provide rich information about the underlying statistical properties of content requests which can be used for advanced cache-placement algorithms. Secondly, to learn the model, we use Bayesian Learning, an efficient framework which does not overfit. This is crucial in edge-caching systems since only partial view of the entire request set is available at the local cache and the learning method should be able to estimate the content popularities without overfitting. In the simulation results, we compare the performance of our approach with the existing popularity estimation method.

In a mmWave multiple-input multiple-output (MIMO) communication system employing a large-scale antenna array (LSAA), the hybrid transceivers are used to reduce the power consumption and the hardware cost. In a hybrid analog-digital (A/D) transceiver, the pre/post-processing operation splits into a lower-dimensional baseband (BB) pre/postcoder, followed by a network of analog phase shifters. Primarily two kinds of hybrid architectures are proposed in the literature to implement hybrid transceivers namely, the fully- connected and the partially-connected. Implementation of fully-connected architecture has higher hardware complexity, cost and power consumption in comparison with partially- connected. In this paper, we focus on partially- connected hybrid architecture and develop a low- complexity algorithm for transceiver design for a single user point-to-point mmWave MIMO system. The proposed algorithm utilizes the variable elimination (projection) and the minorization- maximization (MM) frameworks and has convergence guarantees to a stationary point. Simulation results demonstrate that the proposed algorithm is easily scalable for LSAA systems and achieves significantly improved performance in terms of the spectral efficiency (SE) of the system compared to the state-of-the-art solution.

Recent satellite standards enforce the coding of multiple users’ data in a frame. This transmission strategy mimics the well-known physical layer multigroup multicasting (MGMC). However, typical beam coverage with a large number of users and limited frame length lead to the scheduling of only a few users. Moreover, in emerging aggressive frequency reuse systems, scheduling is coupled with precoding. This is addressed in this work, through the joint design of scheduling and precoding for frame-based MGMC satellite systems. This aim is formulated as the maximization of the sum- rate under per beam power constraint and minimum SINR requirement of scheduled users. Further, a framework is proposed to transform the non-smooth SR objective with integer scheduling and nonconvex SINR constraints as a difference- of-convex problem that facilitates the joint update of scheduling and precoding. Therein, an efficient convex-concave procedure based algorithm is proposed. Finally, the gains (up to 50%) obtained by the jointed design over state- of-the-art methods is shown through Monte-Carlo simulations.

This paper designs dynamic onboard signal processing schemes in a multiple gateway multi-beam satellite system where full frequency reuse pattern is considered among the beams and feeds. In particular, we deploy on-board Joint Precoding, Feed selection and Signal switching mechanism (JPFS) so that the following advantages are realized, I) No need of Channel State Information (CSI) exchange among the gateways and satellite, since the performance of precoding is highly sensitive to the quality of CSI, II) In case one gateway fails, rerouting signals through other gateways can be applied without any extra signal processing, III) Properly selecting on-board feed/s to serve each user which generates maximum gain toward corresponding user, IV) Flexibly switching the signals received from the gateways to requested users where each user can dynamically request traffic from any gateway, and V) Multiple users with multiple traffic streams can be dynamically served at each beam. However, deploying such JPFS architecture imposes high complexity to the satellite payload. To tackle this issue, this study aims at deploying JPFS that can provide affordable complexity at the payload. In addition, while increasing the data demand imposes extensive bandwidth resources requirement in the feeder link, the proposed JPFS design works efficiently with available feeder link resources even if the data demand increases. The proposed design is evaluated with a close-to-real beam pattern and the latest broadband communication standard for satellite communications.

Carrier Aggregation (CA) is an integral part of current terrestrial networks. Its ability to enhance the peak data rate, to efficiently utilize the limited available spectrum resources and to satisfy the demand for data-hungry applications has drawn large attention from different wireless network communities. Given the benefits of CA in the terrestrial wireless environment, it is of great interest to analyze and evaluate the potential impact of CA in the satellite domain. In this paper, we study CA in multibeam high throughput satellite systems. We consider both inter-transponder and intra-transponder CA at the satellite payload level of the communication stack, and we address the problem of carrier-user assignment assuming that multiple users can be multiplexed in each carrier. The transmission parameters of different carriers are generated considering the transmission characteristics of carriers in different transponders. In particular, we propose a flexible carrier allocation approach for a CA-enabled multibeam satellite system targeting a proportionally fair user demand satisfaction. Simulation results and analysis shed some light on this rather unexplored scenario and demonstrate the feasibility of the CA in satellite communication systems.

Beam-Hopping (BH) and precoding are two trending technologies for the satellite community. While BH enables flexibility to adapt the offered capacity to the heterogeneous demand, precoding aims at boosting the spectral efficiency. In this paper, we consider a high throughput satellite (HTS) system that employs BH in conjunction with precoding. In particular, we propose the concept of Cluster-Hopping (CH) that seamlessly combines the BH and precoding paradigms and utilize their individual competencies. The cluster is defined as a set of adjacent beams that are simultaneously illuminated. In addition, we propose an efficient time-space illumination pattern design, where we determine the set of clusters that can be illuminated simultaneously at each hopping event along with the illumination duration. We model the CH time-space illumination pattern design as an integer programming problem which can be efficiently solved. Supporting results based on numerical simulations are provided which validate the effectiveness of the proposed CH concept and time-space illumination pattern design.

Most physical-layer security (PLS) works employ information theoretic metrics for performance analysis. In this paper, however, we investigate PLS from a signal processing point of view, where we rely on bit-error rate (BER) at the eavesdropper (Eve) as a metric for information leakage. Recently, symbol-level precoding (SLP) has been shown to enhance PLS in the presence of an Eve. In this work, nonetheless, we introduce a machine learning (ML) based attack to which even SLP schemes can be vulnerable. Namely, this attack manifests when an Eve utilizes ML in order to learn the precoding pattern when precoded pilots are sent. With this ability, an Eve can decode data with favorable accuracy. As a countermeasure to this attack, we propose a novel security enhanced precoding technique. The proposed countermeasure yields high BER at the Eve, which makes symbol detection practically infeasible for the latter, thus providing physical-layer security between the base station (BS) and the users. In the numerical results, we validate both the attack and the countermeasure, and show that this gain in security can be achieved at the expense of only a small additional power consumption at the transmitter.

In this paper, we investigate the outage performance of a multiuser threshold-based decode-and-forward integrated satellite-terrestrial relay network (ISTRN) with opportunistic user scheduling, where the satellite link and terrestrial links undergo Shadowed-Rician (SR) fading and correlated Rayleigh fading, respectively. First, we propose a new probability density function (PDF) to statistically characterize the square sum of independent and identically distributed SR random variables, which is more accurate and concise than existing expressions. Next, based on the new PDF, we derive an analytical expression of outage probability (OP) of the considered network. Furthermore, the asymptotic OP expression is developed at high signal-to-noise ratio to reveal the achievable diversity order of the considered ISTRN. Finally, computer simulation is conducted to validate the analytical results, and show the impact of various parameters on the outage performance.

In this paper we propose a new measurement procedure for the amplitude response of a wideband transponder in an in-orbit testing (IOT) context. The proposed procedure resorts to a direct sequence spread spectrum (DSSS) technique in order to limit the interference caused by the measurement signals to the traffic. Further, an adaptive approach exploiting the knowledge of the inband traffic power has been introduced. Such knowledge, acquired either a priori or by sensing the channel, allows to easily design measurement signals satisfying multiple performance requirements (i.e., accuracy, resolution, interference, duration, and complexity). The proposed procedure can be used as a design tool to optimize the dimensioning of the IOT system parameters, and to flexibly trade off a performance indicator against another.

Phase, frequency and time synchronization is a crucial requirement for many applications as such as multi-static remote sensing and distributed beamforming for communications. The literature on the field is very wide, and in some cases, the requirements of the proposed synchronization solution may surpass the ones set by the application itself. Moreover, the synchronization solution becomes even more challenging when the nodes are flying or hovering on aerial or space platforms. In this work, we compare and classify the synchronization technologies available in the literature according to a common proposed framework, and we discuss the considerations of an implementation for distributed remote sensing applications. The general framework considered is based on a distributed collection of autonomous nodes that try to synchronize their clocks with a common reference. Moreover, they can be classified in non-overlapping, adjacent and overlapping frequency band scenarios.

In a content-delivery network, files’ popularity and users’ requests change fast. Conventional caching schemes, e.g., caching (re)placement once per day during the off-peak hours, may not capture the up-to-date popularity. In this case, the contents in caches have to be regularly updated to prevent information becoming outdated, and at the same time users’ requested files must be delivered. These two tasks are challenging in practical heavy-traffic and multi-user scenarios when the network resources are limited. In this paper, we apply non-orthogonal multiple access (NOMA) to facilitate concurrent caching replacement and content delivery in downlink transmission. We formulate a resource allocation problem to investigate how to efficiently push proactive files to the cache at the small base station and deliver the requested files to users. The resource-allocation problem is formulated as a mixed-integer exponential conic optimization problem. To enable a computationally-efficient optimal solution with finite convergence, we develop an iterative algorithm based on polyhedral outer approximation, where a polyhedral relaxation subproblem and a convex subproblem are constructed and iteratively solved to tighten the lower and upper bounds for the optimum, respectively. The numerical results demonstrate significant performance gains of the NOMA-enabled data transmission scheme in power and resource savings compared to the baseline scheme.

Consider a collaborative task carried out by two autonomous agents that can communicate over a noisy channel. Each agent is only aware of its own state, while the accomplishment of the task depends on the value of the joint state of both agents. As an example, both agents must simultaneously reach a certain location of the environment, while only being aware of their own positions. Assuming the presence of feedback in the form of a common reward to the agents, a conventional approach would apply separately: (i) an off-the-shelf coding and decoding scheme in order to enhance the reliability of the communication of the state of one agent to the other; and (ii) a standard multiagent reinforcement learning strategy to learn how to act in the resulting environment. In this work, it is argued that the performance of the collaborative task can be improved if the agents learn how to jointly communicate and act. In particular, numerical results for a baseline grid world example demonstrate that the jointly learned policy carries out compression and unequal error protection by leveraging information about the action policy.

In a multi-beam satellite communication system, traffic requests are typically asymmetric across beams and highly heterogeneous among terminals. In practical operations, it is important to achieve a good match between the offered and requested traffic, i.e., to improve the performance of Offered Capacity to requested Traffic Ratio (OCTR). Due to satellites’ payload constraints and limited flexibilities, it is a challenging task for resource optimization. In this paper, we tackle this issue by formulating a max-min resource allocation problem, taking fairness into account such that the lowest OCTR can be maximized. To exploit the potential synergies, we introduce Non-Orthogonal Multiple Access (NOMA) to enable aggressive frequency reuse and mitigate intra-beam interference. Although NOMA has proven its capabilities in improving throughput and fairness in 5G terrestrial networks, for multi-beam satellite systems it is unclear if NOMA can help to enhance the OCTR performance, and hence is worth quantifying how much gain it can bring. To solve the problem, we design a suboptimal algorithm to firstly decompose the original problem into multiple convex subproblems by fixing power allocation for each beam, and secondly adjust beam power to improve the minimum OCTR in iterations. Numerical results show the convergence of the proposed algorithm and the superiority of the proposed NOMA scheme in max-min OCTR.

We propose a novel formulation for joint maximization of total weighted sum-spectral efficiency and weighted sum-harvested energy to study Simultaneous Wireless Information and Power Transfer (SWIPT) from a pricing perspective. Specifically, we consider that a transmit source communicates with multiple destinations using Orthogonal Frequency Division Multiplexing (OFDM) system within a dual-hop relay-assisted network, where the destination nodes are capable of jointly decoding information and harvesting energy from the same radiofrequency (RF) signal using either the time-switching (TS) or power-splitting (PS) based SWIPT receiver architectures. Computation of the optimal solution for the aforementioned problem is an extremely challenging task as joint optimization of several network resources introduce intractability at high numeric values of relays, destination nodes and OFDM sub-carriers. Therefore, we present a suitable algorithm with sub-optimal results and good performance to compute the performance of joint data processing and harvesting energy under fixed pricing methods by adjusting the respective weight factors, motivated by practical statistics. Furthermore, by exploiting the binary options of the weights, we show that the proposed formulation can be regulated purely as a sum-spectral efficiency maximization or solely as a sum-harvested energy maximization problem. Numerical results illustrate the benefits of the proposed design under several operating conditions and parameter values.

This paper presents designing precoding technique at the gateway of a multibeam mobile satellite systems, enabling full frequency reuse pattern among the beams. Such a system brings in two critical challenges to overcome. The inter-beam interference makes applying interference mitigation techniques necessary. Further, when the user terminals are mobile the Channel State Information (CSI) becomes time-varying which is another challenge to overcome. Therefore, the gateway has only access to an outdated CSI, which can eventually limit the precoding gains. In this way, employing a proper CSI estimation mechanism at the gateway can improve the performance of the precoding scheme. In this context, the objectives of this paper are two folds. First, we present different CSI feedback mechanisms which aim at preserving a lower CSI variations at the gateway. Then, we develop the corresponding precoding schemes which are adapted with the proposed CSI feedback mechanisms. To keep the complexity of the proposed precoding schemes affordable, we consider a maritime communication scenario so that the signals received by mobile user terminals suffer from a lower pathloss compared to the Land Mobile communication. Finally, we provide several simulations results in order to evaluate the performance of the proposed precoding techniques.

The optimal flow assignment is strongly dependent on the network link capacities, which in turn are determined by the allocation of the available radio resources. In this paper, we consider the holistic design of joint power and flow assignment in the context of Integrated Terrestrial-Satellite Backhaul (ITSB) networks. Aiming for an spectral efficient system, we focus on the scenario where the satellite links operate in the non- exclusive Ka band, which is shared with the terrestrial microwave backhaul links. We focus on the maximization of the network throughput considering a penalizing term to restrict the use of the satellite links in order to avoid the expensive cost of satellite bandwidth. The interference resulting from the spectrum sharing assumption makes the joint power and flow assignment a very challenging problem. We propose a convex relaxation approach which eases the formulation and allows the implementation of efficiency convex optimization tools to achieve a feasible solution to the original problem. Supporting results based on numerical simulations validate the proposed approach.

Edge-caching is recognized as an efficient technique for future wireless cellular networks to improve network capacity and user-perceived quality of experience. Due to the random content requests and the limited cache memory, designing an efficient caching policy is a challenge. To enhance the performance of caching systems, an accurate content request prediction algorithm is essential. Here, we introduce a flexible model, a Poisson regressor based on a Gaussian process, for the content request distribution in stationary environments. Our proposed model can incorporate the content features as side information for prediction enhancement. In order to learn the model parameters, which yield the Poisson rates or alternatively content popularities, we invoke the Bayesian approach which is very robust against over-fitting. However, the posterior distribution in the Bayes formula is analytically intractable to compute. To tackle this issue, we apply a Monte Carlo Markov Chain (MCMC) method to approximate the posterior distribution. Two types of predictive distributions are formulated for the requests of existing contents and for the requests of a newly-added content. Finally, simulation results are provided to confirm the accuracy of the developed content popularity learning approach.

Blockchain is a merging technology for decentralized management and data security, which was first introduced as the core technology of cryptocurrency, e.g., Bitcoin. Since the first success in financial sector, blockchain has shown great potentials in various domains, e.g., internet of things and mobile networks. In this paper, we propose a novel blockchain-based architecture for content delivery networks (B-CDN), which exploits the advances of the blockchain technology to provide a decentralized and secure platform to connect content providers (CPs) with users. On one hand, the proposed B-CDN will leverage the registration and subscription of the users to different CPs, while guaranteeing the user privacy thanks to virtual identity provided by the blockchain network. On the other hand, the B-CDN creates a public immutable database of the requested contents (from all CPs), based on which each CP can better evaluate the user preference on its contents. The benefits of B-CDN are demonstrated via an edge-caching application, in which a feature-based caching algorithm is proposed for all CPs. The proposed caching algorithm is verified with the realistic Movielens dataset. A win-win relation between the CPs and users is observed, where the B-CDN improves user quality of experience and reduces cost of delivering content for the CPs.

Edge caching has received much attention as a promising technique to overcome the stringent latency and data hungry challenges in the future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit simultaneously. In this paper, we study a cache-aided FD system via delivery time analysis and optimization. In the considered system, an edge node (EN) operates in FD mode and serves users via wireless channels. Two optimization problems are formulated to minimize the largest delivery time based on the two popular linear beamforming zero-forcing and minimum mean square error designs. Since the formulated problems are non-convex due to the self-interference at the EN, we propose two iterative optimization algorithms based on the inner approximation method. The convergence of the proposed iterative algorithms is analytically guaranteed. Finally, the impacts of caching and the advantages of the FD system over the half-duplex (HD) counterpart are demonstrated via numerical results.

We investigate the performance of multi-user multiple-antenna downlinks via joint antenna selection and power control design. In order to fully exploit the spatial diversity while minimizing the energy consumed by active radio frequency (RF) modules, a subset of antennas are selected to serve the users. Firstly, we propose a joint antenna selection and power allocation (JASPA) algorithm to maximize the system sum rate subjected to the total transmit power constraint and quality of service (QoS) requirements. JASPA copes with the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner iteration successively optimizes the transmit power followed by an outer loop that tries all valid antenna combinations. Although approaching the global optimality, JASPA suffers a combinatorial complexity, which might limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and power allocation (L-ASPA) which significantly reduces the high computational time of JASPA while retaining comparative performance. The core idea behind L-ASPA is to exploit the advances in machine learning to establish underlaying relation between the key system parameters and the selected antennas. The effectiveness of the proposed algorithms is demonstrated via numerical results, which show that JASPA could achieve 90% of the optimal performance while reducing more than 93% computation time.

In this paper, we investigate the performance of cache-assisted simultaneous wireless information and power transfer (SWIPT) cooperative systems, in which one source communicates with one destination via the aid of multiple relays. In order to prolong the relays’ serving time, the relays are assumed to be equipped with a cache memory and energy harvesting (EH) capability. Based on the time-splitting mechanism, we analyze the effect of caching on the system performance in terms of the serving throughput and the stored energy at the relay. In particular, two optimization problems are formulated to maximize the relay-destination throughput and the energy stored at the relay subject to some quality-of-service (QoS) constraints, respectively. By using the KKT conditions and with the help of the Lambert function, closed-form solutions are obtained for the two formulated problems. In order to further improve the performance, a relay selection policy is introduced to select the best relay based on either the maximum throughput between the relays’ and destination link or maximum stored energy at the relay, for conveying information to the destination. Numerical results reveal significant benefits of incorporating caching capabilities to SWIPT systems, in terms of improved serving time, throughput, and EH performance at the relays.

In this paper, we consider three transmitter designs for symbol-level-precoding (SLP), a technique that mitigates multiuser interference (MUI) in multiuser systems by designing the transmitted signals using the channel state information and the information-bearing symbols. The considered systems tackle the high hardware complexity and power consumption of existing SLP techniques by reducing or completely eliminating fully digital radio frequency (RF) chains. The first proposed architecture referred to as, Antenna Selection SLP, minimizes the MUI by activating a subset of the available antennas and thus, reducing the number of required RF chains to the number of active antennas. In the other two architectures, which we refer to as RF domain SLP, the processing happens entirely in the RF domain, thus eliminating the need for multiple fully digital RF chains altogether. Instead, the analog phase shifters directly modulate the signals on the transmit antennas. The precoding design for all the considered cases is formulated as a constrained least squares problem and efficient algorithmic solutions are developed via the Coordinate Descent method. Simulations provide insights into the power efficiency of the proposed schemes and the improvements over the fully digital counterparts.

We investigate the resource allocation and relay selection in a two-hop relay-assisted multi-user orthogonal frequency division multiple access (OFDMA) network, where the end-nodes support the simultaneous wireless information and power transfer (SWIPT) employing a power splitting (PS) technique. Our goal is to optimize the end-nodes’ PS ratios as well as the relay, carrier, and power assignment so that the sum-rate of the system is maximized subject to harvested energy and transmitted power constraints. Such joint optimization with mixed-integer non-linear programming structure is combinatorial in nature. Due to the complexity of this problem, we propose to solve its dual problem, which guarantees asymptotic optimality and less execution time compared to a highly-complex exhaustive search approach. Furthermore, we also present a heuristic method to solve this problem with lower computational complexity. The simulation results reveal that the proposed algorithms provide significant performance gains compared to a semi-random resource allocation and relay selection approach and is close to the optimal solution when the number of OFDMA sub-carriers is sufficiently large.

Small-cell densification is a strategy enabling the offloading of users from macro base stations (MBSs), in order to alleviate their load and increase the coverage, especially, for cell-edge users. In parallel, as the network increases in density, the BS cooperation emerges as an efficient design method towards the demands for drastic improvement of the system performance against the detrimental overall interference. We, therefore, model and scrutinize a heterogeneous network (HetNet) of two tiers (macro and small cells) with multiple-antenna BSs serving a multitude of users, which differ with respect to their basic design parameters, e.g., the deployment density, the number of transmit antennas, and transmit power. In addition, the tiers are enhanced with cell association policies by introducing the concept of the association probability. Above this and motivated by the advantages of cooperation among BSs, the small base stations (SBSs) are enriched with this property in their design. The SBS cooperation allows shedding light into its impact on the cell selection rules in multi-antenna HetNets. Under these settings, software-defined networking (SDN) is introduced smoothly to play the leading role in the orchestration of the network. In particular, heavy operations such as the coordination and the cell association are undertaken by virtue of an SDN controller performing and managing efficiently the corresponding computations due to its centralized adaptability and dynamicity towards the enhancement and potential scalability of the network. In this context, we derive the coverage probability and the mean achievable rate. Not only we show the outperformance of BS cooperation over uncoordinated BSs, but we also demonstrate that the SBS cooperation enables the admittance of more users from the macro-cell BSs (MBSs). Furthermore, we show that by increasing the number of BS antennas, the system performance is improved as the metrics under study reveal. Moreover, we investigate the performance of different transmission techniques, and we identify the optimal bias in each case when SBSs cooperate. Finally, we depict that the SBS densification is beneficial until a specific density value since a further increase does not increase the coverage probability.

In this paper, we investigate resource allocation and relay selection in a dual-hop orthogonal frequency division multiplexing (OFDM)-based multi-user network where amplify-and-forward (AF) enabled relays facilitate simultaneous wireless information and power transfer (SWIPT) to the end- users. In this context, we address an optimization problem to maximize the end-users' sum-rate subjected to transmit power and harvested energy constraints. Furthermore, the problem is formulated for both time-switching (TS) and power- splitting (PS) SWIPT schemes.We aim at optimizing the users' SWIPT splitting factors as well as sub-carrier-destination assignment, sub-carrier pairing, and relay-destination coupling metrics. This kind of joint evaluation is combinatorial in nature with non-linear structure involving mixed-integer programming. In this vein, we propose a sub-optimal low complex sequential resource distribution (SRD) method to solve the aforementioned problem. The performance of the proposed SRD technique is compared with a semi- random resource allocation and relay selection approach. Simulation results reveal the benefits of the proposed design under several parameter values with various operating conditions to illustrate the efficiency of SWIPT schemes for the proposed techniques.

This paper aims to design joint precoding and onboard beamforming at a multiple gateway multibeam satellite system. Full frequency reuse pattern is considered among the beams and each gateway serves a cluster of adjacent beams such that multiple clusters are served through a set of gateways. However, two issues are required to be addressed. First, the interference in both user and feeder links is the bottleneck of the whole system and employing interference mitigation techniques is essential. Second, as the data demand increases, the ground and space segments should employ extensive bandwidth resources in the feeder link accordingly. This entails embedding an extra number of gateways aiming to support a fair balance between the increasing demand and the corresponding required feeder link resources. To tackle these problems, this paper studies the impact of employing a joint multiple gateway architecture and on-board beamforming scheme. It is shown that by properly designing the on-board beamforming scheme, the number of gateways can be kept affordable even if the data demand increases. The proposed beamforming scheme can partially mitigate the interference in the user link. While the user and feeder link channels vary over time, this paper focuses on designing fixed beamforming which is sufficiently robust to the variations in both channels, leading to keep payload complexity low. Moreover, Zero Forcing precoding technique is employed at the gateways to reject the interference in the feeder links as well as it helps the proposed fixed on-board beamforming by partially equalizing the interference in user link.

We present a convex optimization based Symbol-Level Precoding (SLP) for sum power minimization and propose the low-latency closed-form algorithm to find a heuristic solution to the optimization problem. The technique exploits constructive interference at the multi-user MIMO systems and minimizes the sum power of the transmitted precoded signal per each set of MIMO symbols. As a result, the received signals gain extra Signal-to-Noise Ratio (SNR), which improves data rate and energy efficiency of the system. We benchmark the low-complexity algorithm for solving the optimization technique against the conventional Fast Non-Negative Least Squares algorithm (NNLS). The demonstrated design of the SLP technique combined with the proposed closed-form algorithm has low computational complexity and fast processing time, which is applicable in low-latency high-throughput satellite communication systems.

Towards energy savings in large-scale nonorthogonal multiple access (NOMA) networks, we investigate power and load optimization for multi-cell and multi-carrier NOMA systems in this paper. To capture the coupling relation of mutual interference among cells, firstly, we extend a load-coupling model from orthogonal multiple access (OMA) to NOMA networks. Next, with this analytical tool, we formulate the considered optimization problem in NOMA-based load-coupled systems, where optimizing load, power, and determining decoding order are the key aspects in the optimization. Theoretically, we prove that the minimum network energy consumption can be achieved by using all the time-frequency resources in each cell to deliver users' demand. To achieve the optimal load and enable efficient power optimization, we develop a power-adjustment algorithm. Numerical results demonstrate promising energy-saving gains of NOMA over OMA in large-scale cellular networks, in particular for the high-demand and resource-limited scenarios.

Symbol-level precoding (SLP) is a promising technique which allows to constructively exploit the multi-user interference in the downlink of multiple antenna systems. Recently, this approach has also been used in the context of non-linear systems for reducing the instantaneous power imbalances among the antennas. However, previous works have not exploited SLP to improve the dynamic properties of the waveforms in the temporal dimension, which are fundamental for non-linear systems. To fill this gap, this paper proposes a novel precoding method, referred to as spatio-temporal SLP, which minimizes the peak-to-average power ratio of the transmitted waveforms both in the spatial and in the temporal dimensions, while at the same time exploiting the constructive interference effect. Numerical results are presented to highlight the enhanced performance of the proposed scheme with respect to state of the art SLP techniques, in terms of power distribution and symbol error rate over non-linear channels.

In this paper, an on-line interactive method is proposed for learning a linear classifier. This problem is studied within the Active Learning (AL) framework where the learning algorithm sequentially chooses unlabelled training samples and requests their class labels from an oracle in order to learn the classifier with the least queries to the oracle possible. Additionally' a constraint is introduced into this interactive learning process which limits the percentage of the samples from one “unwanted” class under a certain threshold. An optimal AL solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP) and its performance is demonstrated through numerical simulations.

We consider the problem of user selection for symbol-level multigroup multicasting in the downlink of multiuser MISO systems. Symbol-level precoding is a new paradigm for multiuser multiple-antenna downlink systems which aims at creating constructive interference among the simultaneous data streams. This can be enabled by designing the precoded signal of the multiantenna transmitter on a symbol level, taking into account both channel state information and data symbols. This work proposes a user selection algorithm to facilitate serving multiple groups of users, by transmitting a stream of common symbols to each group on symbol-by-symbol basis if we have large number of users. We provide numerical results to validate the proposed algorithm.

Edge-caching has received much attention as an efficient technique to reduce delivery latency and network congestion during peak-traffic times by bringing data closer to end users. In this paper, we investigate the latency performance of content delivery networks with the aid of edge-caching, in which a data centre is serving the users via a shared wireless medium. Firstly, we derive a cache placement design which minimizes the average (buffering) latency during the delivery phase. It is found that the derived placement solution differs from the conventional placement method for throughput minimization. Secondly, for a given cache placement scheme, we optimize the signal transmission in the delivery phase taking into consideration the cached content to minimize the average user latency. Particularly, two optimization problems based on zero-forcing (ZF) and minimum mean square error (MMSE) designs are formulated subject to requesting rate and transmit power constraints. To deal with the non-convexity of the MMSE problem, an iterative algorithm is proposed that approximates the non-convex constraint by its first-order approximation. Finally, numerical results are presented to demonstrate the effectiveness of the proposed designs.

This paper present On-Board Precoding (OBP) for a multiple gateway multibeam satellite system where full frequency reuse pattern is employed at both user and feeder links. By reducing the Channel State Information (CSI) round-trip delay to half, OBP offers significant benefits in the emerging multiple gateway scenario in terms of lower gateways coordination. However, two critical issues need to be addressed: (a) interference in both user and feeder links is the bottleneck of the whole system and employing interference mitigation techniques is essential, (b) clear push towards non-adaptive (fixed) payload implementation, leading to low computationally complex satellite architectures. In order to fulfill requirements (a) and (b), this paper studies the impact of employing a fixed OBP technique at the payload which is sufficiently robust to the variations in both user and feeder link channels. In addition to (a) and (b), the provided simulation results depict the performance gain obtained by our proposed OBP with respect to the conventional interference mitigation techniques in multiple gateway multibeam systems.

We investigate the performance of time switching (TS) based energy harvesting model for cache-assisted simultaneous wireless transmission of information and energy (Wi-TIE). In the considered system, a relay which is equipped with both caching and energy harvesting capabilities helps a source to convey information to a destination. First, we formulate based on the time-switching architecture an optimization problem to maximize the harvested energy, taking into consideration the cache capability and user quality of service requirement. We then solve the formulated problem to obtain closed-form solutions. Finally, we demonstrate the effectiveness of the proposed system via numerical results.

This paper aims to design joint on-ground precoding and on-board beamforming of a multiple gateway multibeam satellite system in a hybrid space-ground mode where full frequency reuse pattern is considered among the beams. In such an architecture, each gateway serves a cluster of adjacent beams such that the adjacent clusters are served through a set of gateways that are located at different geographical areas. However, such a system brings in two challenges to overcome. First, the inter-beam interference is the bottleneck of the whole system and applying interference mitigation techniques becomes necessary. Second, as the data demand increases, the ground and space segments should employ extensive bandwidth resources in the feeder link accordingly. This entails embedding an extra number of gateways aiming to support a fair balance between the increasing demand and the corresponding required feeder link resources. To solve these problems, this study investigates the impact of employing a joint multiple gateway architecture and onboard beamforming scheme. It is shown that by properly designing the on-board beamforming scheme, the number of gateways can be kept affordable even if the data demand increases. Moreover, Zero Forcing (ZF) precoding technique is considered to cope with the inter-beam interference where each gateway constructs a part of block ZF precoding matrix. The conceived designs are evaluated with a close-to-real beam pattern and the latest broadband communication standard for satellite communications.

In this paper, we Investigate the performance of cache enabled millimeter wave (mmWave) ad-hoc network, where randomly distributed nodes are supported by a cache memory. Specifically, we study the optimal caching placement at the desirable mmWave node using a network model that accounts for the uncertainties in node locations and blockages. We then characterize the average success probability of content delivery. As a desirable side effect, certain factors like the density of nodes and increased antenna gain, can significantly increase the cache hit ratio in mmWave networks. However, a trade-off between the cache hit probability and the average successful content delivery probability with respect to the density of nodes is presented.

This paper deals with the problem of precoding, scheduling, and link adaptation in next generation mobile interactive multibeam satellite systems. In contrast to the fixed satellite services, when the user terminals move across the coverage area, additional challenges appear. Due to the time varying channel, the gateway has only access to a delayed version of the channel state information (CSI), which can eventually limit the overall system performance. However, in contrast to general multiuser multiple-input multiple-output terrestrial systems, the CSI degradation in multibeam mobile applications has a very limited impact for typical fading channel and system assumptions. Under realistic conditions, the numerical results show that precoding can offer an attractive gain in the system throughput compared with the conservative frequency reuse allocations.

This letter proposes a beamforming (BF) scheme to enhance wireless information and power transfer in terrestrial cellular networks coexisting with satellite networks. By assuming that the energy receivers are the potential eavesdroppers overhearing signals intended for information receivers (IRs), we first formulate a constrained optimization problem to maximize the minimal achievable secrecy rate of the IRs subject to the constraints of energy harvest requirement, interference threshold, and transmit power budget. Through exploiting the sequential convex approximation method, we convert the original problem into a linear one with a series of linear matrix inequality and second-order cone constraints. An iterative algorithm is then proposed to obtain the BF weight vectors. Finally, simulation results demonstrate the effectiveness and superiority of the proposed scheme.

Despite the importance of Rayleigh-product multiple-input multiple-output channels and their experimental validations, there is no work investigating their performance in the presence of residual additive transceiver hardware impairments, which arise in practical scenarios. Hence, this paper focuses on the impact of these residual imperfections on the ergodic channel capacity for optimal receivers, and on the ergodic sum rates for linear minimum mean-squared-error (MMSE) receivers. Moreover, the low- and high-signal-to-noise ratio cornerstones are characterized for both types of receivers. Simple closed-form expressions are obtained that allow the extraction of interesting conclusions. For example, the minimum transmit energy per information bit for optimal and MMSE receivers is not subject to any additive impairments. In addition to the exact analysis, we also study the Rayleigh-product channels in the large system regime, and we elaborate on the behavior of the ergodic channel capacity with optimal receivers by varying the severity of the transceiver additive impairments.

The narrow-beam operation in millimeter wave (mmWave) networks minimizes the network interference leading to noise-limited networks in contrast with interference-limited ones. The medium access control (MAC) layer throughput and interference management strategies heavily depend on the noise-limited or interference-limited regime. Yet, these regimes are not considered in recent mmWave MAC layer designs, which can potentially have disastrous consequences on the communication performance. In this paper, we investigate the performance of cache-enabled MAC-based mmWave ad hoc networks, where randomly distributed nodes are supported by a cache. The ad hoc nodes are modeled as homogenous Poisson point processes. Specifically, we study the optimal content placement (or caching placement) at desirable mmWave nodes using a network model that accounts for uncertainties both in node locations and blockages. We propose a contention-based multimedia delivery protocol to avoid collisions among the concurrent transmissions. Subsequently, only the node with smallest back-off timer among its contenders is allowed to transmit. We then characterize the average success probability of content delivery. We also characterize the cache hit ratio probability, and transmission probability of this system under essential factors, such as blockages, node density, path loss, and caching parameters.

This paper deals with the problem of the interference between multiple co-channel transmissions in the downlink of a multi-antenna wireless system. In this framework, symbol-level precoding (SLP) is a promising technique which is able to constructively exploit the multi-user interference and to transform it into useful power at the receiver side. While previous works on SLP were focused on exploiting the multi-user interference, in this paper, we extend this concept by jointly handling the interference both in the spatial dimension (multi-user interference) and in the temporal dimension (inter-symbol interference). Accordingly, we propose a novel precoding method, referred to as spatio-temporal SLP. In this new precoding paradigm, faster-than-Nyquist (FTN) signaling can be applied over multi-user MISO systems, and the inter-symbol interference can be tackled at the transmitter side, without additional complexity for the user terminals. While applying FTN signaling, the proposed optimization strategies perform a sum power minimization with quality-of-service constraints. Numerical results are presented in a comparative fashion to show the effectiveness of the proposed techniques, which outperform the state-of-the-art SLP schemes in terms of symbol error rate, effective rate, and energy efficiency.

Edge-caching has received much attention as an efficient technique to reduce delivery latency and network congestion during peak-traffic times by bringing data closer to end users. Existing works usually design caching algorithms separately from physical layer design. In this paper, we analyze edge-caching wireless networks by taking into account the caching capability when designing the signal transmission. Particularly, we investigate multi-layer caching where both base station (BS) and users are capable of storing content data in their local cache and analyze the performance of edge-caching wireless networks under two notable uncoded and coded caching strategies. First, we calculate backhaul and access throughputs of the two caching strategies for arbitrary values of cache size. The required backhaul and access throughputs are derived as a function of the BS and user cache sizes. Second, closed-form expressions for the system energy efficiency (EE) corresponding to the two caching methods are derived. Based on the derived formulas, the system EE is maximized via precoding vectors design and optimization while satisfying a predefined user request rate. Third, two optimization problems are proposed to minimize the content delivery time for the two caching strategies. Finally, numerical results are presented to verify the effectiveness of the two caching methods.

Optimizing non-orthogonal multiple access (NOMA) in multi-cell scenarios is much more challenging than the single-cell case because inter-cell interference must be considered. Most papers addressing NOMA consider a single cell. We take a significant step in analyzing NOMA in multi-cell scenarios. We explore the potential of NOMA networks in achieving optimal resource utilization with arbitrary topologies. Towards this goal, we investigate a broad class of problems consisting of optimizing power allocation and user pairing for any cost function that is monotonically increasing in time-frequency resource consumption. We propose an algorithm that achieves global optimality for this problem class. The basic idea is to prove that solving the joint optimization problem of power allocation, user pair selection, and time-frequency resource allocation amounts to solving a so-called iterated function without a closed form. We prove that the algorithm approaches optimality with fast convergence. Numerically, we evaluate and demonstrate the performance of NOMA for multi-cell scenarios in terms of resource efficiency and load balancing.

Fast growth of Internet content and availability of electronic devices such as smart phones and laptops has created an explosive content demand. As one of the 5G technology enablers, caching is a promising technique to off-load the network backhaul and reduce the content delivery delay. Satellite communications provides immense area coverage and high data rate, hence, it can be used for large-scale content placement in the caches. In this work, we propose using hybrid mono/multi-beam satellite-terrestrial backhaul network for off-line edge caching of cellular base stations in order to reduce the traffic of terrestrial network. The off-line caching approach is comprised of content placement and content delivery phases. The content placement phase is performed based on local and global content popularities assuming that the content popularity follows Zipf-like distribution. In addition, we propose an approach to generate local content popularities based on a reference Zipf-like distribution to keep the correlation of content popularity. Simulation results show that the hybrid satellite-terrestrial architecture considerably reduces the content placement time while sustaining the cache hit ratio quite close to the upper-bound compared to the satellite-only method.

Non-orthogonal multiple access (NOMA) allows multiple users to simultaneously access the same time-frequency resource by using superposition coding and successive interfer- ence cancellation (SIC). Thus far, most papers on NOMA have focused on performance gain for one or sometimes two base stations. In this paper, we study multi-cell NOMA and provide a general framework for user clustering and power allocation, taking into account inter-cell interference, for optimizing resource allocation of NOMA in multi-cell networks of arbitrary topology. We provide a series of theoretical analysis, to algorithmically en- able optimization approaches. The resulting algorithmic notion is very general. Namely, we prove that for any performance metric that monotonically increases in the cells' resource consumption, we have convergence guarantee for global optimum. We apply the framework with its algorithmic concept to a multi-cell scenario to demonstrate the gain of NOMA in achieving significantly higher efficiency.

This paper investigates the problem of interference among the simultaneous multiuser transmissions in the downlink of multiple antennas systems. Symbol-level precoding (SLP) is a promising technique which has recently demonstrated large performance gains over the conventional block-level techniques. These gains can be translated in lower power requirements, improved energy efficiency, lower peak to average power ratio and resilience to non-linearities. However, previous works have not exploited the full potentials of SLP as it was only used to exploit multiuser interference spatially. In this paper, we extend this concept by using Faster-than-Nyquist (FTN) signaling and employing SLP to manage both multi-user and inter-symbol interference (ISI). We consider the aforementioned paradigm in the context of Massive multiple-input multiple-output (MIMO) systems, where the number of transmit antennas is usually an order of magnitude larger than the number of served users. In this rich degrees of freedom (DoF) environment, we show that FTN SLP can double the effective user rates while improving the energy efficiency.

Interference is identified as a critical issue for satellite communication (SATCOM) systems and services. There is a growing concern in the satellite industry to manage and mitigate interference efficiently. While there are efficient techniques to monitor strong interference in SATCOM, weak interference is not so easily detected because of its low interference to signal and noise ratio (ISNR). To address this issue, this paper proposes and develops a technique which takes place on-board the satellite by decoding the desired signal, removing it from the total received signal and applying an Energy Detector (ED) in the remaining signal for the detection of interference. Different from the existing literature, this paper considers imperfect signal cancellation, examining how the decoding errors affect the sensing performance, derives the expressions for the probability of false alarm and provides a set of simulations results, verifying the efficiency of the technique.

Cognitive Radio (CR) communication has been considered as one of the promising technologies to enable dynamic spectrum sharing in the next generation of wireless networks. Among several possible enabling techniques, Spectrum Sensing (SS) is one of the key aspects for enabling opportunistic spectrum access in CR Networks (CRN). From practical perspectives, it is important to design low-complexity wideband CR receiver having low resolution Analog to Digital Converter (ADC) working at a reasonable sampling rate. In this context, this paper proposes a novel spatio-temporal wideband SS technique by employing multiple antennas and one-bit quantization at the CR node, which subsequently enables the use of a reasonable sampling rate. In our analysis, we show that for the same sensing performance requirements, the proposed wideband receiver can have lower power consumption than the conventional CR receiver equipped with a single-antenna and a high-resolution ADC. Furthermore, the proposed technique exploits the spatial dimension by estimating the direction of arrival of Primary User (PU) signals, which is not possible by the conventional SS methods and can be of a significant benefit in a CRN. Moreover, we evaluate the performance of the proposed technique and analyze the effects of one-bit quantization with the help of numerical results.

Hybrid Analog-Digital transceivers are employed with the view to reduce the hardware complexity and the energy consumption in millimeter wave/large antenna array systems by reducing the number of their Radio Frequency (RF) chains. However, the analog processing network requires power for its operation and it further introduces power losses, dependent on the number of the transceiver antennas and RF chains, that have to be compensated. Thus, the reduction in the power consumption is usually much less than it is expected and given that the hybrid solutions present in general inferior spectral efficiency than a fully digital one, it is possible for the former to be less energy efficient than the latter in several cases. Existing approaches propose hybrid solutions that maximize the spectral efficiency of the system without providing any insight on their actual energy requirements/efficiency. To that end, in this paper, a novel algorithmic framework is developed based on which energy efficient hybrid transceiver designs are developed and their performance is examined with respect to the employed number of RF chains. Solutions are proposed for fully and partially connected hybrid architectures. Numerical results provide insight on when a hybrid transceiver is the most energy efficient solution or not.

This work studies the performance of a cooperative network which consists of two channel-coded sources, multiple relays, and one destination. Due to the spectral efficiency constraint, we assume that a single time slot is dedicated to relaying. Conventional network-coded based cooperation (NCC) selects the best relay which uses network coding to serve the two sources simultaneously. It is shown that NCC, however, only achieves diversity of order two regardless of the number of available relays and the channel code. In this paper, we propose a novel partial relaying based cooperation (PARC) scheme to improve the system diversity in the finite signal-to-noise ratio (SNR) regime. Firstly, closed-form expressions for the system bit error rate (BER) and diversity order of PARC are derived as a function of the operating SNR value and the minimum distance of the channel code. Secondly, we analytically show that the proposed PARC achieves full diversity order in the finite SNR regime, given that an appropriate channel code is used. Finally, numerical results verify our analysis and demonstrate a large SNR gain of PARC over NCC in the SNR region of interest.

The employment of a massive number of antennas in multiple-input multiple-output systems, known as massive MIMO, has drawn a new horizon for future communications systems to support a very large number of users. However, the actual number of active users in massive MIMO are limited by pilots training via the coherence time of the communication channel which is inversely proportional to the user velocity. The current model applies this coherence time for every user to design multiuser massive MIMO, which might result in a suboptimal solution since the users usually move at different speeds in practice. In this paper, we investigate multiuser massive MIMO by taking into consideration the differences in user velocities. In particular, two multiuser models are proposed to maximize the per-user spectral efficiency and the number of served users, respectively. System capacity of the proposed models is provided in analytical expression. Finally, numerical results demonstrate the advantages of our proposed models compared with the reference model.

In this paper, we study a problem of relay selection in a two-hop relaying network where the destination is equipped with Simultaneous Wireless Information and Power Transmission (SWIPT) capabilities. In contrast to conventional cooperative networks, the destination node is considered to be capable of simultaneously decoding information and harvesting energy from both the source and the relay transmissions. In this context, we formulate two optimization problems for both time switching (TS) and power splitting (PS) based SWIPT schemes. The first problem is the maximization of the overall user data rate while ensuring a minimum harvested power. The second problem focuses on the maximization of the overall harvested power at the user under the constraint on the minimum achievable rate. Assuming an amplify-and-forward (AF) relay protocol, closed-form solutions are obtained for the selection of an optimal relay, relay amplification coefficient and the optimal time or power splitting factor. The performance of the proposed relay selection strategies with the aforementioned objectives is evaluated and compared with the case of random relay selection. Furthermore, the Rate-Energy (R-E) tradeoff performance of the scenario with both the direct and indirect relay-assisted links is compared to the case where only a relay-assisted link is available. Our simulation results demonstrate the significant benefits of combining direct and indirect links in SWIPT-enabled cooperative networks in terms of the R-E tradeoff.

We present a precoded multi-user communication test-bed to demonstrate forward link interference mitigation techniques in a multi-beam satellite system scenario which will enable a full frequency reuse scheme. The developed test-bed provides an end-to-end precoding demonstration, which includes a transmitter, a multi-beam satellite channel emulator and user receivers. Each of these parts can be reconfigured accordingly to the desired test scenario. Precoded communications allow full frequency reuse in multiple-input multiple-output (MIMO) channel environments, where several coordinated antennas simultaneously transmit to a number of independent receivers. The developed real-time transmission test-bed assist in demonstrating, designing and benchmarking of the new Symbol-Level Precoding (SLP) techniques, where the data information is used, along with the channel state information, in order to exploit the multi-user interference and transform it into useful power at the receiver side. The demonstrated SLP techniques are designed in order to be computationally efficient, and can be generalized to others multi-channel interference scenarios.

In this paper, we investigate the secrecy outage probability of random wireless networks from the perspective of the k-th best source, which has still not been well characterized. We consider the artificial noise (AN) transmission strategy at source nodes to confuse the eavesdropper. Furthermore, we use a concept of security-region based on the k-th best source index. This is pragmatic in creating a protected communication zone for the typical destination and also in bounding the number of sources that can cooperate in a Coordinated Multi-point transmission (CoMP) network. We further derive the secrecy outage probability for these CoMP sources based on the security-region. We also provide a closed-form expression for the maximum number of eavesdroppers for a given secrecy outage constraint, which can effect the secure communication. Tractable numerical results are presented under various assumptions of densities, antenna gains, AN transmission factors and path loss exponents.

Simultaneous Information and Power Transfer (SWIPT) for wireless communication systems presents a new paradigm, allowing wireless nodes to recharge their rechargeable batteries from RF signals (instead of fixed line or traditional energy sources) while decoding information. In this approach, the energy is harvested from ambient electromagnetic sources available within the communication system or from sources that directionally transmit RF energy. This work presents an overview, and advancement to date, as well as identifies research issues and challenges in SWIPT and RF Wireless Power Transfer (WPT) assisted technologies. The paper, in principal, addresses innovative 5G communications and Internet of Things (IoT) technologies associated with SWIPT/WPT. The paper finally presents recommendations and future trends associated with this emerging future electricity technique. This provides a valuable reference and new avenues for the future research in this direction.

Content caching is an efficient technique to reduce delivery latency and system congestion during peak-traffic times by bringing data closer to end users. Existing works on caching usually assume symmetric networks with identical user requests distribution, which might be in contrast to practical scenarios where the number of users is usually arbitrary. In this paper, we investigate a cache-assisted heterogeneous network in which edge nodes or base stations (BSs) are capable of storing content data in their local cache. We consider general practical scenarios where each edge node is serving an arbitrary number of users. First, we derive an optimal storage allocation over the BSs to minimize the shared backhaul throughput for a uncoded caching policy. Second, a novel coded caching strategy is proposed to further reduce the shared backhaul's load. Finally, the effectiveness of our proposed caching strategy is demonstrated via numerical results.

Hybrid analog-digital transceivers are employed with the view to reduce the hardware complexity and the energy consumption in millimeter wave/large antenna array systems by reducing the number of their radio frequency (RF) chains. However, the analog processing network requires power for its operation and it further introduces power losses, dependent on the number of the transceiver antennas and RF chains that have to be compensated. Thus, the reduction in the power consumption is usually much less than it is expected and given that the hybrid solutions present in general inferior spectral efficiency than a fully digital one, it is possible for the former to be less energy efficient than the latter in several cases. Existing approaches propose hybrid solutions that maximize the spectral efficiency of the system without providing any insight on their energy requirements/efficiency. To that end, in this paper, a novel algorithmic framework is developed based on which energy efficient hybrid transceiver designs are derived and their performance is examined with respect to the number of RF chains and antennas. Solutions are proposed for fully and partially connected hybrid architectures and for both single- and multi-carrier systems under the orthogonal frequency division multiplexing modulation. Simulations and theoretical results provide insight on the cases, where a hybrid transceiver is the most energy efficient solution or not.

Symbol-level precoding is a new paradigm for multiuser multiple-antenna downlink systems aimed at creating constructive interference among transmitted data streams. This can be enabled by designing the precoded signal of the multiantenna transmitter on a symbol level, taking into account both channel state information and data symbols. Previous literature has studied this paradigm for Mary phase shift keying modulations by addressing various performance metrics, such as power minimization and maximization of the minimum rate. In this paper, we extend this to generic multi-level modulations, i.e., Mary quadrature amplitude modulation by establishing connection to PHY layer multicasting with phase constraints. Furthermore, we address the adaptive modulation schemes which are crucial in enabling the throughput scaling of symbol-level precoded systems. In this direction, we design the signal processing algorithms for minimizing the required power under per-user signal to interference noise ratio or goodput constraints. Extensive numerical results show that the proposed algorithm provides considerable power and energy efficiency gains, while adapting the employed modulation scheme to match the requested data rate.

In conventional cognitive radio systems, the secondary user employs a “listen-before-talk” paradigm, where it senses if the primary user is active or idle, before it decides to access the licensed spectrum. However, this method faces challenges, with the most important one being the reduction of the secondary user’s throughput, as no data transmission takes place during the sensing period. In this context, the idea of simultaneous spectrum sensing and data transmission is proposed. This paper studies a system model where this concept is obtained through the collaboration of the secondary transmitter with the secondary receiver. First, the secondary receiver decodes the signal from the secondary transmitter, removes it from the total received signal, and then carries out spectrum sensing in the remaining signal in order to determine the presence/absence of the primary user. Different from the existing literature, this paper considers the imperfect signal cancellation, evaluating how the decoding errors affect the sensing reliability, and derives the analytical expressions for the probability of false alarm. Finally, numerical results are presented illustrating the accuracy of the proposed analysis.

In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) to access the frequency band of a primary user (PU) in an underlay cognitive communication scenario. The main idea is that the CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback is implicit channel state information of the PU link, indicating whether the probing-induced interference is harmful or not. The intelligence of this sequential probing process lies in the selection of the power levels of the secondary users, which aims to minimize the number of probing attempts, a clearly active learning (AL) procedure, and expectantly the overall PU QoS degradation. The enhancement introduced in this paper is that we incorporate the probability of each feedback being correct into this intelligent probing mechanism by using a multivariate Bayesian AL method. This technique is inspired by the probabilistic bisection algorithm and the deterministic cutting plane methods (CPMs). The optimality of this multivariate Bayesian AL method is proven and its effectiveness is demonstrated through numerical simulations. Computationally cheap CPM adaptations are also presented, which outperform existing AL methods.

This paper investigates the problem of the interference among multiple simultaneous transmissions in the downlink channel of a multi- antenna wireless system. A symbol-level precoding scheme is considered, where the data information is used, along with the channel state information, in order to exploit the multi-user interference and transform it into useful power at the receiver side. In this framework, it is important to consider the power limitations individually for each transmitting antenna, since a common practice in multi-antenna systems is the use of separate per-antenna amplifiers. Thus, herein the problem of per-antenna power minimization in symbol-level precoding is formulated and solved, under Quality-of-Service constraints. In the proposed approach, the precoding design is optimized in order to control the instantaneous power transmitted by the antennas, and more specifically to limit the power peaks, while guaranteeing some specific target signal-to-noise ratios at the receivers. Numerical results are presented to show the effectiveness of the proposed scheme, which outperforms the existing state of the art techniques in terms of reduction of the power peaks and of the peak-to-average power ratio across the transmitting antennas.

In this work, a directional modulation-based technique is devised to enhance the security of a multi-antenna wireless communication system employing M-PSK modulation to convey information. The directional modulation method operates by steering the array beam in such a way that the phase of the received signal at the receiver matches that of the intended M-PSK symbol. Due to the difference between the channels of the legitimate receiver and the eavesdropper, the signals received by the eavesdropper generally encompass a phase component different than the actual symbols. As a result, the transceiver which employs directional modulation can impose a high symbol error rate on the eavesdropper without requiring to know the eavesdropper's channel. The optimal directional modulation beamformer is designed to minimize the consumed power subject to satisfying a specific resulting phase and minimal signal amplitude at each antenna of the legitimate receiver. The simulation results show that the directional modulation results in a much higher symbol error rate at the eavesdropper compared to the conventional benchmark scheme, i.e., zero-forcing precoding at the transmitter.

One of the fundamental issues in Wireless Sensor Networks (WSN) is to count and localize multiple targets accurately. In this context, there has been an increasing interest in the literature in using Compressive Sensing (CS) based techniques by exploiting the sparse nature of spatially distributed targets within the monitored area. However, most existing works aim to count and localize the sparse targets utilizing a Single Measurement Vector (SMV) model. In this paper, we consider the problem of counting and localizing multiple targets exploiting the joint sparsity feature of a Multiple Measurement Vector (MMV) model. Furthermore, the conventional MMV formulation in which the same measurement matrix is used for all sensors is not valid any more in practical time-varying wireless environments. To overcome this issue, we reformulate the MMV problem into a conventional SMV in which MMVs are vectorized. Subsequently, we propose a novel reconstruction algorithm which does not need the prior knowledge of the sparsity level unlike the most existing CS-based approaches. Finally, we evaluate the performance of the proposed algorithm and demonstrate the superiority of the proposed MMV approach over its SMV counterpart in terms of target counting and localization accuracies.

In this paper, a novel simplified statistical approach to evaluate the error performance bound of Ordered Statistics Decoding (OSD) of Linear Block Codes (LBC) is investigated. First, we propose a novel statistic which depicts the number of errors contained in the ordered received noisy codeword. Then, simplified expressions for the probability mass function and cumulative distribution function are derived exploiting the implicit statistical independence property of the samples of the received noisy codeword before reordering. Second, we incorporate the properties of this new statistic to derive the simplified error performance bound of the OSD algorithm for all order-I reprocessing. Finally, with the proposed approach, we obtain computationally simpler error performance bounds of the OSD than those proposed in literature for all length LBCs.

In this paper, we study the performance of hybrid cognitive radio systems that combine the benefits of interweave and underlay systems by employing a spectrum sensing and a power control mechanism at the Secondary Transmitter (ST). Existing baseline models considered for performance analysis assume perfect knowledge of the involved channels at the ST, however, such situations hardly exist in practical deployments. Motivated by this fact, we propose a novel approach that incorporates channel estimation at the ST, and consequently characterizes the performance of Hybrid Systems (HSs) under realistic scenarios. To capture the impact of imperfect channel knowledge, we propose outage constraints on the detection probability at the ST and on the interference power received at the primary receiver. Our analysis reveals that the baseline model overestimates the performance of the HS in terms of achievable secondary user throughput. Finally, based on the proposed estimation-sensing-throughput tradeoff, we determine suitable estimation and sensing durations that effectively capture the effect of imperfect channel knowledge and subsequently enhance the achievable secondary user throughput.

In this paper, we consider the cognitive satellite uplink where satellite terminals reuse frequency bands of Fixed-Service (FS) terrestrial microwave links which are the incumbent users in the Ka 27.5–29.5 GHz band. In this scenario, the transmitted power of the cognitive satellite terminals has to be controlled so as to satisfy the interference constraints imposed by the incumbent FS receivers. We investigate and analyze a set of optimization frameworks for the power and rate allocation problem in the considered cognitive satellite scenario. The main objective is to shed some light on this rather unexplored scenario and demonstrate feasibility of the terrestrial-satellite co-existence. In particular, we formulate a multi-objective optimization problem where the rates of the satellite terminals form the objective vector and derive a general iterative framework which provides a Pareto-optimal solution. Next, we transform the multi-objective optimization problem into different single-objective optimization problems, focusing on popular figures of merit such as the sum-rate or the rate fairness. Supporting results based on numerical simulations are provided which compare the different proposed approaches.

This paper considers coordinated multicast beam-forming in a multi-cell wireless network. Each multiantenna base station (BS) serves multiple groups of single antenna users by generating a single beam with common data per group. The aim is to minimize the sum power of BSs while satisfying user-specific SINR targets. We propose centralized and distributed multicast beamforming algorithms for multi-cell multigroup systems. The NP-hard multicast problem is tackled by approximating it as a convex problem using the standard semidefinite relaxation method. The resulting semidefinite program (SDP) can be solved via centralized processing if global channel knowledge is available. To allow a distributed implementation, the primal decomposition method is used to turn the SDP into two optimization levels. The higher level is in charge of optimizing inter-cell interference while the lower level optimizes beamformers for given inter-cell interference constraints. The distributed algorithm requires local channel knowledge at each BS and scalar information exchange between BSs. If the solution has unit rank, it is optimal for the original problem. Otherwise, the Gaussian randomization method is used to find a feasible solution. The superiority of the proposed algorithms over conventional schemes is demonstrated via numerical evaluation.

In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The main idea is that the CRN probes the PU and subsequently applies a Modulation and Coding Classification (MCC) technique to acquire the Modulation and Coding scheme (MCS) of the PU. This feedback is an implicit channel state information (CSI) of the PU link, indicating how harmful the probing induced interference is. The intelligence of this sequential probing process lies on the selection of the power levels of the Secondary Users (SUs) which aims to minimize the number of probing attempts, a clearly Active Learning (AL) procedure, and consequently the overall PU QoS degradation. The enhancement introduced in this work is that we incorporate the probability of each feedback being correct into this intelligent probing mechanism by using a univariate Bayesian Nonparamet-ric AL method, the Probabilistic Bisection Algorithm (PBA). An adaptation of the PBA is implemented for higher dimensions and its effectiveness as an uncertainty driven AL method is demonstrated through numerical simulations.

Knowledge of interacting channels is essential for characterizing the performance of a cognitive radio system in terms of interference power received by a primary receiver and throughput at a secondary receiver. Baseline models considered for the performance characterization assume perfect knowledge of the interacting channels. Recently, an analytical framework has been proposed that incorporates channel estimation and subsequently characterizes the performance of cognitive Interweave Systems (ISs). However, the analysis was pertained to the deterministic behaviour of the interacting channels. In this paper, we extend the characterization of the aforementioned framework to investigate the influence of channel fading on the performance of the IS. Our analysis indicate that an inappropriate choice of estimation time can severely degrade the performance of the IS in terms of achievable secondary throughput.

Cognitive Radio communication is foreseen as one of the possible candidates that can resolve spectrum scarcity currently faced by the upcoming wireless technologies. This scarcity can be solved by enabling secondary access to the licensed spectrum. The interference at the primary receiver can be avoided by employing a detector (spectrum sensing) at the Secondary Transmitter (ST). Energy detection is widely used due to its low complexity and applicability to a large range of primary user signals. Recently, antenna diversity techniques such as square-law selector and square-law combiner have been used to enhance the detection performance at the ST. In this context, the detector's performance pertaining to the antenna diversity techniques has been characterized analytically. However, issues such as RF impairments and deploying a fading model render hardware implementation of such techniques challenging. Motivated by this fact, this paper presents the deployment of a hardware, and subsequently utilize the theoretical expressions to validate the performance of a multi-antenna system at the ST that exploits antenna diversity techniques in a realistic environment. Finally, we emphasize the challenges faced during the hardware implementation and present our approach to address these challenges.

Among several potential applications of Full- Duplex (FD) technology, FD Cognitive Radio (CR) communication is one important area where FD can provide several advantages and possibilities such as concurrent sensing and transmission, improved sensing efficiency and the secondary throughput. However, the main challenge is to mitigate the harmful effects of the residual Self-Interference (SI) which depends on the SI mitigation capability of the employed technique. One way to mitigate this effect is to control the transmit power of the CR node, however, this power control over the entire frame duration results in a power- throughput tradeoff. In this context, we propose a novel Two-Phase Concurrent Sensing and Transmission (2P-CST) framework in which a CR performs concurrent sensing and transmission for a certain fraction of the frame duration by employing a power control mechanism and for the remaining fraction of the frame duration, the CR only transmits with the full power. The proposed framework allows the flexibility to optimize the sensing time and the transmit power in order to maximize the achievable throughput of the FD-CR system. Our results demonstrate that the proposed 2P-CST FD transmission strategy provides better performance in terms of the achievable throughput than the conventional Periodic Sensing and Transmission (PST) and CST techniques.

This paper addresses the problem of exploiting interference among simultaneous multiuser transmissions in the downlink of multiple-antenna systems. Using symbol-level precoding, a new approach toward addressing the multiuser interference is discussed through jointly utilizing the channel state information (CSI) and data information (DI). The interference among the data streams is transformed under certain conditions to a useful signal that can improve the signal-to-interference noise ratio (SINR) of the downlink transmissions and as a result the system’s energy efficiency. In this context, new constructive interference precoding techniques that tackle the transmit power minimization (min power) with individual SINR constraints at each user’s receiver have been proposed. In this paper, we generalize the constructive interference (CI) precoding design under the assumption that the received MPSK symbol can reside in a relaxed region in order to be correctly detected. Moreover, a weighted maximization of the minimum SNR among all users is studied taking into account the relaxed detection region. Symbol error rate analysis (SER) for the proposed precoding is discussed to characterize the tradeoff between transmit power reduction and SER increase due to the relaxation. Based on this tradeoff, the energy efficiency performance of the proposed technique is analyzed. Finally, extensive numerical results show that the proposed schemes outperform other state-of-the-art techniques.

Secondary access to the licensed spectrum is viable only if the interference is avoided at the primary system. In this regard, different paradigms have been conceptualized in the existing literature. Among these, interweave systems (ISs) that employ spectrum sensing have been widely investigated. Baseline models investigated in the literature characterize the performance of the IS in terms of a sensing-throughput tradeoff, however, this characterization assumes perfect knowledge of the involved channels at the secondary transmitter, which is unavailable in practice. Motivated by this fact, we establish a novel approach that incorporates channel estimation in the system model, and consequently investigate the impact of imperfect channel knowledge on the performance of the IS. More particularly, the variation induced in the detection probability affects the detector’s performance at the secondary transmitter, which may result in severe interference at the primary receivers. In this view, we propose employing average and outage constraints on the detection probability, in order to capture the performance of the IS. Our analysis reveals that with an appropriate choice of the estimation time determined by the proposed approach, the performance degradation of the IS can be effectively controlled, and subsequently the achievable secondary throughput can be significantly enhanced.

The constructive interference concept in the downlink of multiple-antenna systems is addressed in this paper. The concept of the joint exploitation of the channel state information (CSI) and data information (DI) is discussed. Using symbol-level precoding, the interference between data streams is transformed Under certain conditions into useful signal that can improve the signal to interference noise ratio (SINR) of the downlink transmissions. In the previous work, different constructive interference precoding techniques have been proposed for the MPSK scenario. In this context, a novel constructive interference precoding technique that tackles the transmit power minimization (min-power) with individual SINR constraints at each user's receivers is proposed assuming MQAM modulation. Extensive simulations are performed to validate the proposed technique.

Understanding the performance of cognitive radio systems is of great interest. To perform dynamic spectrum access, different paradigms are conceptualized in the literature. Of these, Underlay System (US) has caught much attention in the recent past. According to US, a power control mechanism is employed at the Secondary Transmitter (ST) to constrain the interference at the Primary Receiver (PR) below a certain threshold. However, it requires the knowledge of channel towards PR at the ST. This knowledge can be obtained by estimating the received power, assuming a beacon or a pilot channel transmission by the PR. This estimation is never perfect, hence the induced error may distort the true performance of the US. Motivated by this fact, we propose a novel model that captures the effect of channel estimation errors on the performance of the system. More specifically, we characterize the performance of the US in terms of the estimation-throughput tradeoff. Furthermore, we determine the maximum achievable throughput for the secondary link. Based on numerical analysis, it is shown that the conventional model overestimates the performance of the US.

Herein, we study the spectral coexistence of Geostationary (GEO) Fixed Satellite Services (FSS) downlink and Broadcasting Satellite Services (BSS) feeder links in the Ka-band (17.3 – 18.1 GHz) which is primarily allocated for BSS feeder links. Firstly, a novel cognitive spectrum exploitation framework is proposed in order to utilize the available band efficiently. Subsequently, based on the interference analysis carried out between these systems, two cognitive approaches, namely Carrier Allocation (CA) and Beamforming (BF), are investigated under the considered framework assuming the availability of an accurate Radio Environment Map (REM). The employed techniques allow the flexibility of using additional shared carriers for the FSS downlink system along with the already available exclusive carriers (19.7 – 20.2 GHz), thus increasing the overall system throughput. It is shown that a significant improvement in the per beam throughput as well as in the beam availability can be achieved by applying CA and BF approaches in the considered scenario.

Exploring innovative cellular architectures to achieve enhanced system capacity and good coverage has become a critical issue towards realizing the fifth generation (5G) of wireless communications. In this context, this paper proposes a novel concept of an intelligent Amplify and Forward (AF) 5G repeater for enabling the densification of future cellular networks. The proposed repeater features a Spectrum Sensing (SS) intelligence capability and utilizes such intelligence in a complementary fashion in comparison to its existing counterpart (e.g., Cognitive Radio) by detecting the active channels within the assigned spectrum. This intelligence allows the proposed repeater to carry out selective amplification of the active channels in contrast to the full amplification in conventional AF repeaters. Furthermore, the performance of a Frequency Division Multiple Access (FDMA) based two hop cellular network utilizing the proposed repeater is evaluated in terms of the system throughput. Simulation results demonstrate up to 13 % increase when compared with the conventional repeaters. Moreover, the effect of SS errors on the system capacity is analyzed.

Spectrum Sensing (SS) is one of the fundamental mechanisms required by a Cognitive Radio (CR). Among several SS techniques, cyclostationary feature detection is considered as an important technique due to its robustness against noise variance uncertainty and its capability to distinguish among different systems on the basis of their cyclostationary features. However, one of the main limitations of this detector in practical scenarios is its performance degradation in the presence of cyclic frequency mismatch, which mainly arises due to the lack of knowledge about the transmitter clock/oscillator errors at the detector. In this context, this paper proposes a novel solution to address the cyclic frequency mismatch problem utilizing the Slepian basis expansion instead of the widely used Fourier basis expansion. It is shown that the proposed approach captures the deviation in the cyclic frequency caused by the aforementioned imperfections and hence provides a significant improvement in the sensing performance in the presence of cyclic frequency mismatch.

A fundamental problem facing the next generation of Satellite Communications (SatComs) is the spectrum congestion and how the scarce spectral resources are allocated to meet the demand for higher rate and reliable broadband communications. In this context, this paper addresses the cognitive satellite uplink where satellite terminals reuse frequency bands of Fixed-Service (FS) terrestrial microwave links which are the incumbent users in the Ka band. In this scenario, the transmit power of the satellite terminals has to be controlled such that the total aggregated interference at the FS system is kept below some acceptable threshold. In this paper, we review simple and efficient power allocation techniques available in the literature and, with slight adaptations, we evaluate them to the proposed satellite uplink and terrestrial FS co-existence scenario. The presented numerical results highlight the tradeoff between the level of channel state information and the rates that can be achieved at the satellite network.

Satellite communication (SatCom) is facing a spectrum scarcity problem due to the limited available exclusive spectrum and the high demand of the broadband satellite services. In this context, there has been an increasing interest in the satellite community to exploit the non- exclusive Ka-band spectrum in order to enhance the spectral efficiency of future broadband satellite systems. Herein, we propose a novel concept of enabling the spectral coexistence of satellite and terrestrial networks using three dimensional (3D) beamforming, which exploits the elevation dimension in addition to the commonly used azimuth dimension. The proposed beamforming solution is employed in a Multiple-Input Low Noise Block Downconverter (MLNB) based Feed Array Reflector (FAR) in contrast to the widely used Uniform Linear Array (ULA) structure. Within the employed antenna structure, the performance of the proposed beamforming solution is evaluated considering different feed arrangements. Finally, a database-assisted approach and two blind approaches are suggested for the effective implementation of the proposed solutions.

The present work focuses on the forward link of a broadband multibeam satellite system that aggressively reuses the user link frequency resources. Two fundamental practical challenges, namely the need to frame multiple users per transmission and the per-antenna transmit power limitations, are addressed. To this end, the so-called frame-based precoding problem is optimally solved using the principles of physical layer multicasting to multiple co-channel groups under per-antenna constraints. In this context, a novel optimization problem that aims at maximizing the system sum rate under individual power constraints is proposed. Added to that, the formulation is further extended to include availability constraints. As a result, the high gains of the sum rate optimal design are traded off to satisfy the stringent availability requirements of satellite systems. Moreover, the throughput maximization with a granular spectral efficiency versus SINR function, is formulated and solved. Finally, a multicast-aware user scheduling policy, based on the channel state information, is developed. Thus, substantial multiuser diversity gains are gleaned. Numerical results over a realistic simulation environment exhibit as much as 30% gains over conventional systems, even for 7 users per frame, without modifying the framing structure of legacy communication standards.

Interference in wireless networks degrades the signal quality at the terminals. However, it can potentially enhance the secrecy rate. This paper investigates the secrecy rate in a two-user interference network where one of the users, namely user 1, needs to establish a confidential connection. User 1 wants to prevent an unintended user of the network from decoding its transmission. User 1 has to transmit such that its secrecy rate is maximized while the quality of service at the destination of the other user, user 2, is satisfied, and both user's power limits are taken into account. We consider two scenarios: 1) user 2 changes its power in favor of user 1, an altruistic scenario, and 2) user 2 is selfish and only aims to maintain the minimum quality of service at its destination, an egoistic scenario. It is shown that there is a threshold for user 2's transmission power that only below or above which, depending on the channel qualities, user 1 can achieve a positive secrecy rate. Closed-form solutions are obtained to perform joint optimal power control. Further, a new metric called secrecy energy efficiency is introduced. We show that in general, the secrecy energy efficiency of user 1 in an interference channel scenario is higher than that of an interference-free channel.

Distributed spectrum sensing improves the detection reliability of a cognitive radio network but generally comes at the price of a large power consumption. Since cognitive radios are generally low-power sensors with limited batteries, a combined censoring and sleeping scheme is considered as an energy-efficient algorithm for distributed spectrum sensing. Each sensor switches off its sensing module with a specific sleeping rate. When the sensor is on, a censoring policy is employed to send the sensing result to the fusion center. The result is only transmitted, if it is deemed to be informative. Hence, the energy consumption of each sensor, including the sensing and transmission energies, is reduced. The underlying sensing parameters are derived by minimizing the maximum average energy consumption per sensor subject to a lower-bound on the global probability of detection and an upper-bound on the global probability of false alarm. We analyze the problem for the OR and the AND rule and provide a performance analysis for a case study based on the IEEE 802.15.4/ZigBee standard. It is shown that the combined censoring and sleeping scheme achieves a significant energy saving compared to the case where no censoring or sleeping is taken into account.