Satellites provide seamless coverage and are vital for establishing emergency communications during natural disasters. However, its effectiveness is limited by the allocated spectrum and high deployment costs. To address these challenges, we propose a solution based on the fluid antenna system (FAS), which enables dynamic signal adjustment for enhanced performance. Building on this concept, compact ultra massive antenna array (CUMA) is introduced, which activates multiple ports simultaneously, allowing the in-phase components of signals to be constructively combined. This approach mitigates interference while significantly reducing costs, as each fluid antenna requires only a single RF chain yet achieves substantial improvement in the received signal-to-interference-plus-noise ratio (SINR). In this paper, we consider a satellite CUMA network in which all ground users are assigned to the same satellite for uplink transmission, and the satellite leverages CUMA to mitigate inter-user interference. We derive closed-form expressions for the received CUMA signal power, interference power, and their distributions. Based on these expressions, we present the outage probability in a single integrated form, along with an approximated closed-form expression. The ergodic rate is hence provided. Our findings reveal the conditions under which CUMA outperforms maximum ratio combining in satellite communications scenario under various configurations. Notably, our analysis demonstrates that with a sufficient compact fluid antenna setting, the received CUMA signal becomes deterministic rather than a random variable, indicating that the system performance depends solely on the interference distribution. Moreover, for the compact fluid antenna configuration, increasing the number of ports results in a linear improvement in the beamforming gain. Finally, numerical results are provided to compare orthogonal multiple access CUMA (O-CUMA) and non-orthogonal multiple access CUMA (N-CUMA) in satellite communications, showing that with broad bandwidth,N-CUMA outperforms O-CUMA.

Stacked intelligent metasurfaces (SIM) have become a promising technology to improve the wave-domain signal processing and increase the wireless communication capacity. However, optimizing the phase configuration remains a significant challenge due to the discrete and highly combinatorial nature of the multi-layer architecture. To address this, we propose a quantum-inspired coordinated design framework for joint wave-based beamforming and power allocation in SIM-assisted multiuser systems. By leveraging a black-box second-order approximation, the discrete phase optimization is reformulated into a standard quadratic unconstrained binary optimization (QUBO) problem. Quantum-inspired discrete simulated bifurcation (dSB) solver is used to find the candidates effectively, and then a tabu-based local refinement strategy is applied to refine these candidates and reduce the deviation of the approximate solution. Concurrently, an iterative water-filling scheme is integrated to optimize power allocation, facilitating a synergy between global search and fine-grained control. Simulation results confirm that the proposed approach consistently outperforms classical benchmarks in terms of sum rate, convergence speed, and interference suppression. The framework exhibits strong scalability across varying system dimensions and channel realizations, validating its effectiveness in wave-domain communication scenarios.

To efficiently implement Terahertz (THz) communications in the 6G era, ultra-massive multiple-input multiple-output (UM-MIMO) technique is considered essential. However, effective wideband THz UM-MIMO transmissions necessitate low-cost yet accurate channel estimation (CE) methods. In this article, we investigate the wideband THz UM-MIMO CE problem under hybrid near- and far-field propagation, molecular absorption, and multi-path reflection. The CE problem is reformulated into a compressed sensing (CS)-aided counterpart (CSCE), exploiting the inherent sparsity of THz UM-MIMO channels to reduce pilot overhead. Our key contributions are: 1) after analyzing the inefficiency of conventional Bayesian learning (BL)-based CSCE frameworks in solving this CE task, we propose a deep unfolding (DU)-aided BL (DUBL) CE algorithm, in which the unfolded expectation-maximization (EM) iteration is implemented through a carefully tailored deep neural network (DNN) architecture; 2) we design a staged offline training procedure equipped with a dedicated loss function to ensure efficient DUBL training; and 3) we conduct a detailed complexity analysis that explicitly quantifies the computational cost of each unrolled layer, thereby characterizing the online inference overhead of the proposed DUBL method. Simulation results demonstrate that the DUBL solution offers substantial THz UM-MIMO CE gains over representative baselines, while complexity comparison highlights its enhanced real-time inference.

Ambient backscatter communications (AmBC), where a backscatter transmitter (BT) modulates and reflects ambient signals to a backscatter receiver (BR), have been deemed a low-power communication technology for the Internet of Things. Previous work on symbol detection in AmBC assumed perfect time synchronization (TS), which is unrealistic in practice. The residual TS errors (RTSE) causepartial sample mismatch, degrading symbol detection performance. To address this, we propose a new AmBC symbol detection framework that incorporates the BT’s current and adjacent symbols, as well as channel coefficients. Using energy detector (ED) as a case study, we derive both exact and approximate bit error rate (BER) expressions. Our results show that the ED’s BER performance degrades significantly under RTSE, with the symbol detection threshold optimized under the assumption of perfect TS. We then derive a closed-form expression for a near-optimal symbol detection threshold that minimizes BER under RTSE. To estimate the required parameters for the detection threshold, we propose a novel method exploiting the attributes of the BR’s received signal samples. The analytical results are verified by simulation results.

The fluid antenna system (FAS) enables position reconfigurability. A potential drawback of real-time FAS, however, is that it requires complete channel state information (CSI) for each FAS port at every communication time slot, an approach referred to as ideal-FAS. Recognizing the difficulties of achieving ideal-FAS, we propose a FAS scheme based on incomplete CSI, referred to as semi-blind FAS. This paper first introduces the spatial-temporal framework of FAS, upon which the proposed semi-blind FAS is developed. The proposed semi-blind FAS is lightweight and computationally efficient, scalable to an arbitrary number of ports and time slots, and operates without pre-training or deep learning structures. The scheme effectively exploits incomplete historical CSI to estimate the conditional distribution across all FAS ports at the desired time slot, thereby identifying the statistical optimal port for signal reception. Generally, the key idea of semi-blind FAS is to select the optimal port through conditional distribution analysis, from a statistical perspective, with optimality defined according to the scenario of interest. Inspired by information-theoretic entropy, we further develop the residual entropy power ratio to characterize how physical parameters influence the performance gap between semi-blind FAS and ideal-FAS. Our analysis reveals that estimation performance depends not only on the number of sampled ports and time slots, but also on the specific indices of ports with given CSI at each time slot, i.e., the port sampling strategy. This critical factor has been largely overlooked in existing port estimation studies. Numerical results demonstrate that the proposed semi-blind FAS achieves performance comparable to, and in some cases indistinguishable from, that of ideal-FAS, while requiring significantly fewer port CSI measurements and lower port switching speeds.

This paper develops an algorithmic solution using Ising machines to solve large-scale higher-order binary optimization (HOBO) problems with inequality constraints for resource optimization in wireless communications systems. Quadratic unconstrained binary optimization (QUBO) aims to solve a special category of these problems widely encountered in engineering and science. To solve QUBO instances, specialized Ising machines have been designed, while sophisticated quantum annealing algorithm and quantum-inspired classical heuristics have been developed. However, the application of QUBO in wireless communications has limited practical interest mainly due to the complexity of resource optimization problems which are often characterized by high-order polynomial terms and strict inequality constraints. To overcome these bottlenecks and take advantage of recent advancements in Ising machines, in this paper, we propose an iterative algorithmic solution to solve HOBO problems, which is based on the augmented Lagrangian method to handle constraints. Specifically, Taylor expansion is employed to approximate higher-order polynomials to quadratic ones in the augmented Lagrangian function, which enables the solution of a single QUBO problem at each iteration without auxiliary variables. As an illustrative case study, we consider the problem of phase optimization in a simultaneous wireless information and power transfer system, where a reconfigurable intelligent surface with 1-bit phase resolution is used to facilitate information/energy transfer. Simulation results verify that the proposed algorithm achieves satisfactory performance and outperforms heuristic benchmark schemes.

Fluid antenna is a new reconfigurable antenna technology that can dynamically adjust the positions or ports of radiating elements and therefore provides a new degree of freedom for wireless communications. However, the associated port selection is a challenging large-scale combinatorial optimization problem and difficult to solve. Existing manually designed heuristic algorithms are not only labor-intensive, but cannot achieve satisfactory performance. In this paper, we propose a novel paradigm that leverages large language models (LLMs) for automated design of optimization algorithms for fluid antenna systems without manual hyperheuristic tuning. Specifically, we study the problem of maximizing the minimum signal-to-interference-plus-noise ratio (SINR) in the downlink to ensure fairness among users by optimizing port selection and beamforming. We investigate two LLM-enabled algorithm optimization strategies. The first is to optimize the crossover and mutation operations to enhance the performance of the well-known genetic algorithm and the second is to design AutoPort, a new heuristic from scratch by LLM, to solve the optimization problem. Simulation results verify that the proposed method can achieve near-optimal performance and significant improvement over the conventional genetic algorithm and the deep learning approach.

In future integrated sensing and communication (ISAC)-based vehicular networks, infrastructures such as road-side units (RSUs) are expected to simultaneously support high-efficiency communication and ubiquitous sensing functionalities for multiple vehicles. However, this integration introduces a critical challenge in multi-objective optimization (MOO) to achieve shared sensing and communication precoding (SSCP). To address this challenge, we propose a multi-task learning (MTL) scheme to solve the MOO problem. Specifically, we design a Transformer-based hybrid data-knowledge-driven network (Transformer-HDKDN), which enhances communication learning efficiency by incorporating domain knowledge. Furthermore, we introduce a gradient and uncertainty weighting (GUW) algorithm to dynamically balance the loss functions of the communication and sensing subtasks. Simulation results demonstrate that the proposed MTL scheme outperforms the evaluated schemes in terms of SSCP performance.

Cooperative caching is regarded as one of the most promising technologies for vehicular networks, as it significantly reduces content delivery latency by prestoring popular content at roadside units (RSUs) closer to vehicle users (VUs). However, accurately predicting popular content and subsequently making caching decisions to enhance the Quality of Experience (QoE) remains a complex challenge, with varied content preferences and performance levels across VUs. To address this issue, we propose a fairness-aware collaborative edge caching system that formulates the factors influencing QoE as a multiagent Markov decision process, with the aim of maximizing the long-term system revenue. Furthermore, we design an activity-based VU clustering and weighted prediction model and propose a training framework based on asynchronous federated learning for global model updates. The trained model extracts key features and accurately predicts popular content from historical data. Additionally, we present an attention-enhanced multiagent discrete soft actor-critic algorithm to tackle the complex caching decision problem and promote collaboration among RSUs. Extensive simulation experiments validate the superiority of our solution. Compared with other benchmarks, the proposed method improves service fairness by 50% and increases average system revenue by 16.7%.

The remarkable success of transformers across various fields such as natural language processing and computer vision has paved the way for their applications in automatic modulation classification, a critical component in the communication systems of Internet of Things (IoT) devices. However, it has been observed that transformer-based classification of radio signals is susceptible to subtle yet sophisticated adversarial attacks. To address this issue, we have developed a defensive strategy for transformer-based modulation classification systems to counter such adversarial attacks. In this paper, we propose a novel vision transformer (ViT) architecture by introducing a new concept known as adversarial indicator (AdvI) token to detect adversarial attacks. To the best of our knowledge, this is the first work to propose an AdvI token in ViT to defend against adversarial attacks. Integrating an adversarial training method with a detection mechanism using AdvI token, we combine a training time defense and running time defense in a unified neural network model, which reduces architectural complexity of the system compared to detecting adversarial perturbations using separate models. We investigate into the operational principles of our method by examining the attention mechanism. We show the proposed AdvI token acts as a crucial element within the ViT, influencing attention weights and thereby highlighting regions or features in the input data that are potentially suspicious or anomalous. Through experimental results, we demonstrate that our approach surpasses several competitive methods in handling white-box attack scenarios, including those utilizing the fast gradient method, projected gradient descent attacks and basic iterative method.

This paper explores the potential of employing rate-splitting multiple access to enhance the achievable rate and energy efficiency (EE) of an uplink cell-free massive multiple-input multiple-output (MIMO) system, where the access points (APs) are configured with low-resolution analog-to-digital converters (ADCs) to minimize the hardware expense and power consumption. Taking the large-scale fading decoding, ADC quantization, and imperfect successive interference cancellation into consideration, a rigorous closed-form rate expression is derived within Ricean fading environments. This analytical framework facilitates an in-depth analysis of the rate performance with respect to various system parameters. To quantify the benefits of low-resolution ADCs, a power consumption model is subsequently incorporated into the analysis, facilitating an evaluation of the system’s EE. Furthermore, the optimization of power control coefficients and receiver weights is tackled through the formulation of weighted sum-rate (WSR) and EE maximization problems. Two efficient alternative algorithms are then proposed to determine their optimal solutions. The theoretical propositions and the efficacy of the proposed WSR and EE optimization algorithms are substantiated through comprehensive simulations.

In this paper, the problem of energy-efficient uncrewed aerial vehicle (UAV)-assisted multi-access task offloading is investigated. In the studied system, several UAVs are deployed as edge servers to cooperatively aid task executions for several energy-limited computation-scarce terrestrial user equipments (UEs). An expected energy efficiency maximization problem is then formulated to jointly optimize UAV trajectories, UE local central processing unit (CPU) clock speeds, UAV-UE associations, time slot slicing, and UE offloading powers. This optimization is subject to practical constraints, including UAV mobility, local computing capabilities, mixed-integer UAV-UE pairing indicators, time slot division, UE transmit power, UAV computational capacities, and information causality. To tackle the multi-dimensional optimization problem under consideration, the duo-staggered perturbed actor-critic with modular networks (DSPAC-MN) solution in a multi-agent deep reinforcement learning (MADRL) setup, is proposed and tailored, after mapping the original problem into a stochastic (Markov) game. Time complexity and communication overhead are analyzed, while convergence performance is discussed. Compared to representative benchmarks, e.g., multi-agent deep deterministic policy gradient (MADDPG) and multi-agent twin-delayed DDPG (MATD3), the proposed DSPAC-MN is validated to be able to achieve the optimal performance of average energy efficiency, while ensuring 100% safe flights.

In cellular networks, the performance of cell-edge users is notably deficient, especially for those receiving almost comparable signal strengths from the serving base station (BS) and interfering BS(s). To improve their performance, a cell-edge UAV deployment scheme is proposed, where the UAVs are deployed to hover over these cell-edge users to serve them. Specifically, the locations of BSs are modeled using a Poisson point process (PPP) and the Voronoi cells are formed. We consider two distinct types of cell-edge users positioned at the Voronoi vertices and boundaries, corresponding to the worst-case and boundary users, respectively. Due to the intractable spatial distribution of the interfering UAVs, we utilize the PPP and binomial point process (BPP) approximations to characterize their interference. Subsequently, we derive the success probabilities for these two types of cell-edge users. The results show the similar effectiveness of the two approximate models for the locations of the UAVs. Furthermore, we obtain the asymptotic success probabilities to analyze the performance in high-reliability regimes and propose an effective approximation based on the asymptotic behavior to simplify the analytical expressions. The results highlight the substantial performance enhancement through the proposed UAV deployment scheme for cell-edge users.

Fluid antenna enables position reconfigurability that gives transceiver access to a high-resolution spatial signal and the ability to avoid interference through the ups and downs of fading channels. Previous studies investigated this fluid antenna multiple access (FAMA) approach in a single-cell setup only. In this paper, we consider a cell-free network architecture in which users are associated with the nearest base stations (BSs) and all users share the same physical channel. Each BS has multiple fixed antennas that employ maximum ratio transmission (MRT) to beam to its associated users while each user relies on its fluid antenna system (FAS) on one radio frequency (RF) chain to overcome the inter-user interference. Our aim is to analyze the outage probability performance of such cell-free FAMA network when both large-and small-scale fading effects are considered. To do so, we derive the distribution of the received magnitude for a typical user and then the interference distribution under both fast and slow port switching techniques. The outage probability is finally obtained in integral form in each case. Numerical results demonstrate that in an interference-limited situation, although fast port switching is typically understood as the superior method for FAMA, slow port switching emerges as a more effective solution when there is a large antenna array at the BS. Moreover, it is revealed that FAS at each user can serve to greatly reduce the burden of BS in terms of both antenna costs and CSI estimation overhead, thereby enhancing the scalability of cell-free networks.

In this paper, the problem of energy-efficient unmanned aerial vehicle (UAV)-assisted computation offloading over the Terahertz (THz) spectrum is investigated. In the studied system, several UAVs are deployed as edge servers to aid task executions for multiple energy-limited computation-scarce terrestrial user equipments (UEs). Then, an expected energy efficiency maximization problem is formulated, aiming to jointly optimize UAVs’ trajectories, UEs’ local central processing unit (CPU) clock speeds, UAV-UE associations, time slot slicing, and UEs’ offloading powers. To tackle the considered multi-dimensional optimization problem, the duo-staggered perturbed actor-critic with modular networks (DSPAC-MN) solution in a multi-agent deep reinforcement learning (MADRL) setup, is proposed and tailored, after mapping the original problem into a stochastic (Markov) game. Compared to representative benchmarks in simulations, e.g., multi-agent deep deterministic policy gradient (MADDPG) and multi-agent twin-delayed DDPG (MATD3), the proposed DSPAC-MN can achieve the optimal performance of average energy efficiency, while ensuring 100% safe flights.

Massive multiple input multiple output (mMIMO) is a promising technology for the next-generation wireless communication systems, yet accurate channel modeling is still a challenging problem. Recent field experiments revealed that, the channel is non-stationary in space, time and frequency (STF) domains. Meanwhile, it is also disclosed that, the channel changes in a smooth way. Namely, the channel should be consistent in space and time domains. This paper proposes a three-dimensional (3D) space-time consistency modeling method for the STF non-stationary mMIMO environments. Innovatively, a growth-curve based power attenuation factor (PAF) is introduced to evolve the birth and death (BD) process of the scattering clusters, so that the space-time consistency can be guaranteed. In validation experiments, the modeled channel characteristics are in good agreements with the measured ones, indicating that the proposed method is suitable for mMIMO channel modelling.

In cellular networks, the performance of cell-boundary users with almost equal distance from the serving base station (BS) and the nearest interfering BS(s) is extremely poor. In order to improve the performance of these users, this paper proposes a cell-boundary UAV deployment scheme. To quantify the performance enhancement, the locations of BSs are modeled as a Poisson point process forming Voronoi cells. Two distinct types of cell-boundary users located at the vertices and boundaries of Voronoi cells are served by a UAV-mounted BS hovering over them, and the success probabilities of these two types of users are derived with the stochastic geometry tool. Furthermore, the asymptotic success probabilities are obtained to analyze the performance in high-reliability regime and an effective approximation is proposed based on the asymptotic behavior to simplify the performance evaluation. Finally, the re-sults highlight the substantial performance enhancement achieved through UAV-assisted communication for the cell-boundary users.

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.

Energy-efficient computation is an inevitable trend for unmanned aerial vehicles (UAV)-enabled wireless powered mobile edge computing (MEC), while it has not been investigated when the hybrid passive and active transmissions (ATs) are considered for Internet of Things (IoT) nodes’ task offloading. In this paper, we study the computation energy efficiency (EE) fairness among IoT nodes in a UAV-enabled wireless powered MEC network with hybrid passive and ATs, where the UAV serves as a dynamic energy source to support IoT nodes for backscatter communication (BackCom) and AT. Specifically, we formulate an optimization problem to maximize the computation EE of the worst IoT node by jointly optimizing the UAV’s transmit power and trajectory, the IoT nodes’ BackCom time and reflection coefficients, the IoT nodes’ AT power and time, as well as the IoT nodes’ local computing time and frequencies. The formulated problem is highly non-convex and difficult to be solved optimally. To address it, we first obtain the closed-form expressions for the UAV’s transmit power and the IoT nodes’ local computing time by means of the proof by contradiction to simplify the problem, and then propose a Dinkelbach-based iterative algorithm to obtain the solution of other optimization variables. Specifically, based on the Dinkelbach’s method, the original fractional problem is transformed into the problem with the subtractive objective function. Then we further decouple the transformed problem into two subproblems based on the block-coordinated-decent (BCD) method and solve the transformed problem by the proposed BCD-based iterative algorithm, where the above two subproblems are solved by means of the existing convex optimization tools and the proposed successive convex approximation (SCA)-based iterative algorithm alternatively. Simulation results show that the proposed algorithms have a fast rate of convergence and that the proposed scheme outperforms other baseline schemes in terms of the computation EE fairness.

Due to the threat of various jamming attacks in wireless communications, the efficient and general anti-jamming schemes are required to guarantee communication the quality of wireless networks. To this end, this paper proposes the efficient index modulation based frequency hopping spread spectrum (IM-FHSS) scheme. Unlike the classical IM-FHSS which only considered the reactive jamming, the efficient IM-FHSS scheme extends the anti-jamming capacity to various jamming models. Specifically, we firstly derive the closed-form expressions of bit error rate (BER) performance of the IM-FHSS under three jamming models in an additional white gaussian noise (AWGN) channel and a Rayleigh fading channel, which enables the design of anti-jamming strategies. Then, the unified anti-jamming framework is provided for designing the efficient anti-jamming system. Based on the framework, we further provide the strategies of the efficient IM-FHSS for reactive jamming, constant and random jamming. Simulations show that the theoretical derivations match well with the simulated results, which validates the performance analysis. Moreover, the efficient IM-FHSS has demonstrated the superior anti-jamming performance for three typical jamming models, which is also energy-efficient and reliable for achieving targeted bit error rate (BER) performance.

Space-air–ground integrated network (SAGIN) slicing has been studied for supporting diverse applications, which consists of the terrestrial layer (TL) deployed with base stations (BSs), the aerial layer (AL) deployed with unmanned aerial vehicles (UAVs), as well as the space layer (SL) deployed with low earth orbit (LEO) satellites. The capacity of each SAGIN component is limited, and efficient and synergic load balancing (LB) has not been fully considered yet in the exiting literature. For this motivation, we originally propose a priority-based LB scheme for SAGIN slicing, where the AL and SL are merged into one layer, namely non-TL (NTL). First, three typical slices (i.e., high-throughput, low-delay, and wide-coverage slices) are built under the same physical SAGIN. Then, a priority-based cross-layer LB approach is introduced, where the users will have the priority to access the terrestrial BS, and different slices have different offloading priorities. More specifically, the overloaded BS can offload the users of low-priority slices to the NTL preferentially. Furthermore, the throughput, delay, and coverage of the corresponding slices are jointly optimized by formulating a multiobjective optimization problem (MOOP). In addition, due to the independence and priority relationship of TL and NTL, the above MOOP is decoupled into two sub-MOOPs. Finally, we customize a two-layer multiagent deep deterministic policy gradient (MADDPG) algorithm for solving the two subproblems, which first optimizes the user-BS association and resource allocation at the TL, then it determines the UAVs’ position deployment, users-UAV/LEO satellite association, and resource allocation at the NTL. The reported simulation results show the advantages of our proposed LB scheme and show that our proposed algorithm outperforms the benchmarkers.

The rapid development of radio access network (RAN) slicing and control- and user-plane separation (CUPS) has created a new paradigm for future networks, namely, CUPS-based RAN slicing. In this article, we formulate the utility optimization problems of the CUPS-based RAN slicing system and propose a Lyapunov-based deep reinforcement learning (L-DRL) framework to solve them. Specifically, we propose that the control plane (CP) and user plane (UP) slices should control their respective power and subcarrier resources. First, we provide coverage-driven slices in the CP for coverage control and data-driven slices in the UP for diverse user requests, where we consider the influence of coverage-driven slices on data-driven slices. Second, we define the system’s utilities as income minus cost, and we formulate the utility maximization problem of the UP as a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard because it considers both continuous actions (densities deployment and power allocation) and discrete action (subcarrier allocation). Furthermore, we design an alternating optimization method for the CP and UP based on the densities of deployment. Finally, we develop a novel framework for mixed-action optimization problems and propose a specific Lyapunov-based asynchronous advantage actor–critic (L-A3C) algorithm. Simulation results demonstrate that our proposed Lyapunov-based A3C (L-A3C) algorithm outperforms the standard A3C algorithm in terms of the convergence while achieving higher performance than Lyapunov optimization. Moreover, our proposed CUPS-based RAN slicing scheme surpasses the benchmark RAN slicing schemes in terms of the achievable rate and delay.

Most of works on backscatter communication (BackCom) assume the availability of a direct link from the BackCom transmitter to its receiver and the long-packet transmission for BackCom. However, the above assumptions may be inapplicable in some practical Internet of Things (IoT) applications, e.g., industrial automation. Motivated by this, this paper proposes and studies a relay assisted short-packet BackCom network, where a full-duplex relay (R) with two antennas and an energy self-sustaining IoT node form a monostatic short-packet BackCom paradigm in the first phase while R forwards the IoT’s short-packet information to the information receiver (IR) in the second phase via a decode-and-forward (DF) or amplify-and-forward (AF) relaying protocol. For a given DF or AF relaying protocol, we propose to optimize the power allocation factor of two antennas at R in the second phase to minimize the IoT node’s average block error rate (BLER), and derive the optimal solution in the closed form. With the optimal power allocation factor, we derive the analytical expressions of the IoT node’s average BLER under the DF and AF protocols while considering the residual self-interference at R and the energy causal constraint at the IoT node. Simulation results verify the correctness of the derived expressions and reveal the impacts of various parameters such as the IoT node’s short-packet blocklength and power reflection coefficient on the average BLER. It is found that when the short-packet blocklength increases, the average BLER decreases until it reaches a certain value.

As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customized services in the global space–air–ground-integrated network (SAGIN) with diverse resource constraints. In this article, we dynamically consider three typical classes of radio access network (RAN) slices, namely, high-throughput slices, low-delay slices and wide-coverage slices, under the same underlying physical SAGIN. The throughput, the service delay, and the coverage area of these three classes of RAN slices are jointly optimized in a nonscalar form by considering the distinct channel features and service advantages of the terrestrial, aerial, and satellite components of acrshortpl SAGIN. A joint central and distributed multiagent deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving the above problem to obtain the Pareto-optimal solutions. The algorithm first determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the interslice subchannel and power sharing by relying on a centralized unit. Then, it optimizes the intraslice subchannel and power allocation, and the virtual base station (vBS)/vUAV/virtual low Earth orbit (vLEO) satellite deployment in support of three classes of slices by three separate distributed units. Simulation results verify that the proposed method approaches the Pareto-optimal exploitation of multiple RAN slices, and outperforms the benchmarkers.

This paper investigates the optimization of the probabilistically robust transmit beamforming problem with channel uncertainties in the multiuser multiple-input single-output (MISO) downlink transmission. This problem poses significant analytical and computational challenges. Currently, the state-of-the-art optimization method relies on convex restrictions as tractable approximations to ensure robustness against Gaussian channel uncertainties. However, this method not only exhibits high computational complexity and suffers from the rank relaxation issue but also yields conservative solutions. In this paper, we propose an unsupervised deep learning-based approach that incorporates the sampling of channel uncertainties in the training process to optimize the probabilistic system performance. We introduce a model-driven learning approach that defines a new beamforming structure with trainable parameters to account for channel uncertainties. Additionally, we employ a graph neural network to efficiently infer the key beamforming parameters. We successfully apply this approach to the minimum rate quantile maximization problem subject to outage and total power constraints. Furthermore, we propose a bisection search method to address the more challenging power minimization problem with probabilistic rate constraints by leveraging the aforementioned approach. Numerical results confirm that our approach achieves non-conservative robust performance, higher data rates, greater power efficiency, and faster execution compared to state-of-the-art optimization methods.

In unmanned space exploration, the cooperation among space robots requires advanced communication techniques. In this paper, we propose a communication optimization scheme for a specific cooperation system named the “mother-daughter system”. In this setup, the mother spacecraft orbits the planet, while daughter probes are distributed across the planetary surface. During each control cycle, the mother spacecraft senses the environment, computes control commands and distributes them to daughter probes for actions. They synergistically form sensing-communication-computing-control ($\mathbf {SC^{3}}$) loops. Given the indivisibility of the$\mathbf {SC^{3}}$loop, we optimize the mother-daughter downlink for closed-loop control. The optimization objective is the linear quadratic regulator (LQR) cost, and the optimization parameters are the block length and transmit power. To solve the nonlinear mixed-integer problem, we first identify the optimal block length and then transform the power allocation problem into a tractable convex problem. We further derive the approximate closed-form solutions for the proposed scheme and two communication-oriented schemes: the max-sum rate scheme and the max-min rate scheme. On this basis, we analyze their power allocation principles. In particular, for time-insensitive control tasks, we find that the proposed scheme demonstrates equivalence to the max-min rate scheme. These findings are verified through simulations.

Direction of arrival (DOA) estimation is widely used in many applications. Traditional DOA estimation generally adopts the second-order statistics and the uniform linear array (ULA) structure. However, the second-order statistics and the uniform array structure restrict the number of DOAs that can be accurately estimated. In addition, the performance of the second-order statistics-based methods is sensitive to noise. As the wireless environment is becoming increasingly complex with the advent of the 5G era, the propagation channel can be composed of numerous paths. When the number of paths exceeds the size of the antenna array, DOA estimation becomes an under-determined problem, which the second-order statistics-based traditional methods fail to deal with. In order to address the under-determined DOA estimation problem, this paper proposes a method based on higher-order statistics and non-uniform array structure. Higher-order statistics-based methods can not only expand the array aperture, but also suppress the additive Gaussian noise. However, if combined with a uniform array structure, the degrees of freedom of the expanded array is limited. Therefore, we further adopt a non-uniform array structure and optimize its structure. Consequently, the proposed method is capable of achieving$M^{2}$-level DOA estimation with an M-element array, while simultaneously providing good robustness to noise. For instance, using the proposed method, the DOAs of 20 incoming wave directions can be accurately estimated with a 6-antenna non-uniform array when the signal-to-noise ratio is as low as 0 dB.

Traditional physical layer secure beamforming is achieved via precoding before signal transmission using channel state information (CSI). However, imperfect CSI will compromise the performance with imperfect beamforming and potential information leakage. In addition, multiple RF chains and antennas are needed to support the narrow beam generation, which complicates hardware implementation and is not suitable for resource-constrained Internet-of-Things (IoT) devices. Moreover, with the advancement of hardware and artificial intelligence (AI), low-cost and intelligent eavesdropping to wireless communications is becoming increasingly detrimental. In this paper, we propose a multi-carrier based multi-band waveform-defined security (WDS) framework, independent from CSI and RF chains, to defend against AI eavesdropping. Ideally, the continuous variations of sub-band structures lead to an infinite number of spectral features, which can potentially prevent brute-force eavesdropping. Sub-band spectral pattern information is efficiently constructed at legitimate users via a proposed chaotic sequence generator. A novel security metric, termed signal classification accuracy (SCA), is used to evaluate the security robustness under AI eavesdropping. Communication error probability and complexity are also investigated to show the reliability and practical capability of the proposed framework. Finally, compared to traditional secure beamforming techniques, the proposed multi-band WDS framework reduces power consumption by up to six times.

In this paper, we investigate channel estimation in a reconfigurable intelligent surface (RIS) assisted multi-user network while taking the mobility of users into consideration. Based on a time-varying channel model, we utilize Kalman filter (KF) that is able to exploit temporal correlation to track cascaded channel. In order to maintain a relatively low pilot overhead, we present a multiple sub-phases based transmission protocol where the number of pilot sequences in each sub-phase is less than the number of users, i.e., pilot contamination exists. For the sake of practicality, we directly utilize discrete Fourier transform (DFT) matrix as phase shift matrix. We analyze normalized mean square error and provide some asymptotic results. A more practical scenario with hardware impairments (HWI) at the transceiver and the RIS is also considered. Since HWI is also part of the measurement matrix and is unknown to the base station, we propose a joint estimation of the channel and HWI. Under this joint estimation framework, the underlying state space model becomes nonlinear. We develop an extended KF (EKF) algorithm to tackle the nonlinearity through which the model can be linearized. Numerical results show that the proposed KF and EKF algorithms outperform benchmark schemes under various scenarios.

In this work, we employ a constrained risk-sensitive deep reinforcement learning (CRS-DRL) approach for joint scheduling in a dynamic multiplexing scenario involving enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC). Our scheduling policy minimizes the adverse impact of URLLC puncturing on eMBB users while satisfying URLLC requirements. Conventional DRL-based algorithms for eMBB/URLLC scheduling prioritize maximizing the expected return. However, for URLLC mission-critical applications, it is crucial to explicitly avoid catastrophic scheduling failures associated with the long tail of the reward distribution. Therefore, robust management of such uncertainties and risks is imperative. Our proposed CRS-DRL algorithm incorporates the conditional Value-at-Risk (CVaR) as the risk criterion for optimization. A URLLC queuing mechanism is considered to decrease the URLLC drops and increase eMBB throughput compared to the instant scheduling policy. Our architecture is based on the actor-critic model but considers a transfer function to obtain feasible solutions of the unconstrained actor network, and the critic predicts the entire distribution over future returns instead of simply the expectation. Numerical results indicate that our CRS-DRL algorithm, under varying CVaR levels, achieves similar expected returns but reduces long-tail behavior for long-term rewards compared to the risk-neutral approach.

This paper investigates quantum machine learning to optimize the beamforming in a multiuser multiple-input single-output downlink system. We aim to combine the power of quantum neural networks and the success of classical deep neural networks to enhance the learning performance. Specifically, we propose two hybrid quantum-classical neural networks to maximize the sum rate of a downlink system. The first one proposes a quantum neural network employing parameterized quantum circuits that follows a classical convolutional neural network. The classical neural network can be jointly trained with the quantum neural network or pre-trained leading to a fine-tuning transfer learning method. The second one designs a quantum convolutional neural network to better extract features followed by a classical deep neural network. Our results demonstrate the feasibility of the proposed hybrid neural networks, and reveal that the first method can achieve similar sum rate performance compared to a benchmark classical neural network with significantly less training parameters; while the second method can achieve higher sum rate especially in presence of many users still with less training parameters. The robustness of the proposed methods is verified using both software simulators and hardware emulators considering noisy intermediate-scale quantum devices.

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.

This article proposes a novel unmanned aerial vehicle (UAV)-enabled wireless-powered mobile edge computing (WP-MEC) network, where several Internet of Things (IoT) nodes use the energy harvested from the UAV’s radio frequency signals to support the local computation and the hybrid active–passive communications-based task offloading. Two weighted sum computation bits (WSCB) maximization problems are formulated under the partial and binary offloading, respectively, by jointly optimizing the local computing frequencies and time, the IoT nodes’ reflection coefficients, the IoT nodes’ transmit powers, the UAV’s trajectory, etc., subject to the quality-of-service and energy-causality constraints per IoT node, the speed constraint of the UAV, etc. Since the formulated problems are highly nonconvex, two iterative algorithms are proposed to solve the formulated problems under two modes. Simulation results demonstrate that the proposed iterative algorithms have a fast convergence rate, and the proposed schemes achieve higher WSCB than several baseline schemes.

Due to great success of transformers in many applications, such as natural language processing and computer vision, transformers have been successfully applied in automatic modulation classification. We have shown that transformer-based radio signal classification is vulnerable to imperceptible and carefully crafted attacks called adversarial examples. Therefore, we propose a defense system against adversarial examples in transformer-based modulation classifications. Considering the need for computationally efficient architecture particularly for Internet of Things (IoT)-based applications or operation of devices in an environment where power supply is limited, we propose a compact transformer for modulation classification. The advantages of robust training such as adversarial training in transformers may not be attainable in compact transformers. By demonstrating this, we propose a novel compact transformer that can enhance robustness in the presence of adversarial attacks. The new method is aimed at transferring the adversarial attention map from the robustly trained large transformer to a compact transformer. The proposed method outperforms the state-of-the-art techniques for the considered white-box scenarios, including the fast gradient method and projected gradient descent attacks. We have provided reasoning of the underlying working mechanisms and investigated the transferability of the adversarial examples between different architectures. The proposed method has the potential to protect the transformer from the transferability of adversarial examples.

To develop the energy efficient of 6G networks, the fog radio access network (F-RAN) is expected to meet various use cases of high-performance mobile services. Although network virtualization greatly enhances the flexibility to accommodate various services, the interaction and optimization between delay and energy efficiency (EE) in virtual slicing (VS)-based F-RAN have not been well studied. To accomplish the EE and delay optimization of VS of F-RAN. The key technical challenges lie in the construction of new network architecture, the integration and optimization of radio, caching, and computing 3-D resources, reducing the algorithm’s complexity, and simulation verification. We first design a novel network architecture based on VS and fog computing. The VS method in F-RAN to embed VS assembles virtual radio, caching, and computing resources into physical substrates, transforming nonconvex problems into convex optimization problems by transforming constraints and using Lyapunov optimization methods. We further propose a virtual resource allocation optimization algorithm based on EE. To reduce the complexity of the algorithm, a low-complexity EE optimization algorithm is further proposed for virtual resource allocation. The simulation results show that the low-complexity virtual resource allocation EE optimization algorithm proposed in this article has better performance than the existing fog access network resource allocation methods. The EE is further improved by about 30% under the condition that the guaranteed delay threshold is two slots (i.e., 1 ms).

With the increasing demand of high data rate and massive access in both ultra-dense and industrial Internet-of-things networks, spectral efficiency (SE) and energy efficiency (EE) are regarded as two important and inter-related performance metrics for future networks. In this paper, we investigate a novel integration of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) into cellular systems to achieve a desirable tradeoff between SE and EE. Different from the commonly used passive RIS, we adopt reflection elements with active load to improve a newly defined metric, called resource efficiency (RE), which is capable of striking a balance between SE and EE. This paper focuses on the RE optimization by jointly designing the base station (BS) transmit precoding and RIS beamforming (BF) while guaranteeing the transmit and forward power budgets of the BS and RIS, respectively. To efficiently tackle the challenges for solving the RE maximization problem due to its fractional objective function, coupled optimization variables, and discrete coefficient constraint, the formulated nonconvex problem is solved by proposing a two-stage optimization framework. For the outer stage problem, a quadratic transformation is used to recast the fractional objective into a linear form, and a closed-form solution is obtained by using auxiliary variables. For the inner stage problem, the system sum rate is approximated into a linear function. Then, an alternating optimization (AO) algorithm is proposed to optimize the BS precoding and RIS BF iteratively, by utilizing the penalty dual decomposition (PDD) method. Simulation results demonstrate the superiority of the proposed design compared to other benchmarks.

Integrating reconfigurable intelligent surface (RIS) into cell-free massive multiple-input multiple-output (MIMO) is a promising approach to enhance the coverage quality, spectral efficiency (SE), and energy efficiency. In this paper, an RIS-assisted cell-free massive MIMO downlink system suffering from the transceiver hardware impairments (T-HWIs) is investigated. To improve the accuracy of the direct estimation (DE) scheme, a modified ON/OFF estimation (MOE) with moderate pilot overhead is proposed. Relying on the knowledge of imperfect channel state information, we derive closed-form expressions of the lower-bound achievable SE with T-HWIs under both DE and MOE schemes. The closed-form results facilitate the investigation of how RIS improves the downlink SE under various system settings and allow us to explore the trade-off strategies between using more hardware-impaired APs and low-cost RISs in terms of the downlink SE and power consumption. Numerical results validate the theoretical analysis and show that the proposed MOE scheme outperforms the DE scheme in terms of the downlink SE. Moreover, the benefits of introducing RIS into hardware-impaired cell-free massive MIMO systems are also illustrated.

This paper studies the allocation of shared resources between ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) in the emerging 5G and beyond cellular networks. In this paper, we design a unique queuing mechanism for the joint eMBB/URLLC system. The aim is to flexibly schedule URLLC traffic to enhance the total eMBB throughput and the reliability of URLLC packets (i.e., the probability of not dropping URLLC packets in each mini-slot) while maintaining a satisfactory transmission latency as per the 3GPP requirements. Precisely, by deriving the steady-state probabilities of URLLC queue backlog analytically, we formulate a stochastic optimization problem to maximize the total normalized eMBB throughput and the URLLC utility. Due to the stochastic nature of the objective function, it is expensive to evaluate it for any set of inputs, and thus the Bayesian optimization is applied to obtain the optimal results of such a black-box objective function. Numerical results demonstrate that the proposed queuing mechanism never violates the latency requirement of the URLLC services but improves the reliability. It also enhances the total normalized eMBB throughput as compared to the method without queuing.

Deep Learning has attracted significant interests in wireless communication design problems. However, recent studies discovered that the deep neural network is vulnerable to adversarial attacks in the sense that a carefully designed and imperceptible perturbation to the input of the neural network could mislead the prediction of the neural network. In this paper, motivated by attractive classification performance of the transformer based neural networks, we analyse the vulnerability and robustness of the transformer against adversarial attacks in modulation classification scenarios. Using real datasets, we demonstrate that the transformer can achieve higher accuracy as compared to a convolutional neural network in the presence of adversarial attacks.

The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN)-based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds.

Sparse code multiple access (SCMA) is a kind of code-domain nonorthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multiaccess edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. In this article, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. Specifically, a system utility is first used to calculate the weighted energy consumption and task execution latency. The initial utility minimization problem is nonconvex and then can be subdivided into two tractable subproblems by fixing task offloading decisions, namely, optimal local computing via CPU frequency scheduling and optimal edge computing via the SCMA codebook assignment, subcarrier power allocation, and MEC server computing resources distribution. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed. Moreover, we come up with CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation (CRA) of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and task offloading can achieve a good compromise between time delay and energy consumption for IoT devices. It is demonstrated that the proposed strategy has a remarkable advantage compared to the previous SCMA-MEC schemes.

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.

The unmanned aerial vehicle (UAV) technique provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards downlink signals to user terminals (UTs) distributed over an area while the other one collects uplink data. The conditional average achievable rates, as well as their lower bounds, of both the uplink and downlink transmission are derived considering the active probability of UTs and the service queues of two UAVs. In addition, a problem aiming to maximize the energy efficiency of the whole system is formulated, which takes into account communication related energy and propulsion energy consumption. Then, we develop a novel multi-agent Q-learning (MA-QL) algorithm to maximize the energy efficiency, through optimizing the trajectory and transmit power of the UAVs. Finally, simulation results are conducted to verify our analysis and examine the impact of different parameters on the downlink and uplink achievable rates, UAV energy consumption, and system energy efficiency. It is demonstrated that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes.

Simultaneous wireless information and power transfer (SWIPT) has long been proposed as a key solution for charging and communicating with low-cost and low-power devices. However, the employment of radio frequency (RF) signals for information/power transfer needs to comply with international health and safety regulations. In this article, we provide a complete framework for the design and analysis of far-field SWIPT under safety constraints. In particular, we deal with two RF exposure regulations, namely, the specific absorption rate (SAR) and the maximum permissible exposure (MPE). The state of the art regarding SAR and MPE is outlined together with a description as to how these can be modeled in the context of communication networks. We propose a deep learning approach for the design of robust beamforming subject to specific information, energy harvesting, and SAR constraints. Furthermore, we present a thorough analytical study for the performance of large-scale SWIPT systems, in terms of information and energy coverage under MPE constraints. This work provides insights with regards to the optimal SWIPT design and the potentials from the proper development of SWIPT systems under health and safety restrictions.

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.

This paper investigates the secure transmission in downlink cell-free massive multiple-input multiple-output (MIMO) systems in the presence of an active multi-antenna eavesdropper (Eve) over Rician fading channels, assuming that each access point (AP) possesses multiple antennas which are connected with low-resolution digital-to-analog converters (DACs). Closed-form expressions of the achievable secrecy rate relied on the additive quantization noise model are derived. Based on these analytical results, we quantify the impacts of key system parameters, such as the antenna array number, DAC resolution, Rician$\mathcal K$-factor, and balance factor between data and artificial noise power on secrecy enhancement. Several interesting insights are attained by assuming that Eve can or cannot perfectly remove inter-mobile-terminal interference. Moreover, we also propose a power control algorithm that maximizes the achievable secrecy rate, which can be represented as a series of second-order-cone programs for which efficient solvers exist. All the theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments.

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.

This paper studies the fast adaptive beamforming for the multiuser multiple-input single-output downlink. Existing deep learning-based approaches assume that training and testing channels follow the same distribution which causes task mismatch, when the testing environment changes. Although meta learning can deal with the task mismatch, it relies on labelled data and incurs high complexity in the pre-training and fine tuning stages. We propose a simple yet effective adaptive framework to solve the mismatch issue, which trains an embedding model as a transferable feature extractor, followed by fitting the support vector regression. Compared to the existing meta learning algorithm, our method does not necessarily need labelled data in the pre-training and does not need fine-tuning of the pre-trained model in the adaptation. The effectiveness of the proposed method is verified through two well-known applications, i.e., the signal to interference plus noise ratio balancing problem and the sum rate maximization problem. Furthermore, we extend our proposed method to online scenarios in non-stationary environments. Simulation results demonstrate the advantages of the proposed algorithm in terms of both performance and complexity. The proposed framework can also be applied to general radio resource management problems.

Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.

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.

Advantages of deep learning over traditional methods have been demonstrated for radio signal classification in the recent years. However, various researchers have discovered that even a small but intentional feature perturbation known as adversarial examples can significantly deteriorate the performance of the deep learning based radio signal classification. Among various kinds of adversarial examples, universal adversarial perturbation has gained considerable attention due to its feature of being data independent, hence as a practical strategy to fool the radio signal classification with a high success rate. Therefore, in this paper, we investigate a defense system called neural rejection system to propose against universal adversarial perturbations, and evaluate its performance by generating white-box universal adversarial perturbations. We show that the proposed neural rejection system is able to defend universal adversarial perturbations with significantly higher accuracy than the undefended deep neural network.

This paper studies the long-standing problem of outage-constrained robust downlink beamforming in multi-user multi-antenna wireless communications systems. State of the art solutions have very high computational complexity which poses a major challenge to meet the latency requirement in the future communications systems, e.g., the targeted 1 ms end-to-end latency in 5G. By transforming the robust beamforming problem into a deep learning problem, we propose a new unsupervised data augmentation based deep neural network (DNN) method to address the outage-constrained robust beamforming problem with uncertain channel state information at the transmitter. Simulation results demonstrate that our proposed data augmentation based DNN method for the robust beamforming problem is capable to satisfy the required outage probability, and more importantly, compared to the benchmark Bernstein-Type Inequality (BTI) method, it is less conservative, more power efficient and several orders of magnitude faster.

This paper studies the deep learning based adaptive downlink beamforming solution for the signal-to-interference-plus-noise ratio balancing problem. Adaptive beamforming is an important approach to enhance the performance in dynamic wireless environments in which testing channels have different distributions from training channels. We propose an adaptive method to achieve fast adaptation of beamforming based on the principle of meta learning. Specifically, our method first learns an embedding model by training a deep neural network as a transferable feature extractor. In the adaptation stage, it fits a support vector regression model using the extracted features and testing data of the new environment. Simulation results demonstrate that compared to the state of the art meta learning method, our proposed algorithm reduces the complexities in both training and adaptation processes by more than an order of magnitude, while achieving better adaptation performance.

Abstract Scarcity of frequency spectrum is one of the main issues in wireless communications. Cognitive radio networks (CRNs) have been considered as an effective way of improving the spectrum efficiency by opportunistically using the spectrum resources through appropriate cooperation between primary and secondary networks. Exploitation of content caching in CRNs can enhance the system performance and reduce the backhaul cost and delay. In this paper, we propose a combined caching strategy and base station coordination in CRNs to achieve a proper balance between the signal cooperation gain and the content diversity gain. Depending on the availability and placement of the requested content, we propose a zero‐forcing coordinated beamforming technique to simultaneously transmit the most popular contents that are cached in every secondary base station and achieve the signal cooperation multiplexing gains, also we propose a maximum ratio transmission technique to deliver the less popular contents which are cached in different secondary base stations and achieve the caching diversity gain. Enumeration of the solution space search is used to obtain the optimal cache solution. Numerical results show that our proposed solution outperforms the cooperative caching and transmission solution proposed where only a maximum ratio transmission technique is used.

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.

In the Fog Radio Access Networks (F-RANs), the local storage and computing capability of Fog Access Points (FAPs) provide new communication resources to address the latency and computing constraints for delay-sensitive applications. To achieve the ultra-low latency, a novel joint user association and channel allocation scheme is proposed in this paper, where the FAPs are clustered from a user-centric perspective. The delay performance is improved regarding both the control signaling procedure and the data transmission procedure. Specifically, the multiple access interference (MAI) between users is analyzed, where the closed-form expression for the effective rate of a typical user with multiple FAP connections and arbitrary interfering users is obtained. With the consideration of MAI, the proposed distributed joint user association and channel allocation algorithm provides a guaranteed delay violation probability. Moreover, the distributed algorithm can be conducted on individual FAPs, whose calculation is simplified by look-up tables. Simulation results show that the proposed algorithm is capable of providing statistical delay performance guarantee including both average delay and delay bound violation probability, which demonstrates its superiority in supporting delay-sensitive applications in F-RANs.

This article studies fast adaptive beamforming optimization for the signal-to-interference-plus-noise ratio balancing problem in a multiuser multiple-input single-output downlink system. Existing deep learning based approaches to predict beamforming rely on the assumption that the training and testing channels follow the same distribution which may not hold in practice. As a result, a trained model may lead to performance deterioration when the testing network environment changes. To deal with this task mismatch issue, we propose two offline adaptive algorithms based on deep transfer learning and meta-learning, which are able to achieve fast adaptation with the limited new labelled data when the testing wireless environment changes. Furthermore, we propose an online algorithm to enhance the adaptation capability of the offline meta algorithm in realistic non-stationary environments. Simulation results demonstrate that the proposed adaptive algorithms achieve much better performance than the direct deep learning algorithm without adaptation in new environments. The meta-learning algorithm outperforms the deep transfer learning algorithm and achieves near optimal performance. In addition, compared to the offline meta-learning algorithm, the proposed online meta-learning algorithm shows superior adaption performance in changing environments.

This paper studies the problem of trajectory design for unmanned aerial vehicle (UAV)-assisted emergency communications, where the ground base station (BS) may be no longer functioning and the UAV acts as an aerial BS to provide emergency communication services to the ground users. In the event of emergency situations, the user distribution and the geographical features of the target area may have changed dramatically while urgent demand for communications are raised by the surviving ground users. In this paper, we model UAV trajectory design problem as a deep reinforcement learning (DRL) process and propose to adopt transfer learning to leverage previously learned knowledge so as to boost up the learning procedure. Simulation results validate that with limited interactions with the environment, the UAV can rapidly and effectively adapt its trajectory to the new environment and achieve much faster convergence speed than DRL based design.

The authors investigate physical layer security design, which employs a random linear network coding with opportunistic relaying and jamming to exploit the secrecy benefit of both source and relay transmissions. The proposed scheme requires the source to transmit artificial noise along with a confidential message. Moreover, in order to further improve the dynamical behaviour of the network against an eavesdropping attack, aggregated power controlled transmissions with optimal power allocation strategy is considered. The network security is accurately characterised by the probability that the eavesdropper will manage to intercept a sufficient number of coded packets to partially or fully recover the confidential message.

Caching is a promising solution for the cloud radio access network (Cloud‐RAN) to mitigate the traffic load problem in the fronthaul links. Multiuser downlink beamforming plays an important role in efficient utilisation of spectrum and transmission power while satisfying the user's quality of service requirements. When the number of users exceeds the serving capacity of the network, certain users will have to be dropped or rescheduled. This is normally achieved by appropriate admission control mechanisms. Introducing local storage or cache at the remote radio heads where some popular contents are cached, the authors propose beamforming and admission control techniques for cache‐enabled Cloud‐RAN in the downlink. This minimises the total network cost including power and fronthaul cost while admitting as many users as possible. They formulate this multi‐objective optimisation problem as a single objective optimisation problem. The original problem, which is a mixed‐integer non‐linear programme, is first converted to the mixed‐integer second‐order cone programming form. The branch and bound algorithm is then used to determine the optimal and suboptimal solutions. A simulation study has been conducted to assess the performance of both methods.

In the era of industrial 4.0, industrial Internet of Things (IIoT) has brought essential changes to human society. For IIoT, communication in network can be defined as the basic condition for further development and integrated information exchange. In this way, cached-enabled heterogeneous industrial network is necessary to be optimized. In this paper, we consider the optimal geographical placement of contents in cache-enabled heterogeneous networks to minimize the total missing probability. And the probability represents that typical user cannot find requested file in the nearby base stations (BSs). In contract to existing works which only concern content placement, we jointly optimize content placement at BSs and activation densities of BSs of different tiers subject to the cache size limits and the constraint on the BSs energy consumption cost. In addition, the user distribution in this work is modeled by a homogeneous Poisson Point Process. We prove that the original optimization problem can be transformed to a convex problem. The convexity of the optimization problem allows us to apply the KKT conditions to derive useful analytical results of the optimal solution. Based on this, we propose a low-complexity near-optimal algorithm to find the approximated content placement probabilities. We further extend the optimization to heterogeneous networks with the user distribution modeled by the modified Cluster Process. Extensive simulation results show the superior performance of joint optimization of content placement and BSs activation densities compared to only optimizing content placement.

Reliable and efficient networks are the trend for next-generation wireless communications. Recent improved hardware technologies - known as Large Intelligent Surfaces (LISs) - have decreased the energy consumption of wireless networks, while theoretically being capable of offering an unprecedented boost to the data rates and energy efficiency (EE). In this paper, we use stochastic geometry to provide performance analysis of a realistic two-step user association based millimeter wave (mmWave) networks consisting of multiple users, transmitters and one-hop reflection from a LIS. All the base stations (BSs), users and LISs are equipped with multiple uniform linear antenna arrays. The results confirm that LIS-assisted networks significantly enhance capacity and achieve higher optimal EE as compared to traditional systems when the density of BSs is not large. Moreover, there is a trade-off between the densities of LIS and BS when there is a total density constraint. It is shown that the LISs are excellent supplements for traditional cellular networks, which enormously enhance the average rate and area spectral efficiency (ASE) of mmWave networks. However, when the BS density is higher than the LIS density, the reflected interference and phase-shift energy consumption will limit the performance of LIS-assisted networks, so it is not necessary to employ the LIS devices.

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

This paper investigates the optimal transmit beamforming design of simultaneous wireless information and power transfer (SWIPT) in the multiuser multiple-input-single-output (MISO) downlink with specific absorption rate (SAR) constraints. We consider the power splitting technique for SWIPT, where each receiver divides the received signal into two parts: one for information decoding and the other for energy harvesting with a practical non-linear rectification model. The problem of interest is to maximize as much as possible the received signal-to-interference-plus-noise ratio (SINR) and the energy harvested for all receivers, while satisfying the transmit power and the SAR constraints by optimizing the transmit beamforming at the transmitter and the power splitting ratios at different receivers. The optimal beamforming and power splitting solutions are obtained with the aid of semidefinite programming and bisection search. Low-complexity fixed beamforming and hybrid beamforming techniques are also studied. Furthermore, we study the effect of imperfect channel information and radiation matrices, and design robust beamforming to guarantee the worst-case performance. Simulation results demonstrate that our proposed algorithms can effectively deal with the radio exposure constraints and significantly outperform the conventional transmission scheme with power backoff.

This paper studies the problem of joint edge cache placement and content delivery in cache-enabled small cell networks in the presence of spatio-temporal content dynamics unknown a priori. The small base stations (SBSs) satisfy users' content requests either directly from their local caches, or by retrieving from other SBSs' caches or from the content server. In contrast to previous approaches that assume a static content library at the server, this paper considers a more realistic non-stationary content library, where new contents may emerge over time at different locations. To keep track of spatio-temporal content dynamics, we propose that the new contents cached at users can be exploited by the SBSs to timely update their flexible cache memories in addition to their routine off-peak main cache updates from the content server. To take into account the variations in traffic demands as well as the limited caching space at the SBSs, a user-assisted caching strategy is proposed based on reinforcement learning principles to progressively optimize the caching policy with the target of maximizing the weighted network utility in the long run. Simulation results verify the superior performance of the proposed caching strategy against various benchmark designs.

This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes.

Unmanned aerial vehicles (UAVs) have the capability of supplementing as well as improvising terrestrial cellular communications as aerial base stations to improve network coverage. This puts significance on the effective deployment of multiple UAVs while maximizing the coverage area in presence of co- channel interference generated by these UAVs. To this end, it is important to determine the parameters that affects the coverage area performance. In this paper, we investigate the effect of co-channel interference on the effective coverage of UAV-based small cells (USCs) deployed in a certain geographical area to satisfy a target signal-to-interference-plus-noise ratio (SINR) at the cell edge. We propose a coordinated multi- UAV strategy to evaluate the trade-off between the UAV separation distance and the overall coverage area assuming symmetric placement of UAVs at a common optimal altitude to ensure minimum transmit power. Numerical results unveil that the number of UAVs and the separation distance between them should be carefully designed to achieve the optimal coverage area performance.

This paper focuses on joint edge cache placement and content delivery problem at a base station (BS) in the presence of spatio-temporal unknown content dynamics, where the BS can satisfy its users' content demands either directly from its local cache or by fetching from the content server. Unlike the previous works that assume a static content library, we consider a more realistic non-stationary scenario, where new contents are emerging over time at the content library and might be cached at users. We propose that the new contents cached at local users can be utilized by the BS to timely update its flexible portion of cache memory in addition to its routine off-peak main cache update from the content server. We model the caching problem as a non- stationary bandit problem and introduce a user-aided caching algorithm that accounts for the traffic demand variations and the limited caching space at the BS. The proposed algorithm progressively improves the caching policy, with the target of maximizing the weighted content delivery rate to the users in the long run. Simulation results validate that the proposed strategy outperforms various benchmark designs.

This paper studies the problem of max-min fairness power allocation in distributed small cell networks operated under the same frequency bandwidth. We introduce a calibrated learning enhanced time division multiple access scheme to optimize the transmit power decisions at the small base stations (SBSs) and achieve max-min user fairness in the long run. Provided that the SBSs are autonomous decision makers, the aim of the proposed algorithm is to allow SBSs to gradually improve their forecast of the possible transmit power levels of the other SBSs and react with the best response based on the predicted results at individual time slots. Simulation results validate that in terms of achieving max-min signal-to-interference-plus-noise ratio, the proposed distributed design outperforms two benchmark schemes and achieves a similar performance as compared to the optimal centralized design.

The network profit is an effective performance metric to improve the flexibility of network deployment by the operators to adjust the network setting and data prices according to the network conditions. It is particularly useful for physical layer security (PLS), where the network will consume more energy on jamming. This paper studies the secrecy performance of a wireless-powered communication network (WPCN) in terms of profits (i.e. security profits), where power beacons (PBs) will first transmit energy to the energy-harvesting transmitters wirelessly, and then act as jammers to send artificial noise (AN) aided jamming signals to randomly distributed eavesdroppers (EDs) to prevent information leakage. With the use of stochastic geometry, we derive a closed-form expression of the secrecy profit gained by WPCNs which is defined as the difference between the revenue from secrecy transmission and the cost from the PBs' energy consumption. Numerical results verify the maximum secrecy profit of the proposed networks can be achieved with proper transmit power and density of the PBs, and show the flexibility of the secrecy profits compared with the secrecy energy efficiency.

With the development of the Internet-of-Things (IoT) industry, more and more fields are involved such as multimedia data. Currently, users rely on videos and images with high data volume, so it has brought more challenges for wireless communication and transmission. For multimedia data, it is obviously different from traditional communication data. So new method is required to solve the problem of high data volume in communication. The proactive content caching and the unmanned aerial vehicle (UAV) relaying techniques are deployed over IoT network, enabling the maximum throughput for the served IoT devices. Even though these two existing technologies are important to solve the problem of throughput, there are still other challenges for efficiently improving the system throughput. We mainly study the cache-enabled UAV to maximize throughput among IoT devices in the IoT with the placement of content caching and UAV location. Especially, we divide the joint optimization problem into two parts. First, the UAV deployment problem is decomposed into vertical and horizontal dimensions to ensure the optimal deployment height and 2-D position. The enumeration search method is employed to obtain the 2-D position. Then, we also formulate a concave problem for probabilistic caching placement. Experimental results have indicated that the cache-enabled UAV scheme can obtain a better throughput, which can bring new approach for multimedia data throughput maximization in IoT system.

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknowna priori, and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the accuracy of the autonomous decision making. We evaluate the performance of the proposed algorithm in cases of maximizing the long-term sum-rate, the overall energy efficiency and the average minimum achievable data rate. Numerical simulation results demonstrate that the proposed design outperforms the benchmark scheme with limited amount of information exchange and rapidly approaches towards the optimal centralized solution for all case studies.

Blockchain has created a revolution in digital networking by using distributed storage, cryptographic algorithms, and smart contracts. Many areas are benefiting from this technology, including data integrity and security, as well as authentication and authorization. Internet of Things (IoTs) networks often suffers from such security issues, which is slowing down wide-scale adoption. In this paper, we describe the employing of blockchain technology to construct a decentralized platform for storing and trading information in the air-to-ground IoT heterogeneous network. To allow both air and ground sensors to participate in the decentralized network, we design a mutual-benefit consensus process to create uneven equilibrium distributions of resources among the participants. We use a Cournot model to optimize the active density factor set in the heterogeneous air network and then employ a Nash equilibrium to balance the number of ground sensors, which is influenced by the achievable average downlink rate between the air sensors and the ground supporters. Finally, we provide numerical results to demonstrate the beneficial properties of the proposed consensus process for air-to-ground networks and show the maximum active sensor's density utilization of air networks to achieve a high quality of service.

The combination of non-orthogonal multiple access (NOMA) and transmit antenna selection (TAS) techniques has recently attracted significant attention due to the low cost, low complexity, and high diversity gains. Meanwhile, random linear coding (RLC) is considered to be a promising technique for achieving high reliability and low latency in multicast communications. In this paper, we consider a downlink system with a multi-antenna base station and two multicast groups of single-antenna users, where one group can afford to be served opportunistically, while the other group consists of comparatively low-power devices with limited processing capabilities that have strict quality of service (QoS) requirements. In order to boost reliability and satisfy the QoS requirements of the multicast groups, we propose a cross-layer framework, including NOMA-based TAS at the physical layer and RLC at the application layer. In particular, two low-complexity TAS protocols for NOMA are studied in order to exploit the diversity gain and meet the QoS requirements. In addition, RLC analysis aims to facilitate heterogeneous users, such that sliding window-based sparse RLC is employed for computational restricted users, and conventional RLC is considered for others. Theoretical expressions that characterize the performance of the proposed framework are derived and verified through simulation results.

In wireless powered communication networks (WPCNs), base station (BS) and power beacons (PBs) can offer supplement power for uplink transmission of user equipments (UEs). However, the aggregate power consumption of massively deployed PBs may exceed that of a BS. We propose a non-uniform deployment scheme for PBs in WPCNs, where a cell is divided into inner and outer areas, such that BS and PBs can cooperate to power UEs. To be more specific, a BS located in the center of a cell provides downlink power supply for the inner area UEs and uplink information decoding for all the UEs in the cell; while the PBs power UEs in the outer area. With multiple antennas, maximum ratio transmission and maximum ratio combining are adopted for downlink energy beamforming and uplink information reception. Considering a finite area of the network, we derive the distribution of the distance from a non-center-located UE to its nearest PB in the outer area. An optimization problem is formulated to minimize total average power consumption while satisfying BS average transmission power constraint and coverage probability threshold. Moreover, coverage probability is derived for performance evaluation. The numerical results show that the power consumption of the proposed scheme is reduced significantly compared to PB-only WPCNs.

Based on wireless network virtualization, radio access network (RAN) slicing is developed to provide services for the different users' requirements. Moreover, the users' sum data rate and delay are two significant metrics to guarantee quality of services. In this paper, we first establish an optimization problem to maximize the downlink sum rate while guaranteeing users' delay for RAN slices, where the base stations and user equipments are randomly distributed. Then we analyze the performance tradeoff between the sum rate maximization and delay tolerance. With the aid of Lyapunov optimization, the upper bounds of the achievable rate and delay are derived, through which the existence of tradeoff in performance is obvious and verified by numerical results.

We investigate robust multigroup multicast precoding for frame-based multi-beam satellite communication systems with full frequency reuse. To mitigate the effect of outdated channel state information (CSI), we first investigate robust multigroup multicast precoding that minimizing per beam transmission power while guaranteeing a predetermined average signal to interference plus noise ratio at each user. We then propose a low complexity precoder for it based on semidefinite relaxation and Gaussian randomization techniques. Simulation results demonstrate that the proposed robust approach can provide substantial performance gains over the conventional approach in multibeam satellite communication systems.

Full-duplex (FD) has emerged as a disruptive solution for improving the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex (HD) SE gain, in the context of cellular networks, is however largely limited by the mutual interference (MI) between the downlink (DL) and uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic analysis of FD enabled cloud radio access network (CRAN) with finite user- centric cooperative clusters. Contrary to the most existing theoretical studies of C-RAN, we explicitly take into consideration non-isotropic fading channel conditions, and finite-capacity fronthaul links. Accordingly, we develop analytical expressions for the FD C-RAN DL and UL SEs. The results indicate that significant FD versus HD C-RAN SE gains can be achieved, particularly in the presence of sufficient- capacity fronthaul links and advanced interference cancellation capabilities.

Communications aided by low-altitude unmanned aerial vehicles (UAVs) have emerged as an effective solution to provide large coverage and dynamic capacity for both military and civilian applications, especially in unexpected scenarios. However, because of their broad coverage, UAV communications are prone to passive eavesdropping attacks. This paper analyzes the secrecy performance of UAVs networks at the millimeter wave band and takes into account unique features of air-to-ground channels and practical constraints of UAV deployment. To be specific, it explores the 3-D antenna gain in the air-to-ground links and uses the Matérn hardcore point process to guarantee the safety distance between the randomly deployed UAV base stations. In addition, we propose the transmit jamming strategy to improve the secrecy performance in which part of UAVs send jamming signals to confound the eavesdroppers. Simulation results verify our analysis and demonstrate the impact of different system parameters on the achievable secrecy rate. It is also revealed that optimizing the density of jamming UAVs will significantly improve security of UAV-enabled networks.

In this paper, we propose a combined full duplex (FD)- and half duplex (HD)-based transmission and channel acquisition model for an open-loop multiuser multiple-input multiple-output (MIMO) systems. Assuming residual self-interference at the base station (BS), the idea is to utilize the FD mode during the uplink (UL) training phase in order to achieve simultaneous downlink (DL) data transmission and UL CSI acquisition. More specifically, the BS begins serving a user when its CSI becomes available, while at the same time, it also receives UL pilots from the next scheduled user. We investigate both zero-forcing (ZF) and maximum ratio transmission MIMO beamforming techniques for the DL data transmission in the FD mode. The BS switches to the HD mode once it receives the CSI of all users and it employs ZF beamforming for the DL data transmission until the end of the transmission frame. Furthermore, we derive closed-form approximations for the lower bounded ergodic achievable rate relying on the proposed model. Our numerical results show that the proposed FD-HD transmission and channel acquisition approach outperforms its conventional HD counterpart and achieves higher data rates.

Network virtualization (NV) is widely accepted as one enabling technology for future network, which enables multiple virtual networks (VNs) with different paradigms and protocols to coexist on the shared substrate network (SN). One key challenge in NV is VN embedding (VNE), which maps a VN onto the shared SN. Since VNE is NP-hard, existing efforts mainly focus on proposing heuristic algorithms that try to achieve feasible VNE in reasonable time, consequently the resulted embedding is not optimal. To tackle this difficulty, we propose a candidate assisted (CAN-A) optimal VNE algorithm with lower computational complexity. The key idea of the CAN-A algorithm lies in constructing the candidate substrate node subset and the candidate substrate path subset before embedding. This reduces the mapping execution time substantially without performance loss. In the following embedding, four types of node and link constraints are considered in the CAN-A algorithm, making it more applicable to realistic networks. Simulation results show that the execution time of CAN-A is hugely cut down compared with pure VNE-MIP algorithm. CAN-A also outperforms the typical heuristic algorithms in terms of other performance indices, such as the average VN request acceptance ratio and the average virtual link propagation delay.

Full-duplex (FD) wireless has emerged as a disruptive communications paradigm for enhancing the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference mitigation. The FD versus half-duplex (HD) SE gain in cellular networks is, however, largely limited by the mutual-interference (MI) between the downlink (DL) and the uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic design and analysis of FD enabled cloud radio access network (C-RAN) under the Poisson point process-based abstraction model of multi-antenna radio units and user equipments. We consider different network- and user-centric approaches toward the formation of finite clusters in the C-RAN. Contrary to most existing studies, we explicitly take into consideration non-isotropic fading channel conditions and finite-capacity fronthaul links. Accordingly, upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics of all intended and interfering signals, are derived. The performance of the FD C-RAN is investigated through the proposed theoretical framework and Monte-Carlo simulations. According to simulations using parameters of a state-of-the-art system, significant FD versus HD C-RAN SE gains can be achieved in the presence of advanced interference cancellation capabilities and sufficient-capacity fronthaul links.

This paper studies the performance of cache-enabled dense small cell networks consisting of multi-antenna sub-6 GHz and millimeter-wave (mm-wave) base stations. Different from the existing works which only consider a single antenna at each base station, the optimal content placement is unknown when the base stations have multiple antennas. We first derive the successful content delivery probability by accounting for the key channel features at sub-6 GHz and mm-wave frequencies. The maximization of the successful content delivery probability is a challenging problem. To tackle it, we first propose a constrained cross-entropy algorithm which achieves the near-optimal solution with moderate complexity. We then develop another simple yet effective heuristic probabilistic content placement scheme, termed two-stair algorithm, which strikes a balance between caching the most popular contents and achieving content diversity. Numerical results demonstrate the superior performance of the constrained cross-entropy method and that the two-stair algorithm yields significantly better performance than only caching the most popular contents. The comparisons between the sub-6 GHz and mm-wave systems reveal an interesting tradeoff between caching capacity and density for the mm-wave system to achieve similar performance as the sub-6 GHz system.

In this paper, we analyze the effect of phase noise on the downlink ergodic sum-rate of a cloud radio access network. The system comprises of one baseband processing unit (BBU) on the cloud server which coordinates M multi-antenna remote radio heads (RRHs) serving K single-antenna users using regularized zero-forcing precoding. We assume the BBU has all users' data and imperfect channel state information and communicate with RRHs via optical fibers which are referred to as fronthaul links. The effect of phase noise both at RRHs and users is also taken into consideration. A deterministic approximation of downlink ergodic sum-rate is derived based on large dimensional random matrix theory when the numbers of antennas at RRHs and users are asymptotically large with a fixed ratio. From simulation results, it is confirmed that the deterministic approximation is accurate and the effect of phase noise is shown to result in a significant reduction in system performance.

This paper studies the performance of cache-enabled dense small cell networks consisting of multi- antenna sub-6 GHz and millimeter-wave base stations. We first derive the successful content delivery probability by accounting for the key channel features at sub-6 GHz and mmWave frequencies. In general, the optimal content placement is unknown when the base stations have multiple antennas. Then we propose a simple yet effective probabilistic content placement scheme to maximize the successful content delivery probability, which could balance caching both the most popular contents and achieving content diversity. Numerical results demonstrate that our proposed content placement scheme yields significantly better performance than only caching the most popular contents. The comparisons between the sub-6 GHz and millimeter-wave systems reveal an interesting tradeoff between caching capacity and base station density for the millimeter-wave system to achieve similar performance as the sub-6 GHz system.

Relay selection is a simple yet effective means to improve the reliability and coverage of wireless cooperative networks. However, it suffers from inefficient use of the available bandwidth resources. This paper introduces the use of content caching at relays in order to tackle this problem and improve the performance of relay selection. Three cache placement schemes are considered: one based on the most popular files, a uniform based caching and a hybrid scheme of the two. We analytically derive their outage performance as well as their diversity order and coding gain. Numerical results demonstrate the substantial performance gains of these schemes over the traditional optimal relay selection approach without caching capabilities.

In this paper, we study a wireless-powered two-way relay system, in which both wireless-powered sources exchange information through a multi-antenna relay. Both sources are assumed to have no embedded energy supply and thus first need to harvest energy from the radio frequency signals broadcasted by the relay before exchanging their information via the relay. We aim to maximize the sum throughput of both sources by jointly optimizing the time switching duration, the energy beamforming vector and the precoding matrix at the relay. The formulated problem is non-convex and hard to solve in its original form. Motivated by this, we simplify the problem by reducing the number of variables and by decomposing the precoding matrix into a transmit vector and a receive vector. We then propose bisection search, 1-D search and iterative algorithms to optimize each variable. Numerical results show that our proposed scheme can achieve higher throughput than the conventional scheme without optimization on beamforming vector and precoding matrix at the relay.

We propose techniques for optimizing transmit beamforming in a full-duplex multiple-input-multiple-output wireless-powered communication system, which consists of two phases. In the first phase, the wireless-powered mobile station (MS) harvests energy using signals from the base station (BS), whereas in the second phase, both MS and BS communicate to each other in a full-duplex mode. When complete instantaneous channel state information (CSI) is available, the BS beamformer and the time-splitting (TS) parameter of energy harvesting are jointly optimized in order to obtain the BS-MS rate region. The joint optimization problem is non-convex, however, a computationally efficient optimum technique, based upon semidefinite relaxation and line-search, is proposed to solve the problem. A sub-optimum zero-forcing approach is also proposed, in which a closed-form solution of TS parameter is obtained. When only the second-order statistics of transmit CSI is available, we propose to maximize the ergodic information rate at the MS while maintaining the outage probability at the BS below a certain threshold. An upper bound for the outage probability is also derived and an approximate convex optimization framework is proposed for efficiently solving the underlying non-convex problem. Simulations demonstrate the advantages of the proposed methods over the sub-optimum and half-duplex ones.

We provide a theoretical framework for the study of massive multiple-input multiple-output (MIMO)-enabled full-duplex (FD) cellular networks in which the residual self-interference (SI) channels follow the Rician distribution and other channels are Rayleigh distributed. In order to facilitate bi-directional wireless functionality, we adopt: 1) in the downlink (DL), a linear zero-forcing (ZF) with SI-nulling precoding scheme at the FD base stations and 2) in the uplink (UL), an SI-aware fractional power control mechanism at the FD mobile terminals. Linear ZF receivers are further utilized for signal detection in the UL. The results indicate that the UL rate bottleneck in the FD baseline single-input single-output system can be overcome via exploiting massive MIMO. On the other hand, the findings may be viewed as a reality-check, since we show that, under state-of-the-art system parameters, the spectral efficiency gain of FD massive MIMO over its half-duplex counterpart is largely limited by the cross-mode interference between the DL and the UL. In point of fact, the anticipated twofold increase in SE is shown to be only achievable when the number of antennas tends to be infinitely large.

This paper brings the novel idea of paying the utility to the winning agents in terms of some physical entity in cooperative communications. Our setting is a secret two-way communication channel where two transmitters exchange information in the presence of an eavesdropper. The relays are selected from a set of interested parties, such that the secrecy sum rate is maximized. In return, the selected relay nodes' energy harvesting requirements will be fulfilled up to a certain threshold through their own payoff so that they have the natural incentive to be selected and involved in the communication. However, relays may exaggerate their private information in order to improve their chance to be selected. Our objective is to develop a mechanism for relay selection that enforces them to reveal the truth since otherwise they may be penalized. We also propose a joint cooperative relay beamforming and transmit power optimization scheme based on an alternating optimization approach. Note that the problem is highly non-convex, since the objective function appears as a product of three correlated Rayleigh quotients. While a common practice in the existing literature is to optimize the relay beamforming vector for given transmit power via rank relaxation, we propose a second-order cone programming-based approach in this paper, which requires a significantly lower computational task. The performance of the incentive control mechanism and the optimization algorithm has been evaluated through numerical simulations.

In this paper, we consider a three-node cooperative wireless powered communication system consisting of a multi-antenna hybrid access point (H-AP) and a single-antenna relay and a single-antenna user. The energy constrained relay and user first harvest energy in the downlink and then the relay assists the user using the harvested power for information transmission in the uplink. The optimal energy beamforming vector and the time split between harvest and cooperation are investigated. To reduce the computational complexity, suboptimal designs are also studied, where closed-form expressions are derived for the energy beamforming vector and the time split. For comparison purposes, we also present a detailed performance analysis in terms of the achievable outage probability and the average throughput of an intuitive energy beamforming scheme, where the H-AP directs all the energy towards the user. The findings of the paper suggest that implementing multiple antennas at the H-AP can significantly improve the system performance, and the closed-form suboptimal energy beamforming vector and time split yields near optimal performance. Also, for the intuitive beamforming scheme, a diversity order of N+1/2 can be achieved, where N is the number of antennas at the H-AP.

The demand for wider bandwidths has motivated the need for wireless systems to migrate to higher frequency bands. In line with this trend is an envisaged deployment of Ka-band (or mmWave) cellular infrastructure. Further, to improve the spectral efficiency, developing full-duplex radio transceivers is gaining momentum. In view of this move, the paper proposes the possibility of reusing the satellite feeder uplink band in the full-duplex small cells. The motivation for such a reuse is two-fold :(a) there is virtually no interference from the small cells to the incumbent in-orbit satellite receiver, and (b) directive feeder antennas, with possibly additional isolation and processing causing negligible interference to the small cells. The presented interference analysis clearly supports the proposed coexistence.

In this paper, we propose a power-efficient approach for information and energy transfer in multiple-input single-output downlink systems. By means of data-aided precoding, we exploit the constructive part of interference for both information decoding and wireless power transfer. Rather than suppressing interference as in conventional schemes, we take advantage of constructive interference among users, inherent in the downlink, as a source of both useful information signal energy and electrical wireless energy. Specifically, we propose a new precoding design that minimizes the transmit power while guaranteeing the quality of service (QoS) and energy harvesting constraints for generic phase shift keying modulated signals. The QoS constraints are modified to accommodate constructive interference, based on the constructive regions in the signal constellation. Although the resulting problem is nonconvex, several methods are developed for its solution. First, we derive necessary and sufficient conditions for the feasibility of the considered problem. Then we propose second-order cone programming and semi-definite programming algorithms with polynomial complexity that provide upper and lower bounds to the optimal solution and establish the asymptotic optimality of these algorithms when the modulation order and SINR threshold tend to infinity. A practical iterative algorithm is also proposed based on successive linear approximation of the nonconvex terms yielding excellent results. More complex algorithms are also proposed to provide tight upper and lower bounds for benchmarking purposes. Simulation results show significant power savings with the proposed data-aided precoding approach compared to the conventional precoding scheme.

We consider a full-duplex (FD) decode-and-forward system in which the time-switching protocol is employed by the multiantenna relay to receive energy from the source and transmit information to the destination. The instantaneous throughput is maximized by optimizing receive and transmit beamformers at the relay and the time-split parameter. We study both optimum and suboptimum schemes. The reformulated problem in the optimum scheme achieves closed-form solutions in terms of transmit beamformer for some scenarios. In other scenarios, the optimization problem is formulated as a semidefinite relaxation problem and a rank-one optimum solution is always guaranteed. In the suboptimum schemes, the beamformers are obtained using maximum ratio combining, zero-forcing, and maximum ratio transmission. When beamformers have closed-form solutions, the achievable instantaneous and delay-constrained throughput are analytically characterized. Our results reveal that beamforming increases both the energy harvesting and loop interference suppression capabilities at the FD relay. Moreover, simulation results demonstrate that the choice of the linear processing scheme as well as the time-split plays a critical role in determining the FD gains.

We propose a new beamforming scheme for the multi- user multiple-input-single-output (MISO) downlink channel. While conventional beamforming aims at the minimization of the transmit power subject to suppressing interference to guarantee quality of service (QoS) constraints, here we exploit, rather than suppress, the constructive part of interference. By exploiting the power of constructively interfering symbols, the proposed scheme achieves the required QoS at lower transmit power. In addition, we derive an equivalent virtual multicast formulation for the proposed optimization to facilitate the design of a more efficient solver. Our simulation and analysis show significant power savings for small scale MISO downlink channels with the proposed optimization compared to conventional beamforming optimization.

In this paper we propose a power-efficient transfer of information and energy, where we exploit the constructive part of wireless interference as a source of green useful signal power. Rather than suppressing interference as in conventional schemes, we take advantage of constructive interference among users, inherent in the downlink, as a source of both useful information and wireless energy. Specifically, we propose a new precoding design that minimizes the transmit power while guaranteeing the quality of service (QoS) and energy harvesting constraints for generic phase shift keying modulated signals. The QoS constraints are modified to accommodate constructive interference. We derive a sub-optimal solution and a local optimum solution to the precoding optimization problem. The proposed precoding reduces the transmit power compared to conventional schemes, by adapting the constraints to accommodate constructive interference as a source of useful signal power. Our simulation results show significant power savings with the proposed data-aided precoding compared to the conventional precoding.

We consider a dual-hop full-duplex (FD) relaying system, where the energy constrained relay node is powered by radio frequency signals from the source using the time-switching architecture. Both the amplify-and-forward and decode-and-forward relaying protocols are studied. Specifically, we provide an analytical characterization of the achievable throughput of three different communication modes, namely, instantaneous transmission, delay-constrained transmission, and delay tolerant transmission. In addition, the optimal time split is studied for different transmission modes. Our results reveal that, when the time split is optimized, FD relaying could substantially boost the system throughput compared to the conventional half-duplex relaying architecture for all three transmission modes. In addition, it is shown that the instantaneous transmission mode has the highest throughput. However, compared to the delay tolerant transmission mode, the throughput gap is negligible. Unlike the instantaneous time split optimization which requires instantaneous channel state information, the optimal time split in the delay tolerant transmission mode depends only on the statistics of the channel, hence, is attractive for practical implementation.

This letter maximizes the achievable throughput of a relay-assisted wirelessly powered communications system, where an energy constrained source, assisted by an energy constrained relay and both powered by a dedicated power beacon (PB), communicates with a destination. Considering the time splitting approach, the source and relay first harvest energy from the PB, which is equipped with multiple antennas, and then transmits the information to destination. Simple closed-form expressions are derived for the optimal PB energy beamforming vector and time split for energy harvesting and information transmission. Numerical results and simulations demonstrate the superior performance compared with some intuitive benchmark beamforming scheme. Also, it is found that placing the relay at the middle of the source-destination path is no longer optimal.

Network coding is an efficient means to improve the spectrum efficiency of satellite communications. However, its resilience to eavesdropping attacks is not well understood. This paper studies the confidentiality issue in a bidirectional satellite network consisting of two mobile users who want to exchange message via a multibeam satellite using the XOR network coding protocol. We aim to maximize the sum secrecy rate by designing the optimal beamforming vector along with optimizing the return and forward link time allocation. The problem is nonconvex, and we find its optimal solution using semidefinite programming together with a 1-D search. For comparison, we also solve the sum secrecy rate maximization problem for a conventional reference scheme without using network coding. Simulation results using realistic system parameters demonstrate that the bidirectional scheme using network coding provides considerably higher secrecy rate compared with that of the conventional scheme.

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.

We consider a dual-hop full-duplex relaying system, where the energy constrained relay node is powered by radio frequency signals from the source using the time-switching architecture, both the amplify-and-forward and decode-and-forward relaying protocols are studied. Specifically, we provide an analytical characterization of the achievable throughput of three different communication modes, namely, instantaneous transmission, delay-constrained transmission, and delay tolerant transmission. In addition, the optimal time split is studied for different transmission modes. Our results reveal that, when the time split is optimized, the full-duplex relaying could substantially boost the system throughput compared to the conventional half-duplex relaying architecture for all three transmission modes. In addition, it is shown that the instantaneous transmission mode attains the highest throughput. However, compared to the delay-constrained transmission mode, the throughput gap is rather small. Unlike the instantaneous time split optimization which requires instantaneous channel state information, the optimal time split in the delay-constrained transmission mode depends only on the statistics of the channel, hence, is suitable for practical implementations.

In this paper, we deal with the deployment of full-duplex relaying in amplify-and-forward (AF) cooperative networks with multiple-antenna terminals. In contrast to previous studies, which focus on the spatial mitigation of the loopback interference (LI) at the relay node, a joint precoding/decoding design that maximizes the end-to-end (e2e) performance is investigated. The proposed precoding incorporates rank-1 zero-forcing (ZF) LI suppression at the relay node and is derived in closed-form by solving appropriate optimization problems. In order to further reduce system complexity, the antenna selection (AS) problem for full-duplex AF cooperative systems is discussed. We investigate different AS schemes to select a single transmit antenna at both the source and the relay, as well as a single receive antenna at both the relay and the destination. To facilitate comparison, exact outage probability expressions and asymptotic approximations of the proposed AS schemes are provided. In order to overcome zero-diversity effects associated with the AS operation, a simple power allocation scheme at the relay node is also investigated and its optimal value is analytically derived. Numerical and simulation results show that the joint ZF-based precoding significantly improves e2e performance, while AS schemes are efficient solutions for scenarios with strict computational constraints.

We consider a multiuser multiple-input single-output interference channel where the receivers are characterized by both quality-of-service (QoS) and radio-frequency (RF) energy harvesting (EH) constraints. We consider the power splitting RF-EH technique where each receiver divides the received signal into two parts a) for information decoding and b) for battery charging. The minimum required power that supports both the QoS and the RF-EH constraints is formulated as an optimization problem that incorporates the transmitted power and the beamforming design at each transmitter as well as the power splitting ratio at each receiver. We consider both the cases of fixed beamforming and when the beamforming design is incorporated into the optimization problem. For fixed beamforming we study three standard beamforming schemes, the zero-forcing (ZF), the regularized zero-forcing (RZF) and the maximum ratio transmission (MRT); a hybrid scheme, MRT-ZF, comprised of a linear combination of MRT and ZF beamforming is also examined. The optimal solution for ZF beamforming is derived in closed-form, while optimization algorithms based on second-order cone programming are developed for MRT, RZF and MRT-ZF beamforming to solve the problem. In addition, the joint-optimization of beamforming and power allocation is studied using semidefinite programming (SDP) with the aid of rank relaxation.

Channel estimation attacks can degrade the performance of the legitimate system and facilitate eavesdropping. It is known that pilot contamination can alter the legitimate transmit precoder design and strengthen the quality of the received signal at the eavesdropper, without being detected. In this paper, we devise a technique which employs random pilots chosen from a known set of phase-shift keying (PSK) symbols to detect pilot contamination. The scheme only requires two training periods without any prior channel knowledge. Our analysis demonstrates that using the proposed technique in a massive MIMO system, the detection probability of pilot contamination attacks can be made arbitrarily close to 1. Simulation results reveal that the proposed technique can significantly increase the detection probability and is robust to noise power as well as the eavesdropper's power.

This paper studies the impact of jamming on the design of three-node two-hop cooperative amplify-and-forward (AF) communications with both half-duplex and full-duplex relaying. For the half-duplex relaying, the jammer is smart such that it can optimally allocate jamming power between listening and forwarding phases. Given separate source and relay power constraints, we derive the optimal jamming power allocation; with a total source and relay power constraint, we model the interaction between the legitimate system and the jammer as a noncooperative game and prove the existence and uniqueness of the Nash Equilibrium (NE). It is found that due to the fact that the end performance is limited by the weaker phase, the legitimate systems tries to balance the performance of two phases while the jammer attacks the system by making the two hops imbalanced. While for the full-duplex relaying, we show that if the self-interference can be properly controlled, it can bring substantial performance gain. Simulation results verify our analysis.

Harvest-use (HU) is an energy harvesting (EH) architecture where the received energy cannot be stored and immediately must be consumed in order to maintain operability. Due to its current limited application interest, this architecture has not yet been examined in the literature and its deployment to communication system is an open problem. This paper deals with the application of HU architecture to communication systems and investigates cooperative protocols where the relay node has HU capabilities. We show that HU relaying introduces a trade-off between EH time and relaying (data communication) time; this trade-off is discussed for two fundamental relaying policies a) Amplify-and-forward (AF) with half-duplex (HD) relaying and b) AF with full-duplex (FD) relaying. The optimal time split is formulated as an optimization problem and an approximation is given in a closed form. Numerical results show that FD outperforms HD and is introduced as an efficient relaying policy for HU cooperative systems.

This paper studies the cooperation between a primary system and a cognitive system in a cellular network where the cognitive base station (CBS) relays the primary signal using amplify-and-forward or decode-and-forward protocols, and in return it can transmit its own cognitive signal. While the commonly used half-duplex (HD) assumption may render the cooperation less efficient due to the two orthogonal channel phases employed, we propose that the CBS can work in a full-duplex (FD) mode to improve the system rate region. The problem of interest is to find the achievable primary-cognitive rate region by studying the cognitive rate maximization problem. For both modes, we explicitly consider the CBS transmit imperfections, which lead to the residual self-interference associated with the FD operation mode. We propose closed-form solutions or efficient algorithms to solve the problem when the related residual interference power is non-scalable or scalable with the transmit power. Furthermore, we propose a simple hybrid scheme to select the HD or FD mode based on zero-forcing criterion, and provide insights on the impact of system parameters. Numerical results illustrate significant performance improvement by using the FD mode and the hybrid scheme.

Multibeam systems with hundreds of beams have been recently deployed in order to provide higher capacities by employing fractional frequency reuse. Furthermore, employing full frequency reuse and precoding over multiple beams has shown great throughput potential in literature. However, feeding all this data from a single gateway is not feasible based on the current frequency allocations. In this context, we investigate a range of scenarios involving beam clusters where each cluster is managed by a single gateway. More specifically, the following cases are considered for handling intercluster interference: a) conventional frequency colouring, b) joint processing within cluster, c) partial CSI sharing among clusters, d) partial CSI and data sharing among clusters. CSI sharing does not provide considerable performance gains with respect to b) but combined with data sharing offers roughly a 40% improvement over a) and a 15% over b).

This paper addresses coordinated downlink beamforming optimization in multicell time division duplex (TDD) systems where a small number of parameters are exchanged between cells but with no data sharing. With the goal to reach the point on the Pareto boundary with max-min rate fairness, we first develop a two-step centralized optimization algorithm to design the joint beamforming vectors. This algorithm can achieve a further sum-rate improvement over the max-min optimal performance, and is shown to guarantee max-min Pareto optimality for scenarios with two base stations (BSs) each serving a single user. To realize a distributed solution with limited intercell communication, we then propose an iterative algorithm by exploiting an approximate uplink-downlink duality, in which only a small number of positive scalars are shared between cells in each iteration. Simulation results show that the proposed distributed solution achieves a fairness rate performance close to the centralized algorithm while it has a better sum-rate performance, and demonstrates a better tradeoff between sum-rate and fairness than the Nash Bargaining solution especially at high signal-to-noise ratio.

Security threats introduced due to the vulnerability of the transmission medium may hinder the proliferation of Ka band multibeam satellite systems for civil and military data applications. This paper sets the analytical framework and then studies physical layer security techniques for fixed legitimate receivers dispersed throughout multiple beams, each possibly surrounded by multiple (passive) eavesdroppers. The design objective is to minimize via transmit beamforming the costly total transmit power on board the satellite, while satisfying individual intended users' secrecy rate constraints. Assuming state-of-the-art satellite channel models, when perfect channel state information (CSI) about the eavesdroppers is available at the satellite, a partial zero-forcing approach is proposed for obtaining a low-complexity sub-optimal solution. For the optimal solution, an iterative algorithm combining semi-definite programming relaxation and the gradient-based method is devised by studying the convexity of the problem. Furthermore, the use of artificial noise as an additional degree-of-freedom for protection against eavesdroppers is explored. When only partial CSI about the eavesdroppers is available, we study the problem of minimizing the eavesdroppers' received signal to interference-plus-noise ratios. Simulation results demonstrate substantial performance improvements over existing approaches.

Multibeam satellite systems have been employed to provide interactive broadband services to geographical areas under-served by terrestrial infrastructure. In this context, this paper studies joint multiuser linear precoding design in the forward link of fixed multibeam satellite systems. We provide a generic optimization framework for linear precoding design to handle any objective functions of data rate with general linear and nonlinear power constraints. To achieve this, an iterative algorithm which optimizes the precoding vectors and power allocation alternatingly is proposed and most importantly, the proposed algorithm is proved to always converge. The proposed optimization algorithm is also applicable to nonlinear dirty paper coding. As a special case, a more efficient algorithm is devised to find the optimal solution to the problem of maximizing the proportional fairness among served users. In addition, the aforementioned problems and algorithms are extended to the case that each terminal has multiple co-polarization or dual-polarization antennas. Simulation results demonstrate substantial performance improvement of the proposed schemes over conventional multibeam satellite systems, zero-forcing and regularized zero-forcing precoding schemes in terms of meeting the traffic demand, e.g., using real beam patterns, over twice higher throughput can be achieved compared with the conventional scheme. The performance of the proposed linear precoding scheme is also shown to be very close to the dirty paper coding.

In conventional multibeam satellite systems, frequency and polarization orthogonalization have been traditionally employed for mitigating interbeam interference. However, the paradigm of multibeam joint precoding allows for full frequency reuse while assisting beam-edge users. In this paper, the performance of linear beamforming is investigated in terms of meeting traffic demands. More importantly, generic linear constraints are considered over the transmit covariance matrix in order to model the power pooling effect which can be implemented through flexible traveling wave tube amplifiers (TWTAS) or multiport amplifiers. The performance of this scheme is compared against conventional spotbeam systems based on the rate-balancing objective. In this context, it is shown that significantly higher spectral efficiency can be achieved through beamforming, while flexible power constraint offers better rate-balancing.

This paper addresses the joint relay assignment and power allocation problem for orthogonal multiuser systems using amplify-and-forward (AF) relaying nodes in the downlink. Our aim is to maximize the sum-rate subject to individual and total power constraints on the relays and a relay assignment constraint. In the case of fixed relay selection, the power allocation optimization is convex and an efficient recursive algorithm is proposed to achieve the optimum. The joint optimization of relay selection and power allocation, however, appears to be non-convex and is not known to be tractable. To tackle this, we propose a novel algorithm using Markov chain Monte-Carlo with Kullback-Leibler divergence minimization (MCMC-KLDM), which is proved to converge to the global optimum almost surely. Results show that the proposed scheme significantly outperforms a greedy approach and achieves near-optimal performance at very low complexity.

This paper considers a joint antenna selection method in amplify-and-forward (AF) relay networks where the source, relay and destination terminals are all equipped with multiple antennas. The fact that the system's full diversity can be maintained by antenna selection at each terminal makes it a promising solution to reduce the hardware complexity of multiple-input multiple-output (MIMO) terminals while realizing the diversity benefits of MIMO in relay networks. Since the exhaustive search for antenna subset selection is computationally prohibitive, we devise a low-complexity near-optimal joint antenna selection algorithm based on a constrained cross entropy optimization (CCEO) method to maximize the achievable rate and the convergence is guaranteed. Simulation results reveal both the effectiveness and the efficiency of the proposed algorithm and the significant performance improvement over other benchmark selection techniques. Finally, it is illustrated that the proposed CCEO algorithm can always achieve near-optimal results regardless of the number of selected antennas, outage probabilities and the signal-to-noise ratios (SNRs) at the terminals.

This letter considers the multi-antenna cognitive radio (CR) network, which has a single secondary user (SU) and coexists with a primary network of multiple users. Our objective is to maximize the service probability of the SU, subject to the interference constraints on the primary users (PUs) in the form of probability. Exploiting imperfect channel state information (CSI), with its error modeled by added Gaussian noise, we address the optimization for the beamforming weights at the secondary transmitter. In particular, this letter devises an iterative algorithm that can efficiently obtain the robust optimal beamforming solution. For the case with one PU, we show that a much simpler algorithm based on a closed-form solution for the antenna weights of a given power can be presented. Numerical results reveal that the optimal solution for the constructed problem provides an effective means to tradeoff the performance between the PUs and the SU, bridging the non-robust and worst-case based systems.

In cognitive radio, it is crucial to control the interference from secondary users (SUs) to primary users (PUs). This paper studies the use of transmit beamforming in the cognitive secondary network for enhancing the performance of a SU while controlling the interference to the PUs. In particular, we propose to maximize the service probability of the SU with a number of probability constraints on the interference level at the PUs with the aid of imperfect channel state information (CSI). Modeling the CSI uncertainty as an additive Gaussian noise, it is shown that the optimum can be realized by second-order cone-programming (SOCP) in tandem with a one-dimensional search. Results reveal that the proposed approach provides a technique to tradeoff the performance between the PUs and the SU, making an analytical connection between non-robust and worst-case systems.

This letter studies the collaborative use of amplify-and-forward (AF) relays to form a virtual multiple-input single-output (MISO) beamforming system with the aid of perfect channel state information (CSI) in a flat-fading channel. In particular, we optimize the relay weights jointly to maximize the received signal-to-noise ratio (SNR) at the destination terminal with both individual and total power constraints at the relays. We show that the optimal collaborative-relay beamforming (CRB) solution achieves the full diversity of a MISO antenna system. Another main contribution of this letter is a distributed algorithm that allows each individual relay to learn its own weight, based on the Karush–Kuhn–Tucker (KKT) analysis.

This paper addresses the joint optimization of power control and receive beamforming vectors for a multiuser singleinput multiple-output (SIMO) antenna system in the uplink in which mobile users are single-antenna transmitters and the base station receiver has multiple antennas. Channel state information at the receiver (CSIR) is exploited but the CSIR is imperfect with its uncertainty being modeled as a random Gaussian matrix. Our objective is to devise an energy-efficient solution to minimize the individual users' transmit power while meeting the users' signal-to-interference plus noise ratio (SINR) constraints, under the consideration of CSIR and its error characteristics. This is achieved by solving a sum-power minimization problem, subject to a collection of users' outage probability constraints on their target SINRs. Regarding the signal power minus the sum of inter-user interferences (SMI) power as Gaussian, an iterative and convergent algorithm which is proved to reach the global optimum for the joint power allocation and receive beamforming solution, is proposed, though the optimization problem is indeed non-convex. A systematic scheme to detect feasibility and find a feasible initial solution, if there exists any, is also devised. Simulation results verify the use of Gaussian approximation and robustness of the proposed algorithm in terms of users' probability constraints, and indicate a significant performance gain as compared to the zero-forcing (ZF) and minimum mean square- error (MMSE) beamforming systems.

Estimation of the channel state information (CSI) in quadratic form (i.e., quadratic channel estimation) in the downlink can be performed at the base station by using the relayed signals from the mobile users, which facilitates optimization with transmitter CSI. In this letter, the condition for the optimal training sequence for quadratic channel estimation in a multiuser multiple-input single-output (MISO) antenna system in the downlink is first obtained. The mean-square-error (MSE) in the CSI estimate is then analyzed. Based on the quadratic CSI estimates, a robust beamforming optimization algorithm to minimize the base station power while achieving individual users' quality-of-service (QoS) constraints, measured by the MSE in data reception, is proposed.

In (Dong and Ding, 2006), it has been proposed that channel estimates in quadratic form can be obtained at the base station by sending training sequences to the mobiles where the received signals are forwarded back to the base for channel estimation. In this paper, we first examine the optimal training sequence design for such quadratic channel estimation and then analyze the error bound and statistics of the channel estimates in quadratic form. With the analytical results, two problems for a multiple-input single-output (MISO) antenna system in the downlink are constructed and optimally solved: Power minimization with individual users' 1) worst-case signal-to-interference plus noise ratio (SINR) and 2) average mean-square-error (MSE) constraints, through optimal multiuser MISO beamforming and power allocation.

This paper addresses the problem of maximizing the sum-rate with a total power constraint for a multiuser multiple-input multiple-output (MIMO) antenna system in the downlink using generalized beamforming. Channel state information at the transmitter (CSIT) and the receivers (CSIR) is assumed available. An iterative approach is proposed which alternates between optimizing the power allocation, transmit antenna vectors and receive antenna vectors using a combination of geometric programming (GP), signomial programming (SP), uplink-downlink duality and minimum mean-square-error (MMSE) receiver optimization. It is proved that the sum-rate in the proposed scheme has a monotonic increasing property and hence the algorithm is convergent. Simulation results show that this algorithm can significantly increase the sum-rate compared with a zero-forcing solution.

In this paper, we address the joint transmit and receive beamforming design of a 2-user (2, 2)1downlink system. Our aim is to minimize the aggregate mean-square-error (MSE) (or sum-MSE in short) subject to the total power constraint. By exploiting the uplink-downlink duality, the problem can be converted to the equivalent uplink system and so solved using semidefinite programming relaxation (SDPR). Due to the rank relaxation, however, SDPR will generally not give the optimal solution of the original problem. In particular, for some cases, we can show analytically that the exact optimal beamforming solution can be retrieved from the SDPR. For the other cases, a convergent iterative algorithm is proposed to provide a solution.