This paper introduces an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery, sensing data gathering, and mobile edge computing (MEC) services. Three tenants provide services to their clients by sharing a common infrastructure of a set of UAVs. The content delivery tenant needs to guarantee that each of its clients (users) receives the required content, the sensing tenant aims to gather an adequate amount of uncorrelated data, and the MEC tenant provides computing service to its clients. An energy consumption minimization framework is considered to meet the tenants’ requirements by optimizing the number of deployed UAVs, the deployment location of each UAV, the transmit power of each deployed UAV, the user-UAV association, and the transmission power as well as the computing resources of each UAV. Taking into account the spatial correlation among the sensing users, a subset of these users is activated to gather the required sensing information. A solution approach technique inherited from graph theory is presented, in which the Lagrange approach derives the transmission power and computing resource allocation expressions. Simulation results illustrate that the proposed framework significantly reduces the total energy consumption.

This work proposes a novel quantum-assisted partial sorting algorithm, called multi-minima Dürr–Høyer (MMDH), designed to reduce query complexity in scenarios where only a small subset of elements must be sorted. Empirical results show that MMDH significantly outperforms classical algorithms in these settings, achieving over an order of magnitude reduction in query complexity. The algorithm is applied to signal detection in multiple-input multiple-output systems and is particularly effective when integrated into a newly introduced variable-complexity sphere decoder, called progressive tree expansion (PTE), which inherently benefits from partial sorting. Compared to fixed-complexity sphere decoders (FCSDs), the PTE algorithm substantially reduces the computational complexity, especially at high signal-to-noise ratios (SNRs). When augmented with MMDH, the quantum-assisted PTE decoder achieves near maximum-likelihood error performance while mitigating the query overhead commonly associated with tree-based decoders. In contrast, conventional FCSDs benefit less from MMDH, as they require selection of multiple minima rather than partial ordering, a task where classical methods such as heap-based selection remain competitive. Although MMDH introduces a small failure probability, the resulting error floor stays below practical thresholds in high-SNR regimes.

This paper investigates a drone (aka UAV)-assisted physical layer security framework for space–air–ground integrated networks (SAGINs) in the presence of multiple eavesdroppers. A single full-duplex UAV is deployed to support satellite-to-ground communications by simultaneously relaying desired signals to legitimate users and transmitting artificial noise to degrade the reception quality of eavesdroppers. To enhance secure connectivity, we formulate a max–min secrecy rate optimization problem that jointly considers sub-channel allocation and power distribution. The sub-channel allocation is optimized using a constrained genetic algorithm, which efficiently handles the combinatorial nature of the problem. Additionally, power allocation is optimized through a nested-loop approach, in which the outer loop employs Bayesian optimization to address complex objective functions, while the inner loop makes the allocation tractable using variable substitutions and approximation methods to overcome non-convexity. The simulation results demonstrate that the proposed method outperforms the benchmark schemes in terms of secrecy performance, particularly under stringent resource and security constraints in SAGINs.

With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. To address these challenges, this tutorial provides a systematic and comprehensive introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers an integrated overview of cutting-edge methodologies and practical insights. First, the tutorial outlines the background of 6G communications and reviews the technological evolution from LAMs to Agentic AI. It then systematically examines the key components required for constructing LAMs, classifies various types of LAMs, and analyzes their applicability in communication. A LAM-centric design paradigm tailored for communication systems is subsequently proposed, encompassing dataset construction, internal learning, and external learning approaches. Building upon this foundation, the tutorial develops an LAM-based Agentic AI system for intelligent communications, elaborating on its core components—including agents, world models, planners, knowledge bases, tools, and memory modules— as well as their interaction mechanisms. Finally, it provides an in-depth review of representative applications of LAMs and Agentic AI in communication scenarios, and summarizes the current research challenges and future directions, with the goal of fostering the development of efficient, secure, and sustainable next-generation intelligent communication systems.

Integrated sensing and communication (ISAC) is emerging as a key enabler for spectrum-efficient and hardware-converged wireless networks. However, classical radar systems within ISAC architectures face fundamental limitations under low signal power and high-noise conditions. This paper proposes a novel framework that embeds quantum illumination radar into a base station to simultaneously support full-duplex classical communication and quantum-enhanced target detection. The resulting integrated quantum sensing and classical communication (IQSCC) system is optimized via a sum-rate maximization formulation subject to radar sensing constraints. The non-convex joint optimization of transmit power and beamforming vectors is tackled using the successive convex approximation technique. Furthermore, we derive performance bounds for classical and quantum radar protocols under the statistical detection theory, highlighting the quantum advantage in low signal-to-interference-plus-noise ratio regimes. Simulation results demonstrate that the proposed IQSCC system achieves a higher communication throughput than the conventional ISAC baseline while satisfying the sensing requirement.

Hybrid beamforming structure is widely used in mmWave massive MIMO due to the low hardware complexity. However, its performance is severely degraded by beam squint effects in wideband multiuser mmWave massive MIMO resulting from the non-frequency-specific analog beamforming. To overcome this issue, we develop an integrated user grouping, subcarrier allocation, and hybrid beamforming (IUSH) scheme to compensate for the beam squint effects and maximize the multiuser sum-rate. First, we investigate the time-division and frequency-division analog beamforming for the single radio frequency (RF) chain cases to gain useful insights regarding user grouping and subcarrier allocation. The frequency-division approach achieves superior performance by leveraging the correlations between analog beamforming and user channels, and utilizing subcarrier allocation to dynamically align them. To this end, for multiple RF chain cases, we propose a wideband user grouping strategy that clusters users with close physical channel angles to strengthen the correlations, thereby facilitating efficient subcarrier allocation among users. Then, we develop an alternating minimization-based joint subcarrier allocation and hybrid beamforming (AM-JSH) algorithm to maximize the sum-rate of grouped users. The AM-JSH algorithm alternates between subcarrier allocation and hybrid beamforming, where the subcarrier allocation is formulated as an assignment problem to be solved using the classic Hungarian algorithm, and the hybrid beamforming is implemented with the developed penalty-based two-loop hybrid beamforming algorithm. Simulation results show that the developed IUSH scheme significantly outperforms the existing ones.

Multi orbit low earth orbit (LEO) satellites communication is envisioned as a key infrastructure to deliver global coverage, enabling future services from space air ground integrated networks.However, the optimized design of LEO which jointly addresses satellite selection, association control, and resource scheduling while accounting for dynamic visibility in multi orbit constellations still remains open. Satellites moving along distinct orbital planes yield phase shifted ground tracks and heterogeneous, time varying coverage patterns that significantly complicate the optimization.To bridge the gap, we propose a dynamic visibility aware multi orbit satellite selection framework which can determine the optimal serving satellites across orbital layers. The framework is built upon Markov approximation and matching game theory. Specifically, we formulate a combinatorial optimization problem that maximizes the sum rate under per satellite power budgets. The problem is NP hard , combining discrete user association (UA) decisions with continuous power allocation, and an inherently non convex sum rate maximization objective. We address it through a problem specific Markov approximation. Moreover, we alternately solve UA or bandwidth allocation via a matching game and power allocation via a Lagrangian dual program, which together form a block coordinate descent method tailored to this problem. Simulation results show that the proposed algorithm converges to a suboptimal solution across all scenarios. Extensive experiments against four state of the art baselines further demonstrate that our algorithm achieves, on average, approximately 7.85% higher sum rate than the best performing baseline.

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

This paper proposes a novel index modulation-based orthogonal time frequency space (IM-OTFS) framework for terahertz integrated sensing and communication (THz-ISAC) systems. The framework enhances bit error rate (BER) performance in high-mobility scenarios while improving spectral efficiency relative to existing embedded pilot-based ISAC methods by conveying information through both constellation symbols and index bits in the delay-Doppler (DD) domain. A new sensing and estimation method is developed, employing an argmax-based interior point optimization to estimate integer and fractional DD shifts with low computational complexity. A simple detection strategy combining least-squares residuals and maximum like-lihood power detection accurately recovers data and index bits. The algorithm’s efficiency is analyzed in terms of computational complexity and compared with existing OTFS THz-ISAC systems. Simulations show that the proposed framework achieves improved BER performance, low computational complexity, range root-mean-square error (RMSE) below 10−3m and a velocity RMSE on the order of 10−1m/s. It also allows flexible trade-offs among communication metrics such as peak-to-average power ratio (PAPR) and BER and achieves a PAPR reduction of 3–5 dB compared to prior methods, demonstrating its effectiveness and adaptability for diverse THz-ISAC deployment scenarios.

Pinching antennas (PAs), a special class of leaky-wave antennas (LWAs), have recently emerged as a promising technology to mitigate blockage and reduce large-scale path loss. However, PAs suffer from performance limitations due to their passive radiation characteristics and mechanical actuation. To overcome these limitations, we introduce reconfigurable LWAs (RLWAs), which enable RLWA beamforming through electronic control of both radiation amplitudes and phases. By integrating RLWAs into the existing PA systems (PASS), we propose a hybrid PASS (H-PASS) architecture, which combines mechanically reconfigurable PA beamforming with electronically reconfigurable RLWA beamforming. Given that PAs in H-PASS can be deployed in discrete-position or continuous-position manners, H-PASS comes in two variants accordingly, and we formulate weighted sum-rate maximization problems for them, respectively. For the discrete-position case, we propose a penalty-based two-loop joint analog and digital beamforming algorithm. For the continuous-position case, we propose an alternating-minimization-based joint position optimization and beamforming algorithm. Simulation results demonstrate that H-PASS can increase the sum-rate of the existing PASS by up to 33%, reduce the performance loss caused by phase quantization in discrete-position PAs by up to 69%, and mitigate the performance loss caused by position errors in continuous-position PAs by up to 90%. Overall, H-PASS significantly improves the performance of existing PASS by leveraging the strengths of RLWAs while mitigating the limitations of PAs.

In this paper, we investigate near-field wideband channel estimation with block sparsity. First, we propose an on-grid total variation-regularized block sparse Bayesian learning (TV-BSBL) algorithm. By constructing sparse representations of near-field wideband channels, we show that the sparse coefficient vectors exhibit both common sparsity across subcarriers and block sparsity in the surrogate distance-angle domain. To promote common sparsity, a Gamma prior combined with subcarrier-adaptive factors is utilized. To encourage block sparsity, TV regularization is incorporated into the prior model. The channel estimation is formulated as a maximum a posteriori (MAP) problem and solved via an expectation-maximization (EM) algorithm. Then, in the M-step, we further propose a primal-dual hybrid gradient-based signal hyperparameter update algorithm, which admits simple primal and dual updates and guarantees convergence to the global optimum. Moreover, we propose an off-grid TV-BSBL algorithm for near-field wideband channel estimation. We introduce additional variables to characterize the deviations between the true channel parameters and their quantized grids. These deviation variables are then integrated into the MAP estimation framework and refined via gradient descent. Simulation results demonstrate that the proposed on-grid and off-grid TV-BSBL algorithms achieve superior estimation performance under various conditions.

For millimeter wave communications, beams used to transmit synchronization signal blocks (SSBs) affect both base station coverage and beam training overhead. We therefore consider fair SSB codebook design, formulated as an optimization problem, aiming to maximize the minimum average signal-to-interference-plus-noise ratio (SINR) across user clusters. This problem is challenging due to the non-smoothness of the objective function, arising from the optimal beam-pair selection function and the minimum operator. To address this, we propose a double-loop framework, where the outer loop constructs approximations for the selection function with iteratively reduced error, and the inner loop solves the resulting approximate problems. In each inner-loop iteration, the objective function of the approximate problem is smoothed with iteratively reduced smoothness, enabling gradient derivation. This gradient is then estimated using variance-reduced estimators based on samples from users, and the result is used to update codebooks. Following this framework, we develop both first-order (FO) and zeroth-order (ZO) oracle schemes. The FO scheme requires full channel state information samples for gradient estimation while the ZO scheme only requires SINR samples. Simulation results show that in given scenarios, both schemes achieve SINR fairness comparable to or better than that of discrete Fourier transform codebooks, but with fewer beams.

In this paper, we propose a tri-hybrid beamforming (THBF) architecture based on the radiation-center (RC) reconfigurable antenna array (RCRAA), including the digital beamforming, analog beamforming, and electromagnetic (EM) beamforming, where the EM beamformer design is modeled as RC selection. Aiming at spectral efficiency (SE) maximization subject to the hardware and power consumption constraints, we propose a tri-loop alternating optimization (TLAO) scheme for the THBF design, where the digital and analog beamformers are optimized based on the penalty dual decomposition in the inner and middle loops, and the RC selection is determined through the coordinate descent method in the outer loop. Aiming at energy-efficiency (EE) maximization, we develop a dual quadratic transform-based fractional programming (DQTFP) scheme, where the TLAO scheme is readily used for the THBF design. To reduce the computational complexity, we propose the Lagrange dual transform-based fractional programming (LDTFP) scheme, where each iteration has a closed-form solution. Simulation results demonstrate the great potential of the RCRAA in improving both SE and EE. Compared to the DQTFP scheme, the LDTFP scheme significantly reduces the computational complexity with only minor performance loss.

Recently, an emerging flexible-antenna technology, termed pinching antennas, has attracted growing academic interest. By inserting discrete dielectric materials, pinching antennas can be activated at arbitrary points along waveguides, allowing for flexible customization of channel conditions. This paper investigates a multi-waveguide pinching-antenna integrated sensing and communications (ISAC) system, where transmit pinching antennas (TPAs) and receive pinching antennas (RPAs) coordinate to simultaneously detect one potential target and serve one downlink user. We formulate a communication rate maximization problem subject to radar signal-to-noise ratio (SNR) requirement, transmit power budget, and the allowable movement region of the TPAs, by jointly optimizing TPA locations and transmit beamforming design. To address the non-convexity of the problem, we propose a novel fine-tuning approximation method to reformulate it into a tractable form, followed by a successive convex approximation (SCA)-based algorithm to obtain the solution efficiently. Furthermore, we derive the closed-form optimal solution for a special multi-waveguide case involving a single TPA. Extensive simulations validate both the system design and the proposed algorithm. Results show that the proposed method achieves near-optimal performance compared with the computational-intensive exhaustive search-based benchmark, and pinching-antenna ISAC systems exhibit a distinct communication-sensing trade-off compared with conventional systems.

This work addresses the problem of joint robust transmission, reflection, and reception strategy design in an active reconfigurable intelligent surface (ARIS)-assisted multiuser multiple-input multiple-output (MIMO) system. Specifically, a signal-to-interference-noise (SINR) maximization problem has been formulated by jointly optimizing the transmit beamforming matrix at the base station (BS), the linear reception filters at the users, and the reflection coefficient matrix at the ARIS. The optimization has been performed under constraints on the BS transmit power, the maximum amplification power of the ARIS, and the maximum amplitude coefficients of the ARIS. To jointly optimize RIS-assisted systems, this paper proposes an efficient deep learning (DL) model. Specifically, a multi-layer perceptron (MLP)-based deep neural network (DNN) has been designed to effectively approximate the optimal solution. Further, to handle channel state information (CSI) uncertainties arising from estimation errors and environmental variations, a novel uncertainty injection scheme has been proposed for training DL models. The output of the solution is perturbed through uncertainty injection. The model learns a robust beamforming matrix, linear reception filters, and reflection configurations that maintain high SINR under worst-case channel conditions. Simulation results demonstrate that, for the optimized phase and ARIS configuration, the proposed DL trained with the UI scheme achieves a 26.23% SINR improvement compared to the model trained without UI (WUI). In addition to the ARIS, the performance of the passive reconfigurable intelligent surface has also been analyzed. Further, the time complexity and robustness of the proposed model have been evaluated.

In this article, we investigate the performance of a holographic active reconfigurable intelligent surface (HARIS)-aided near-field (NF) uplink non-orthogonal multiple access (NOMA) secure communication system with an imperfect channel state information (iCSI) in the presence of an eavesdropper (Eve). In order to provide efficient resource utilization, a sum secrecy rate (SSR) maximization problem is formulated, where the combining vector at base station (BS), power allocation at each uplink user, and the HARIS phase profile are jointly optimized under the strict constraints of quality of service (QoS) requirement and limited power budget at each uplink user and HARIS considering norm-bounded CSI uncertainty. In order to tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that adopts an iterative approach and uses optimization techniques such as semidefinite programming (SDP), convex upper bound approximation, and semidefinite relaxation (SDR) to optimize all three design variables simultaneously. Then, extensive simulations are performed to validate the efficacy and convergence of the proposed algorithm. Furthermore, we also demonstrate the impact of key system parameters, such as HARIS elements, minimum QoS corresponding to each user, the total power budget at uplink users, and maximum amplification factor at the HARIS. It is shown that the use of NOMA can achieve up to 45% higher performance compared to SDMA, OMA, and TDMA. It is also highlighted that, under the proposed algorithm with NF assumptions, the achieved average SSR is around 65% higher compared to the hybrid and far-field (FF) assumptions.

In this paper, we investigate a wireless powered communication network (WPCN) in which a multi-antenna hybrid access point (HAP) communicates with multiple Internet-of-Things (IoT) devices, assisted by an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS). In the energy transfer (ET) phase, the IoT devices harvest energy from the HAP with a nonlinear energy harvesting (EH) model, and subsequently transmit information signals to the HAP during the information transmission (IT) phase. To explore its full potential, the aSTAR-RIS employs energy splitting (ES), mode switching (MS), and time switching (TS) protocols. A sum rate maximization problem is formulated for each protocol, which jointly optimize the beamforming at the HAP, allocation of time slots and transmitting power for the IoT devices, and the adaptation of the aSTAR-RIS coefficients. To address the optimization problem with multiple coupled variables and complex non-convex constraints, we firstly decompose it into several subproblems. Specifically, to optimize the coefficients of the aSTAR-RIS in the IT phase, we develop a fractional programming-based successive convex approximation algorithm to handle the fractional objective function and the minimum rate constraints. Moreover, to obtain the coefficients of the aSTAR-RIS during the ET phase, we design a penalty-based SCA algorithm to address the binary constraints in the MS protocol and the rank-one constraints. Numerical results demonstrate that 1) employing the aSTAR-RIS in WPCNs can realize the extraordinary sum rate gain in comparison with the benchmarks of the active RIS and the passive STAR-RIS; 2) among the three operation protocols, the ES demonstrates the best performance, with the MS following closely behind, while the TS is the least effective; 3) as the minimum required data rate for each IoT device decreases, the performance gap among the three protocols becomes narrower.

In the literature of pinching-antenna systems, in-waveguide attenuation is often neglected to simplify system design and enable more tractable analysis. However, its effect on overall system performance has received limited attention in the existing literature. While a recent study has shown that, in line-of-sight (LoS)-dominated environments, the data rate loss incurred by omitting in-waveguide attenuation is negligible when the communication area is not excessively large, its effect under more general conditions remains unclear. This work extends the analysis to more realistic scenarios involving arbitrary levels of LoS blockage. We begin by examining a single-user case and derive an explicit expression for the average data rate loss caused by neglecting in-waveguide attenuation. The results demonstrate that, even for large service areas, the rate loss remains negligible under typical LoS blockage conditions. We then consider a more general multi-user scenario, where multiple pinching antennas, each deployed on a separate waveguide, jointly serve multiple users. The objective is to maximize the average sum rate by jointly optimize antenna positions and transmit beamformers to maximize the average sum rate under probabilistic LoS blockage. To solve the resulting stochastic and nonconvex optimization problem, we propose a dynamic sample average approximation (SAA) algorithm. At each iteration, this method replaces the expected objective with an empirical average computed from dynamically regenerated random channel realizations, ensuring that the optimization accurately reflects the current antenna configuration. Extensive simulation results are provided to the proposed algorithm and demonstrate the substantial performance gains of pinching-antenna systems, particularly in environments with significant LoS blockage.

This paper investigates the beamforming for the reconfigurable intelligent surface (RIS)-aided millimeter wave integrated sensing and communication system. We propose a fractional programming (FP) and alternating optimization-based hybrid beamforming (HBF) scheme. The weighted sum of the signal-to-clutter-and-noise-ratio at the radar receiver and the smallest signal-to-interference-plus-noise ratio among all communication users is maximized under the hardware constraints. Since it is difficult to directly obtain a solution for this non-convex FP problem, it is divided into three sub-problems that are alternately solved. Two sub-problems optimizing the digital transceiving beamforming at the base station (BS) are transformed into typical convex quadratic constraint quadratic programming ones using quadratic transformation. The other sub-problem optimizing the RIS passive beamforming is transformed into a manifold optimization one using Dinkelbach transformation. In addition, we consider the HBF structure at the BS through substituting the fully digital beamformer by the digital and analog ones. To reduce the computational complexity, a low-complexity HBF scheme based on Rayleigh quotient, zero-forcing and discrete Fourier transform codewords is proposed with closed-form expressions. Simulation results verify the effectiveness of two proposed schemes.

Reconfigurable distributed antenna and reflecting surface (RDARS) has been recently proposed as a promising technology. This architecture enables each element to perform flexibly either in the reflection mode, like the traditional passive reconfigurable intelligent surface (RIS), or in the connection mode, akin to the distributed antenna system (DAS). This dual capability allows RDARS to harness both reflection gain and distribution gain. In this paper, we investigate a dynamic RDARS-aided multiple-input multiple-output communication system, where the optimal configuration of the elements operating in connection mode can provide additional selection gain. Considering the theoretical and practical significances, we address the achievable rate maximization problem by jointly optimizing the mode selection, transmit power allocation and passive beamforming under both perfect and imperfect channel state information (CSI) cases. Due to the involvement of the mode selection design of RDARS, the problem is more challenging than those of the traditional RIS-aided systems with fixed reflection operation. For perfect CSI case, by investigating the inherent properties of the objective function, we propose a greedy-based alternating optimization (AO) algorithm with low-complexity and then extend the proposed algorithm to the general multi-user multi-RDARS scenario. Additionally, we find interesting insights about the mode selection of RDARS in a special scenario with a single-antenna user. The result shows that the RDARS elements leading to the largest distribution gain should be selected to operate in connection mode for the rate maximization. For imperfect CSI case, we develop an efficient alternative direction method of multipliers-based AO algorithm. Numerical results show that RDARS-assisted system outperforms the passive-RIS assisted system and DAS under both perfect and imperfect CSI scenarios with promising reflection, distribution and selection gains.

Space-air-ground integrated networks (SAGINs) are revolutionizing wireless communications by integrating space, aerial, and terrestrial networks, providing global connectivity, aiding underserved regions, and enabling 6G applications. This paper proposes a dual reconfigurable intelligent surfaces (RISs)-assisted cooperative hybrid free space optical (FSO)/ radio frequency (RF) communication framework for SAGINs to address the reliable communications challenges. The framework integrates an optical RIS on a low Earth orbit satellite and an RF aerial RIS, enhancing FSO link performance and mitigating RF line-of-sight blockages. The system uses selection combining to dynamically choose the stronger signal between FSO and RF links, ensuring robust communication in varying conditions. We derive analytical expressions for key performance metrics such as outage probability, average symbol error rate, and ergodic capacity, considering pointing errors and atmospheric turbulence effects on the FSO link. Simulation results verify the analytical derivations, highlighting the critical role of RISs in enhancing the system performance.

In this paper, we explore a 6G-enabled space-air-ground integrated network (SAGIN) framework that integrates ground communication hubs (CHs), a UAV with mobile edge computing (MEC) capabilities, and a constellation of low Earth orbit (LEO) nano-satellites. We formulate a joint optimization problem for UAV trajectory, task offloading, and satellite load balancing, modeled as a mixed-integer nonlinear programming (MINLP) problem. To solve this, we propose a quantum-enhanced advantage actor-critic (QEA2C) reinforcement learning algorithm that employs quantum neural networks and two quantum state encoding methods: amplitude encoding (AE) and higher-order encoding (HOE). Simulation results show that HOE achieves superior performance in terms of convergence speed, cumulative rewards, and learning efficiency, successfully serving all CHs with a well-optimized UAV trajectory. Meanwhile, AE achieves better cost minimization with lower resource consumption, making it a more practical option when computational efficiency is a priority. Moreover, these results highlight the trade-offs between learning performance and cost efficiency in quantum-enhanced decision-making for managing 6G-enabled SAGINs.

This study presents a dynamic UAV swarm framework to support ground networks in disaster zones. The framework leverages Generative AI (GenAI) for real-time hover point generation to guide waypoint-based UAV navigation and realistic task modeling, integrated with graph neural networks (GNN) for safe navigation and obstacle avoidance. A multi-agent graph reinforcement learning (MAGRL) mechanism optimizes UAV coordination, enhancing energy efficiency, task completion, and load balancing in response to environmental changes. The framework's graph attention mechanism further improves inter-UAV communication, enabling adaptive task allocation and efficient coverage of high-risk zones. Extensive simulations show that the integrated GenAI-GNN and MAGRL approach achieves superior performance in task completion, energy savings, and system utility, outperforming benchmarks including MADDPG, GCRL, PSO, and Greedy strategies in dynamic disaster scenarios.

In millimeter wave communications, the initial access stage involves a trade-off between beam sweeping overhead and base station coverage. To balance this, we consider site-specific fair synchronization signal block codebook design, formulated as an optimization problem, aiming to maximize the minimum average signal-to-noise ratio (SNR) across user clusters, subject to constant modulus constraints. To solve this, we propose a double-loop framework, where the outer loop relaxes the original problem into sub-problems using an augmented Lagrangian (AL) algorithm, and the inner loop solves these sub-problems using a hybrid variance-reduced (HVR) stochastic gradient descent (SGD) algorithm. In each inner iteration, the non-smooth objective function of the sub-problem, comprising numerous user-wise SNR functions, is approximated by a differentiable function, with its gradient estimated using HVR estimators. The estimated gradient is then used to update the codebook. Simulation results demonstrate the efficiency of the proposed AL-HVR-SGD scheme.

This paper addresses the problem of reliable task offloading in space-air-ground integrated network (SAGIN) based edge computing systems. Specifically, we aim to maximise the successful task offloading ratio for ground users communicating with a satellite's edge server. In our network topology, end-to-end communications are facilitated by relay unmanned aerial vehicles (UAVs). The formulated problem jointly optimises task offloading portions and bandwidth allocations for both ground-to-air and air-to-space links, subject to quality-of-service (QoS) requirements, transmission rates, system bandwidth, and the computing capacity of the satellite's edge server. To solve the formulated complex non-linear, non-convex, and mixed-integer problem, we propose an efficient solution underpinned by a deep reinforcement learning (DRL). Simulation results demonstrate the effectiveness of the proposed method, which achieves stable training performance and an optimised reliable offloading ratio compared to benchmark schemes.

This paper introduces a network architecture that integrates the space-air-ground integrated network with mobile edge computing (MEC) and orbital edge computing to advance sixth-generation communication systems. The proposed system employs unmanned aerial vehicles equipped with reconfigurable intelligent surfaces and satellite-based MEC to optimize resource management in complex, dynamic environments. By efficiently managing resources such as bandwidth and computational power at both base stations and low Earth orbit satellites, while making offloading decisions, the system aims to minimize utility costs while meeting stringent performance requirements. We utilize a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) algorithm to solve the formulated nonlinear programming problem, enabling dynamic and adaptive resource management. The LSTM-enhanced DDPG improves convergence speed by 44.44 % compared to conventional DDPG, significantly enhancing cost efficiency. Simulation results validate the robustness of the proposed method against state-of-the-art approaches.

In this paper, we address the complex mixed-integer nonlinear programming problem associated with channel assignment and joint power-energy allocation in urban platoon-based cellular-vehicle-to-everything (C-V2X) networks. In this context, the potential advantages of integrating quantum neural networks (QNNs) with classical multi-agent deep reinforcement learning (MADRL) approaches are investigated. Specifically, we combine a variational quantum circuit (VQC) with traditional neural networks to develop a hybrid quantum-classical neural network for the MADRL training process. Our goal is to employ this hybrid quantum-classical approach to simultaneously minimise the average age of information (AoI) which quantifies the freshness of information exchange between vehicle platoons and the roadside unit (RSU), maximise the cooperative awareness message (CAM) exchange probability among vehicles within the same platoon, and foster sustainable, green communication strategies through efficient management for both power and energy. We introduce the innovative decomposed multi-agent deep deterministic policy gradient (DE-MADDPG) algorithm, which is integrated with the twin delayed deep deterministic policy gradient (TD3) technique and advanced quantum computing technologies, resulting in our proposed hybrid quantum-classical decomposed multi-agent TD3 (DE-MATD3) algorithm. Compared with classical approaches, our numerical results reveal that the proposed algorithm achieves exceptional energy efficiency performance, while maintaining the algorithm convergence rate and AoI levels.

The Δ-Σ analog-to-digital converter (ADC), with the modulator as its core component, has posed considerable challenges to the designers, due to the complex topologies and instability issues. Thanks to reinforcement learning (RL), appropriate models can be trained to automatically generate efficient modulator structures without the need for prior datasets. Proximal policy optimization (PPO), one of the latest and most promising branches of RL, can be optimally used by virtue of its simplicity and less hyperparameter tuning. This study focuses on multi-agent PPO (MAPPO) for the design of high-order Δ-Σ modulators, with two agents handling topology and parameter optimization respectively in a cooperative way. We address the challenge of both efficiency and stability through proper mathematical formulation and effective integration of weighted objectives. Through extensive simulations and iterative processes of MAPPO, our proposed methodology demonstrates effectiveness in maximizing the efficiency and stability objectives of the desired Δ-Σ modulator via a reward mechanism.

In analog and mixed-signal integrated circuits (ICs), low-dropout regulators (LDOs) are crucial for maintaining a stable power supply throughout the IC. As such, designing LDOs with both time and quality efficiency has attracted substantial research interest. This paper presents an implementation of multi-agent proximal policy optimization (MAPPO) in both separated-parameter and parameter-sharing configurations to address the challenges of multi-objective, multi-variable LDO design. Our experiments show that parameter-sharing MAPPO outperforms both separated-parameter MAPPO and single-agent PPO in exploration and convergence, benefiting from cooperative learning via parameter sharing, which accelerates the identification of optimal design configurations. In summary, our findings indicate that parameter-sharing MAPPO efficiently manages complex specifications and variables.

Effective maritime search and rescue (SAR) requires fast, coordinated action from Internet of Maritime Things (IoMT) nodes operating under extreme communication, energy, and environmental constraints. Existing solutions treat semantic sensing, digital twin modeling, and decentralized control in isolation, limiting their responsiveness and scalability. We propose SEMADT-RL, a unified framework that integrates semantic-driven communication, predictive digital twin forecasting, and decentralized multi-agent deep reinforcement learning with graph attention networks (MADRL-GNN). The semantic layer enables lightweight, anomaly-triggered updates, significantly reducing bandwidth while preserving critical detection cues. The digital twin assimilates these updates using an extended Kalman filter to forecast survivor drift and node dynamics. These forecasts guide decentralized agents that collaboratively optimize mobility, processing, and transmission policies under dynamic and constrained maritime conditions. Simulation results demonstrate that SEMADT-RL achieves faster survivor detection, lower communication overhead, and higher energy efficiency than state-of-the-art baselines, providing a scalable solution for next-generation IoMT-assisted SAR operations.

The Age of Information (AoI) is a critical metric in next-generation communication networks, quantifying data freshness essential for latency-sensitive applications in 6G systems, such as autonomous driving and industrial IoT. This paper presents an AoI analysis within a downlink full-duplex (FD) cooperative simultaneous wireless information and power transfer (SWIPT) system, employing rate-splitting multiple access (RSMA) for short packet communication to enhance timely data updates. By integrating RSMA with SWIPT and FD capabilities, we propose a robust framework to reduce the AoI. In this regard, closed-form expressions of the average block error rate of the RSMA-enabled FD cooperative SWIPT system are derived and validated via Monte Carlo simulations. The results demonstrate that RSMA outperforms non-orthogonal multiple access (NOMA) and FD cooperative SWIPT NOMA in terms of error performance, while also reducing the inherent system design complexity. Our findings reveal that RSMA is a promising approach for minimizing AoI across various system configurations, offering valuable insights for designing future 6G networks that prioritize low latency, high reliability, and data freshness.

Uncrewed aerial vehicles (UAVs) are essential for providing communication and computation services in disaster recovery scenarios where traditional infrastructure is compromised. However, challenges related to energy efficiency, real-time adaptability, coverage, load balancing, and safe navigation persist, particularly in dynamic disaster environments. In this study, we propose a comprehensive framework that integrates generative AI (GenAI) with graph neural networks (GNNs) to dynamically generate hover points for waypoint-based UAV navigation and realistic task generation based on environmental conditions. The GNN-based collision avoidance mechanism further ensures safe navigation by allowing UAVs to avoid obstacles and no-fly zones while coordinating with neighboring UAVs in real time. To optimize UAV swarm operations, we introduce a multiagent graph reinforcement learning (MAGRL) framework, enabling UAVs to maximize overall system utility by refining hover point selection, task allocation, and load balancing in response to environmental changes. A graph attention mechanism enhances UAV coordination, improving communication efficiency and decision-making. Extensive simulations show that the proposed GenAI-GNN and MAGRL framework significantly outperforms existing methods in task completion, energy efficiency, and overall system utility in disaster recovery scenarios.

Intelligent radios play a pivotal role in optimizing communication resources for both commercial and military applications. Automatic signal identification (ASI) serves as a crucial component for intelligent radios, with likelihood-based and feature-based ASI algorithms being conventional approaches. Recent studies have explored the integration of machine learning (ML) algorithms for ASI, revealing their enhanced resilience to channel distortions compared to traditional methods. This article proposes the application of an extreme learning machine (ELM), a type of the ML algorithm, for the identification of cellular signals based on over-the-air measurements of power spectral density (PSD). The proposed ELM undergoes evaluation using two distinct datasets of PSDs to assess identification accuracy, with the first dataset utilized for hyperparameter optimization and the second unseen dataset employed to evaluate robustness and generality. The experimental results showcase improved performance in both accuracy and training complexity compared to recent work in the literature.

Maritime transportation is essential for global trade but presents significant environmental challenges due to its greenhouse gas emissions. Existing studies have addressed these challenges through integrated routing and speed optimization frameworks, yet frequently lack explicit quantification of environmental impacts and exhibit limited scalability for large-scale ship routing operations. Conversely, existing quantum optimization research in vehicle routing predominantly targets land-based transportation scenarios, restricting its direct applicability to maritime logistics. Maritime logistics inherently involve distinct operational complexities, such as nonlinear interactions among speed, payload, fuel consumption, and numerous operational uncertainties. These combined limitations underscore the critical need for quantum optimization methods explicitly designed for green maritime supply chains. To bridge this gap, this paper proposes an efficient quantum-centric optimization framework that uses the quantum approximate optimization algorithm (QAOA) to jointly optimize ship routing and speed management within sustainable maritime supply chains. Specifically, we formulate an NP-hard cost minimization problem integrating critical maritime parameters, including fuel consumption, payload constraints, and operational speeds. We further develop a hybrid quantum-classical alternating optimization approach that iteratively addresses routing decisions through quantum computing techniques and optimizes ship speed using an analytical solution. Simulation results and real quantum hardware experiments demonstrate that our quantum-centric methodology achieves substantial cost reductions and highlights the potential for practical applicability in realistic maritime operations, significantly outperforming classical optimization benchmarks.

The extension of elastic optical network (EON) technologies to multi-band transmission (MB-EON) promises enhanced spectral efficiency, greater throughput, and long-term cost benefits for telecom operators. However, designing such networks presents challenges, particularly in optimizing physical parameters like optical power and quality of transmission (QoT) across different frequency bands. This paper introduces a methodology for optimal span-by-span power allocation using two hyper-accelerated power optimization (HPO) modes: flat launch power (FLP) and flat received power (FRP). This methodology significantly accelerate network power optimization while ensuring service stability in scenarios such as changes in network parameters, QoT degradation due to aging, and network re-optimization or upgrading. Through a comprehensive comparison, we find that FRP notably improves signal flatness and GSNR/OSNR, particularly in the S-band, contributing to a network-wide throughput increase in the order of 12% to 75%. Additionally, we demonstrate that HPO applied to global power optimization is simpler and more cost-effective than when applied to local methods for large-scale networks.

This work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensuring convergence to optimal policies within complex operational environments. The proposed quantum-aided DRL framework that operates in a military surveillance system exploits quantum computing’s parallel processing capabilities to encode operational states and actions into quantum states, significantly reducing the dimensionality of the decision space. For the very first time in literature, we propose a quantum-enhanced actor-critic method, utilizing quantum circuits for policy representation and optimization. Through comprehensive simulations, we demonstrate that our framework improves DoA estimation accuracy by 91.66% and 82.61% over existing DRL algorithms with faster convergence rate, and effectively manages the trade-off between sensing and communication and by optimizing task offloading decisions under stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively.

Integrated sensing and communication (ISAC) is an emerging technique to enable radar and communication systems deployment on a shared hardware, channel characteristics, signal processing methods, etc. This integration improves the deployment efficiency and requires more sophisticated resource allocation and optimization techniques. The ISAC signal is designed to sense targets and to carry private information which could be at risk of being eavesdropped. This paper considers an ISAC framework in which a set of aerial eavesdroppers poses the threat of intercepting the downlink communication from a base station to a set of users. The eavesdroppers are moving, and their unknown locations are estimated based on the echo signal. A maximum likelihood-based scheme is developed to estimate the eavesdroppers’ channels, including coarse estimation with refines to estimate each eavesdropper’s complex channel gain, elevation and azimuth angles. The corresponding Cramér-Rao lower bounds of the estimated parameters are also provided. Given that the eavesdroppers are moving, a long short-term memory (LSTM) deep network is employed to predict their channels and also to enable a less frequent estimation process. Meta-learner LSTM is also presented to provide few-shot learning and provide generalization capability to any trajectory with a few fine-tuning steps. Based on the predicted eavesdroppers’ channels, two secure precoding algorithms are developed based on successive convex approximation and zero forcing techniques to improve the sum secrecy rate for the users. Simulation results illustrate that the developed framework provides substantial improvement in communication secrecy when compared with other benchmark approaches.

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

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

This paper proposes a dual-band reconfigurable antenna array (DBRAA), enabling wireless capabilities in both sub-6 GHz (sub-6G) and millimeter wave (mmWave) bands using a single array. For the sub-6G band, we propose a reconfigurable antenna selection structure, where each sub-6G antenna is formed by multiplexing several mmWave antennas, with its position dynamically adjusted using PIN diodes. For the mmWave band, we develop a reconfigurable hybrid beamforming structure that connects radio frequency chains to the antennas via phase shifters and a reconfigurable switch network. We then investigate integrated sensing and communications (ISAC) in sub-6G and mmWave bands using the proposed DBRAA and formulate a dual-band ISAC beamforming design problem. This problem aims at maximizing the mmWave communication sum-rate subject to the constraints of sub-6G communication quality of service and sensing beamforming gain requirements. The dual-band ISAC beamforming design is decoupled into sub-6G beamforming design and mmWave beamforming design. For the sub-6G beamforming design, we develop a fast search-based joint beamforming and antenna selection algorithm. For the mmWave beamforming design, we develop an alternating direction method of multipliers-based reconfigurable hybrid beamforming algorithm. Simulation results demonstrate the effectiveness of the proposed methods.

Orthogonal time frequency space (OTFS) modulation and massive multi-input multi-output (MIMO) are promising technologies for next generation wireless communication systems for their abilities to counteract the issue of high mobility with large Doppler spread and mitigate the channel path attenuation, respectively. The natural integration of massive MIMO with OTFS in millimeter-wave systems can improve communication data rate and enhance the spectral efficiency. However, when transmitting wideband signals with large-scale arrays, the beam squint effect may occur, causing discrepancies in beam directions across subcarriers in multi-carrier systems. Moreover, the high-mobility wideband millimeter wave communications can induce the Doppler squint effect, leading to different Doppler shifts among the subcarriers. Both beam squint effect and Doppler squint effect (denoted as doubly squint effect) can degrade communication performance significantly. In this paper, we present an efficient channel estimation and hybrid precoding scheme to address the doubly squint effect in massive MIMO-OTFS systems. We first characterize the wideband channel model and the input-output relationship for massive MIMO-OTFS transmission considering doubly squint effect. We then mathematically derive the impact of channel parameters on chirp pilots under the doubly squint effect. Additionally, we develop a peak-index-based channel estimation scheme. By leveraging the results from channel estimation, we propose a hybrid precoding method to mitigate the doubly squint effect in downlink transmission scenarios. Finally, simulation results validate the effectiveness of our proposed scheme and show its superiority over the existing schemes.

Integrated sensing and communication (ISAC) technology has been explored as a potential advancement for future wireless networks, striving to effectively use spectral resources for both communication and sensing. The integration of reconfigurable intelligent surfaces (RIS) with ISAC further enhances this capability by optimizing the propagation environment, thereby improving both the sensing accuracy and communication quality. Within this domain, accurate channel estimation is crucial to ensure a reliable deployment. Traditional deep learning (DL) approaches, while effective, can impose performance limitations in modeling the complex dynamics of wireless channels. This paper proposes a novel application of conditional generative adversarial networks (CGANs) to solve the channel estimation problem of an RIS-assisted ISAC system. The CGAN framework adversarially trains two DL networks, enabling the generator network to not only learn the mapping relationship from observed data to real channel conditions but also to improve its output based on the discriminator network feedback, thus effectively optimizing the training process and estimation accuracy. The numerical simulations demonstrate that the proposed CGAN-based method improves the estimation performance effectively compared to conventional DL techniques. The results highlight the CGAN’s potential to revolutionize channel estimation, paving the way for more accurate and reliable ISAC deployments.

In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-FEDCOM, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-FEDCOM is strengthened by key components like quantum sequential-training-program, with reinforcement learning-based dynamic mode switching to reduce communication costs and manage vehicle states adaptively, and the quantum vehicle-context-grouping utilizing hierarchical clustering and simulated annealing for effective vehicle grouping based on contextual data similarity, addressing the complexities of data heterogeneity. Additionally, the integration of quantum-inspired principal component analysis (Q-PCA) enhances memory efficiency, further optimizing the framework. These elements converge in the QV-FEDCOM algorithm, establishing a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Our study also introduces an innovative quantum trajectory loss (QTL) function, specifically designed for trajectory prediction tasks, which combines the Huber loss with an angular deviation penalty to robustly handle errors and penalize large deviations in the predicted trajectory angle. The effectiveness of the QV-FEDCOM framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.

The learning of fundamental quantum properties—namely coherence, discord, and entanglement—benchmarks the security, computational, and metrological capability of noisy intermediate-scale quantum (NISQ) communication, computing, and sensing networks. The current learning techniques vary widely for these fundamental quantum properties, including standard tomographic procedures that involve exhaustive optimization. Fortunately, the fundamentally distinct quantum properties feature an intricate connection. In this paper, we put forth the concept of universal quantum witness machines (UQWMs) to develop a unified framework for quantum property learning (QPL) of a quantum system. We first formulate the certification and quantification of quantum properties based on quantum witnesses. The witness-based certification method is experimentally accessible and resource-efficient but lacks reliability and generality. To universalize the scope and circumvent the unreliability, we transform the certification task into a classification task by employing UQWMs with classical machine learning to construct quantum property classifiers. This formalism offers a unifying perspective on the certification, quantification, and classification of these enigmatically linked fundamental quantum properties. To demonstrate our UQWM approach, we provide a comparative numerical analysis of quantum property quantification with quantum witnesses and classification performance analysis of quantum property classification with convolutional neural networks, specifically for$4 \times 4$quantum systems.

Reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) are considered promising technologies for the sixth generation (6G) wireless communication. The deployment of RIS within the mmWave ISAC system can achieve better communication performance and sensing accuracy. The mmWave band signals can be utilized to enhance transmission rates and available bandwidth significantly. However, the increased size of the RIS array and bandwidth introduces the beam-squint effect, which impacts the performance of RIS-aided communication and sensing. In this paper, we analyze the beam-squint and beam-split effects on a uniform planar array of RIS. Moreover, we derive controllable beam-squint and beam-split ranges based on true-time-delay (TTD) lines and propose RIS-aided sensing schemes with beam-squint and beam-split for a mmWave ISAC system. The proposed schemes can utilize both time-domain and frequency-domain resources for beam scanning, which reduces the time overhead compared to traditional beam scanning schemes. Simulation results illustrate the effectiveness of the proposed RIS-aided user sensing schemes.

This paper investigates simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multi-user downlink (dl) communications with a primary focus on maximizing information secrecy by considering the channel state information (CSI) error. Acquiring perfect CSI is particularly challenging due to the unavailability of radio frequency chains at the STAR-RIS, the inherent impact of noise and interference on the CSI estimation, as well as non-collaborative nature of the eavesdroppers. In particular, we tackle the worst-case robust beamforming design problem to maximize the sum secrecy rate of the system while considering transmit power limitations, quality of service requirements, and practical constraints on the STAR-RIS phase shifter array. To tackle the resulting non-convex problem, we employ the S-procedure as an initial step to approximate semi-infinite inequality constraints. Subsequently, we leverage the alternating optimization with a line search framework to update the precoder and phase shift matrix iteratively. Furthermore, we extend our solution to address the non-convexity by leveraging a deep reinforcement learning (DRL) multi-agent (MA) framework based on Markov decision process. We also analyze practical phase shifts and the effect of direct links to showcase the practicality of our approach. Simulation results confirm STAR-RIS’s significant performance edge, exhibiting approximately 27.1% higher secrecy in conventional optimization and around 35.4% in the MA-DRL context compared over the conventional RIS. Moreover, our proposed MA-DRL approach surpasses single-agent schemes by about 8.6% in the case of proximal policy optimization and 19.9% in the case of deep deterministic policy gradient, emphasizing the benefits of the MA framework with STAR-RIS.

In this paper, a reconfigurable intelligent surface (RIS)-assisted full duplex communication for quadrature channel modulation (QCM) is proposed. The proposed scheme exploits transmit antennas and radio frequency mirror selection to transmit the real and imaginary parts of a QCM symbol independently to improve spectral efficiency. A dual polarized RIS is also employed to extend the indexing of receive antennas at a dual polarized half duplex receiver to separate the real and imaginary parts of the QCM symbol transmitted during downlink communication. This setup facilitates quadrature spatial modulation at the half duplex receiver and further enhances spectral efficiency. After transmit antenna selection, the remaining silent antennas at the base station are further harnessed to receive the QCM modulated uplink symbol from the half duplex transmitter unit. A decision-making method is adopted at the base station to find these silent antennas that receive uplink signals. Each antenna functions as either a transmit or a receive antenna with the help of a duplexer switch which assists in full duplex communication with minimal self-interference. Finally, an enhanced greedy detector is proposed to detect the signal with lower computational complexity. The system performance is evaluated using measures like average bit error rate, throughput, ergodic capacity, and energy efficiency, and compared with that of the existing state-of-the-art techniques.

In this paper, we investigate super-resolution angle estimation (SRAE) for reconfigurable intelligent surface (RIS)-aided mmWave communications. For the RIS-aided narrowband system, based on beam sweeping using a wide-beam codebook, we propose a two-step SRAE (TS-SRAE) scheme. In the first step, the selected optimal wide beam is refined to a narrow beam. In the second step, we develop an angle quantization error correction method. Then, for the RIS-aided wideband system, we propose an adaptive codebook design scheme, where the angle domain is divided into two regions, including the central region and the edge region, regarding the beam squint effect. Based on the beam sweeping using the adaptive codebook, we propose a two-region SRAE (TR-SRAE) scheme. In the central region, we extend the TS-SRAE scheme for angle estimation. In the edge region, we formulate the angle estimation as a maximum-a-posteriori problem, which is then solved by our developed Bayesian inference method. Simulation results demonstrate that both TS-SRAE and TR-SRAE schemes can effectively reduce the training overhead and improve the achievable rate.

This paper develops a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted symbiotic radio (SR) system, in which the STAR-RIS is deployed to transmit extra Internet of Things (IoT) data and simultaneously enhance the downlink transmission. A simple and efficient ON-OFF keying modulation scheme is applied by the STAR-RIS to modulate IoT data, which allows a low-complexity IoT transceiver and avoids the signal ambiguity. This work aims to maximize the weighted sum-rate (WSR) of downlink users, subject to the minimum received energy requirement of IoT transmission. Under the assumption of perfect channel state information (CSI), an efficient penalty dual decomposition (PDD)-based algorithm is proposed to solve the WSR maximization problem. By leveraging the PDD framework, the STAR-RIS’s coefficients are updated with close-form expressions. For the imperfect CSI case, the WSR maximization problem becomes a challenging stochastic optimization task. To address it, the constrained stochastic successive convex approximation framework is employed. Additionally, an efficient projection method is proposed to handle the STAR-RIS’s amplitude and coupled phase-shift constraints. Simulation results reveal the performance trade-off between the downlink transmission and the IoT transmission and validate the superiority of the proposed algorithms over the benchmarks.

Cell-free (CF) massive multiple-input multiple-output (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Rate-splitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and non-convexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments.

The allocation of secure resources in non-orthogonal multiple access (NOMA) systems has gained significant recognition as a vital research focus in the realm of the Internet of Things (IoT). Previous studies have overlooked the security challenges associated with integrating NOMA with finite blocklength (FBL) transmission. Therefore, this paper examines a secure downlink NOMA system utilizing FBL transmission, which includes a base station (BS), a near user, a far user, and an external eavesdropper. We develop an optimization problem with the objective of maximizing the near user’s effective secrecy throughput, considering the secrecy rates, decoding error probabilities (DEPs), and effective secrecy throughput for both users. Notably, by meticulously defining the DEPs of the users as optimization variables, the monotonicity and concavity of these DEPs in relation to the blocklength, transmission power, and transmission rate can be established effectively. The problem is divided into two sub-problems focusing on the essential conditions for the secrecy rate of the near user, especially in scenarios where successive interference cancellation (SIC) is unsuccessful. These sub-problems are addressed using the block coordinate descent (BCD) algorithm and an exact penalty method. For comparison, the BCD algorithm is also applied to solve the optimization problem using the orthogonal multiple access (OMA) scheme. Numerical simulations confirm the effectiveness of our proposed approaches in improving secure resource allocation when NOMA is combined with FBL transmission.

The integration of terrestrial and non-terrestrial networks with mobile edge computing (MEC) and orbital edge computing (OEC) technologies is essential for advancing 6G communication networks. This paper introduces a network architecture that combines terrestrial and non-terrestrial networks by integrating drones (also known as UAV)-carried reconfigurable intelligent surfaces (RIS) and satellite-based MEC to optimize resource allocation in intelligent autonomous transportation systems (IATS). The primary objective is to minimize total system utility costs through the optimal allocation of bandwidth, computational power at the base station and low Earth orbit (LEO) satellite, and offloading decisions, all while adhering to strict performance and delay constraints. We address the complex resource optimization challenge by formulating a nonlinear programming (NLP) problem. To solve this problem, we employ long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) and LSTM-enhanced twin delayed deep deterministic policy gradient (TD3) algorithms, which enable dynamic and adaptive resource management. These LSTM-enhanced algorithms improve convergence speed by 44.44% and 73.81%, respectively, compared to their conventional counterparts, while significantly enhancing cost efficiency. Our simulation results demonstrate substantial improvements in system performance, with effective resource allocation and minimal utility costs, providing a robust solution for ensuring high-quality, low-latency communication in diverse 6G IATS environments.

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated.

For high-speed train (HST) millimeter wave (mmWave) communications, the use of narrow beams with small beam coverage needs frequent beam switching, while wider beams with small beam gain leads to weaker mmWave signal strength. In this paper, we consider beam switching based beam design, which is formulated as an optimization problem aiming to minimize the number of switched beams within a predetermined railway range subject to that the receiving signal-to-noise ratio (RSNR) at the HST is no lower than a predetermined threshold. To solve this problem, we propose two sequential beam design schemes, both including two alternately-performed stages. In the first stage, given an updated beam coverage according to the railway range, we transform the problem into a feasibility problem and further convert it into a min-max optimization problem by relaxing the RSNR constraints into a penalty of the objective function. In the second stage, we evaluate the feasibility of the beamformer obtained from solving the min-max problem and determine the beam coverage accordingly. Simulation results show that compared to the first scheme, the second scheme can achieve 96.20% reduction in computational complexity at the cost of only 0.0657% performance degradation.

This paper presents a state-of-the-art analytical framework aimed to enhance spectral efficiency in satellite and terrestrial integrated networks (STINs), utilizing reconfigurable intelligent surface (RIS) within the realm of integrated sensing and communication (ISAC). Our methodology pivots on a pioneering federated deep reinforcement learning strategy that introduces new ground beyond conventional optimization techniques to tackle the intricate problem of non-convex resource allocation. The approach leverages federated learning to dynamically adapt to network changes, enabling efficient resource management and ensuring compliance with beamforming designs, multiple target signal-to-interference-plus-noise ratio thresholds, and RIS phase-shift requirements through an effective feedback loop. In particular, we propose a federated deep deterministic policy gradient (F-DDPG) algorithm across multi-agent systems that outperforms existing federated deep Q-network (F-DQN), centralized, and traditional DDPG and DQN methods. The empirical findings underscore the efficiency of the federated algorithms, which closely align with the performance of centralized models while markedly reducing execution time, thus achieving an optimal synergy between operational efficiency and system performance. Simulation results highlight the remarkable advantages of optimal RIS configurations, showcasing a performance increase of 54.2% over random RIS setups and a remarkable 76.8% enhancement compared to scenarios without RIS, underscoring the transformative impact of our federated learning approach. Additionally, our study evaluates the impact of channel estimation errors and interference, confirming the robustness of our approach and its potential to optimize ISAC-enabled STINs.

The integration of non-orthogonal multiple access (NOMA) into low earth orbit (LEO) systems has the potential to facilitate the ubiquitous coverage with high spectrum efficiency. To characterize the fundamental limits of NOMA assisted LEO systems, this paper proposes a model for downlink NOMA-LEO system via modelling the locations of terrestrial users and LEO satellites as two homogeneous spherical Poisson point processes. In particular, a typical satellite uses NOMA to simultaneously serve the nearest and the farthest users within its visible range. The novelty of this paper is to first introduce an equivalent two-dimensional model that can significantly simplify the performance analysis of LEO systems. Then, considering that the satellite-terrestrial channel follows the Nakagami-mfading, the closed-form expressions of the user association and the visible probability are studied under the scenario where the number of users visible to a randomly selected satellite is greater than one. Subsequently, the derived results are utilized to analyze the approximate moments of the conditional success probability and the signal-to-interference-plus-noise ratio Meta distribution for both NOMA-LEO and orthogonal multiple access (OMA) LEO transmissions. Finally, the numerical results demonstrate that:1)Asymmetric target rates can achieve a performance gain of NOMA over OMA in terms of the link reliability and the coverage probability, while symmetric settings still have merit for NOMA if there is a low requirement for reliability; and2)Enhancements in the link reliability and the coverage probability are achievable through improvements in channel quality and reductions in orbital altitude and density. However, improving path loss develops the coverage probability but may not always yield an increase in the link reliability.

This paper considers an integrated sensing and communication (ISAC) system framework, in which an aerial eavesdropper poses the threat to intercept the downlink communication from a base station to a set of users. The eavesdropper is moving and its unknown location is estimated based on the echo signal. A maximum likelihood-based scheme is developed to estimate the eavesdropper channel, which performs a coarse estimation and further refines the estimated parameters. A long short-term memory deep network is employed to predict the eavesdropper channel and also to enable a less frequent estimation process. Based on the predicted eavesdropper’s channel, a precoding optimization algorithm is developed to improve the sum secrecy rate for the users. Simulation results illustrate that the developed framework provides substantial improvement in the communication secrecy when compared with other benchmark approaches.

This paper presents a novel analytical framework for minimizing transmit power in satellite and terrestrial integrated networks using reconfigurable intelligent surface (RIS) technology within integrated sensing and communication systems. We employ a cutting-edge federated deep reinforcement learning approach, utilizing a federated deep deterministic policy gradient (F-DDPG) algorithm, to tackle the complex non-convex power minimization problem effectively. The proposed F- DDPG approach surpasses the federated deep Q-network (DQN), traditional DDPG, and DQN techniques by dynamically adapting to network changes, enabling efficient resource management and compliance with beamforming designs, multiple target and user signal-to-interference-plus-noise ratio thresholds, and RIS phase-shift requirements. Simulation results confirm that the use of RIS can significantly lower power requirements at the base station and maintain a critical balance between efficient power management and strategic resource allocation.

The advancement of sophisticated communication technologies and robust computing systems has unlocked opportunities for new applications across various domains. While these applications promise enhanced convenience and improved living standards, they also raise a critical concern regarding the trade-off between convenience and environmental sustainability. This paper addresses this concern by investigating sustainable resource management, employing a digital twin approach to minimise CO2emissions in edge computing systems. Specifically, our aim is to reduce the amount of CO2emissions by optimising the allocation of computing and communication resources. This includes optimising transmit power, adjusting the clock speed for task processing, and making optimal decisions regarding task offloading. To tackle this complex optimisation problem, we employ an iteratively alternating optimisation algorithm. Through extensive simulations, we illustrate the efficacy of our proposed solution in not only mitigating CO2emissions but also optimising resource allocation, thereby contributing to both environmental sustainability and technological efficiency.

As mobile devices become increasingly popular and the need for indoor localization services grows, the localization of indoor mobile users is becoming more and more popular. However, the instability of received signal strength in the actual environment will have a detrimental influence on indoor localization, and the large multi-story buildings will also create new challenges. In this paper, we put forward a localization model with the graph-based location mapping network. The connection mode of the access points is used to construct a graph describing the location of reference points and users. Also, the graph neural networks are used to extract graph-level representation. This model can effectively capture the misaligned features. Moreover, the proposed approach is assessed on two public datasets, i.e., UJIIndoorLoc and UTSIndoorLoc, and the performance is evaluated against several leading-edge methods. Experimental results demonstrate that the proposed model outperforms existing solutions.

This paper proposes a downlink non-orthogonal multiple access (NOMA) low earth orbit (LEO) satellite system by modeling the locations of terrestrial users and LEO satellites as two homogeneous spherical Poisson point processes, respectively. Firstly, considering that the satellite-terrestrial channel follows the Nakagami-m fading, the closed-form expressions of the user association and the visible probability are studied under the scenario where the number of user visible to the random selected satellite is greater than one. Subsequently, the above results are adopted to analyze the approximate results for the moments of the conditional success probability and the signal-to-interference-plus-noise ratio Meta distribution. The numerical results demonstrate that the asymmetric target rates can realize a performance gain of NOMA over orthogonal multiple access in terms of the link reliability and the coverage probability, while symmetric settings still have a merit for NOMA when there is a low requirement for the link reliability.

The continuous monitoring of activities of daily living (ADLs) can play a vital role in assessing an individuals capability to live independently and enable the possibility for early disease detection. This paper introduces a novel hybrid model, called quantum-based gated recurrent unit - multiclass classifier (QGRU-MC), to enhance ADL classification from wear-able sensor data. Using statistical feature extraction from the raw accelerometer sensor signals, the QGRU-MC model demonstrates good performance in activity recognition. Preliminary findings suggest that our model has good potential in healthcare applications, and in particular, can contribute to the advancement of future intelligent systems centered on daily activity monitoring and the promotion of healthy aging.

In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-MetaFL, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-MetaFL is strengthened by the quantum sequential-training-program (Q-STP) algorithm, a quantum-based sequential training program that transforms model training, reducing communication costs and adeptly managing vehicle states. Complementing this, the quantum vehicle-context-grouping (Q-VCG) mechanism groups vehicles based on contextual data similarity, effectively tackling the complexities of data heterogeneity. The synergy of Q-STP and Q-VCG culminates in the QV-MetaFL algorithm, a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Additionally, our research introduces an innovative composite loss function that amalgamates classical loss metrics with quantum parameter regularization, deftly addressing quantum sensitivity to noise. The effectiveness of the QV-MetaFL framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.

In this paper, we investigate beam training for multiuser millimeter wave massive MIMO. To reduce the training overhead, a super-resolution wide-beam training scheme including three stages is proposed. In the first stage, we perform beam sweeping based on a wide-beam codebook, where a multipath detection method based on extreme point detection is proposed to generate candidate wide-beam pairs for multiuser beam allocation. In the second stage, we propose a narrow-beam prediction method to refine the allocated wide-beam pair. In the third stage, a super-resolution angle estimation method which can break through the resolution limitation is proposed to further calibrate the channel angle-of-arrival and angle-of-departure of the predicted narrow-beam pair. Simulation results demonstrate that the proposed scheme can approach the performance of the existing beam sweeping with only a quarter of the training overhead.

Despite advancement in artificial intelligence (AI) and subsequent successful applications in a vast variety of areas, addressing progressive need in electronic design automation for high performance and fine precision remains a significant challenge. Optimal layout placement design, a notably time-consuming task, often poses an issue to the conventional design process with an aim of maintaining high circuit performance. To mitigate this problem, in this paper we propose an AI-based optimization method to automate this process with better accuracy. We utilize a reinforcement learning (RL) method, advantage actor critic (A2C), to implement a full automation procedure for analog circuit layout design. A topological representation is employed to decrease the size of states in the optimization process. In addition, we have considered the foggy effect caused by electron-beam lithography (EBL) technology. Our simulation results demonstrate the remarkable efficacy of our approach, which can achieve 44 times smaller foggy effect variation and reduce the run time by 42 times in comparison with the analytical and another RL-based (DQN) method, without compromising the wire length and chip area minimization.

This study explores implementing a digital twin network (DTN) for efficient 6 G wireless network management, aligning with the fault, configuration, accounting, performance, and security (FCAPS) model. The DTN architecture comprises the Physical Twin Layer, implemented using NS-3, and the Service Layer, featuring machine learning and reinforcement learning for optimizing carrier sensitivity threshold and transmit power control in wireless networks. We introduce a robust “What-if Analysis” module, utilizing conditional tabular generative adversarial network for synthetic data generation to mimic various network scenarios. These scenarios assess four network performance metrics: throughput, latency, packet loss, and coverage. Our findings demonstrate the efficiency of the proposed what-if analysis framework in managing complex network conditions, highlighting the importance of the scenario-maker and the impact of twinning intervals on network performance.

With the rapid development of communication applications, the integration of reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) techniques has emerged as a promising approach to enhance connectivity and data transmission rate in future wireless networks. To successfully deploy RIS-NOMA aided 6G network, an accurate channel estimation is a crucial task. Quantum machine learning (QML) is a novel approach showing potential computational advantages in various problems of 6G wireless communications. However, its application, particularly in channel estimation, remains largely theoretical rather than adopted in practice. We propose a hybrid quantum-classical neural network model based on convolutional neural network (CNN) and quantum long short-term memory (QLSTM) for channel estimation in RIS-aided 6G NOMA system. Our results show that the proposed CNN-QLSTM model has a better channel prediction compared to its classical counterpart with regard to root mean square error (RMSE) and mean absolute error (MAE).

Grant-free non-orthogonal multiple access (GF-NOMA) based on multiple-input multiple-output (MIMO) has attracted much attention as a promising technique to support massive connectivity and bursty data transmission in massive machine-type communication. In this paper, we propose two compressed sensing based multiuser detection (MUD) algorithms for the MIMO-enabled GF-NOMA system. First, the spatially enhanced variational Bayesian inference (SE-VBI) algorithm is developed for MUD by exploiting the Gaussian mixture prior and diversity combining technique. Then, by applying the covariance-free (CoFe) strategy to the SE-VBI framework to estimate the diagonal elements of the posterior covariance, we propose a low-complexity MUD method named SE-CoFe-VBI. In particular, the proposed algorithms integrate the multivariate nature of the transmitted signal, i.e., discreteness, sparsity, and spatial correlation. Simulation results show that the proposed algorithms offer improved detection performance over the state-of-the-art spatially enhanced sparse Bayesian learning method.

One of the Sustainable Development Goals of the United Nations is affordable and clean energy. True utilization of renewable energy is only possible via battery-based energy storage systems. Overseeing the operation of battery-based energy storage systems and diagnosing abnormal batteries are of the utmost importance for their durability and stability. Because of inadequate anomalous samples and privacy considerations, we jointly train a global autoencoder on various battery-based energy storage systems to detect anomalous batteries. Due to potentially unstable network connectivity in energy storage systems, a chunk of model parameters may be lost during model transmission, leading to dramatic performance deterioration. The trained model tends to classify all measurements as anomalies. To solve this problem, we propose a robust federated learning scheme to mitigate negative impact caused by packet loss during model transmission. By permuting and unpermuting model parameters before and after model transmission, we are able to distribute the lost parameters across the entire model. Such a loss can no longer have a significant negative impact on anomalous battery detection. Experimental results illustrate that the proposed algorithm is robust against packet loss during the model exchange between the cloud server and battery-based energy storage systems.

Integrated sensing and communication (ISAC) is expected to be a promising technology in the sixth-generation (6G) wireless networks for its ability to alleviate resources shortage and excessive hardware expenses. One typical representative for ISAC waveforms is the orthogonal frequency division multiplexing (OFDM) waveform, which divides the time-frequency resources into orthogonal resource elements (REs). In order to satisfy their diverse design requirements and mitigate mutual interference, the communication and sensing subsystems can be assigned with different REs, which necessitates effective allocation strategies of different resources across time and frequency domains. In this article, a cross-domain multicarrier waveform design method-ology is proposed, which optimizes the RE assignment and power allocation strategies for the OFDM-based ISAC system. Specifically, for sensing performance enhancement, the unit cells of the ambiguity function (AF) of the sensing components are spe-cially shaped to achieve a “locally” perfect auto-correlation (AC) property within a predefined region of interest (RoI) in the Delay-Doppler domain. Afterwards, the irrelevant cells outside the RoI, which can determine the sensing power allocation strategy, are optimized alternatively with the communication power allocation strategy to maximize the throughput for the communication purpose. Numerical results demonstrate the superiority of the cross-domain multicarrier waveform design, which also provides useful guidelines for parameter settings of the proposed OFDM-based ISAC system.

The rapid development of next-generation Internet of Things (IoT) applications, including integrated-sensing-and-communication (ISAC), smart grids, smart cities, intelligent transport networks, etc., enables at least tens of billions of bandwidth-thirsty IoT devices, which consume a deluge of data in the sixth-generation (6G) communication systems. In addition, future challenging heterogeneous services and applications, such as Industry 4.0, require the provisioning of unprecedented massive device access, heterogeneous data traffic, high spectral efficiency, and low latency. As a result, there is an urgent demand to pay more attention to IoT networks for high-reliable and low-delay massive access.

To enable concurrent transmissions for Internet of Things (IoT) traffic in multiantenna beyond fifth generation networks, nonorthogonal multiple access (NOMA) mechanisms appear as a promising approach. For NOMA-enabled transmissions, IoT devices are grouped into clusters in order to exploit the benefit of concurrent transmissions. However, how to facilitate transmissions from both intra- and intercluster is not an easy task and the performance of such concurrent transmissions is so far not well understood from a mathematical point of view, especially when error-prone channel conditions are considered. In this article, we propose two random access schemes which enable intra- and intercluster concurrent transmissions for uplink IoT traffic with and without access control. To assess the performance of such systems, we develop two analytical models based on discrete-time Markov chains (DTMCs) that mimic the behavior of such transmissions. Our models deal with cluster-level performance considering dynamic packet arrivals and the transmissions from devices belonging to the same or different clusters. Through extensive simulations, we validate the accuracy of the analytical models and evaluate the system- and cluster-level performance in terms of throughput and delay under various traffic load conditions and network configurations.

This paper investigates the performance of physical layer security (PLS) in wireless communication systems, where a reconfigurable intelligent surfaces (RIS) is deployed between the transmitter and legitimate receiver to enhance the communication security. The Fisher-Snedecor F distribution is adopted to model the underlying fading channels, owing to its accuracy, tractability and generality. On this basis, this paper evaluates the performance of the proposed system by deriving the average secrecy capacity (ASC) and the secrecy outage probability (SOP) under correlated Fisher-Snedecor F channel coefficients. Furthermore, the asymptotic behavior of the ASC and SOP in the high signal-to-noise ratio (SNR) regime is examined. Analyzing the correlated scenario is crucial as it provides a detailed understanding of how interdependencies among channel coefficients impact the system’s security and overall performance, offering valuable insights into real-world communication scenarios. Finally, this paper verifies the analytical results through numerical illustrations, and demonstrates the effectiveness of employing RIS.

In this article, a hybrid radio frequency-visible light communication and positioning (RF-VLCP) system is designed, which can support high-data rate communication and high accuracy positioning with good energy efficiency (EE) performance. The hybrid system uses two links for downlink communication, namely, radio frequency (RF) and visible light communication (VLC) links, and employs visible light positioning (VLP) technology for positioning. Furthermore, an optimization problem is developed to allocate power for VLC and VLP links and to design beamformer for the RF transmitter. By doing so, the EE of the hybrid system is maximized while the Cramer–Rao Lower bound (CRLB) of the positioning error and the minimum data rate of the communication are guaranteed. A two-step algorithm is proposed to tackle the formulated optimization problem, which first determines the power allocation of the VLP signal and then obtains the power allocation of the VLC signal and the beamformer of the RF transmitter. Numerical results demonstrate the advantages of the proposed two-step algorithm over the existing algorithm in terms of computation speed. In addition, the EE performance of the hybrid system is evaluated under different data rate and positioning accuracy requirements. Besides, we also show that the hybrid RF-VLCP system is more energy efficient compared to standalone RF and VLP technologies.

The convergence of advanced communication technologies and powerful computing architecture has unlocked a plethora of opportunities for Internet-of-Things applications. To fully realize this potential, a synergistic design encompassing sensing, computing, and communication is crucial. This article investigates these critical technologies to facilitate service-oriented systems by minimizing end-to-end latency and the number of deployed services at edge servers in mobile edge computing, all within the confines of stringent ultrareliable and low-latency communication requirements and system budget constraints. The addressed optimization problem takes into account variables, such as service placement strategies, task offloading portions, and bandwidth allocation. Simulation results validate the effectiveness of our solution and highlight the impact of key parameters on system performance.

With the popularity of mobile devices and the increasing demand for indoor localization services, the localization of indoor mobile users is becoming more and more popular. However, many existing methods of building the radio map require collecting the received signal strength (RSS) of a large number of access points (APs), which causes high-hardware costs and large storage. Additionally, the instability of RSS in the actual environment will have a detrimental influence on indoor localization, and the large multistory buildings will also create new challenges. In this article, we propose a localization model with the combination of the AP selection network and the graph-based location mapping network. This model selects the optimal APs through the AP selection network and reduces the number of required APs. Then, the connection mode of the selected APs is used to construct a graph describing the location of reference points and users. Besides, the graph neural networks are used to extract graph-level representation, effectively capturing the misaligned features. Moreover, evaluated on the UJIIndoorLoc and UTSIndoorLoc data sets, the proposed method could not only reduce the number of required APs while ensuring localization performance but also outperform several state-of-the-art methods.

Quantum Byzantine fault tolerance (BFT) consensus is a secure and reliable mechanism that enables network nodes to reach an agreement even in the presence of faulty nodes, by using distributed private correlated lists. It plays a crucial role in developing the blockchain-based Metaverse to ensure its integrity and security. In this paper, we propose a counterfactual quantum BFT (CQ-BFT) protocol for a multipartite network using counterfactual unitary telecomputation with the chained quantum Zeno gates. This consensus protocol achieves an agreement among the parties without the passage of any physical particles through the quantum channel. Due to the unique properties of counterfactual communication, we demonstrate that the CQ-BFT protocol can operate in the absence of a shared phase reference and provide a quantum layer of security and robustness against dephasing noise, fulfilling the stringent requirements of blockchain technology. In addition, we analyze the performance tradeoff of the CQ-BFT protocol in terms of the three pillars of blockchain—i.e., security, scalability, and decentralization. The human-centric Metaverse could leverage high degrees of security, noise resilience, and fault tolerance of the CQ-BFT protocol to enhance its underlying network infrastructure. This protocol leads to more robust and immersive virtual environments that prioritize the needs and experiences of Metaverse users.

In this paper, we investigate the energy efficiency (EE) problem under reconfigurable intelligent surface (RIS)-aided secure cell-free networks, where multiple legitimate users and eavesdroppers (Eves) exist. We formulate a max-min security EE optimization problem by jointly designing the distributed active beamforming and artificial noise at base stations as well as the passive beamforming at RISs under practical constraints. To deal with it, we first divide the original optimization problem into two sub-ones, and then propose an iterative optimization algorithm to solve each sub-problem based on the fractional programming, constrained concave-convex procedure (CCCP) and semi-definite programming (SDP) techniques. After that, these two sub-problems are alternatively solved until convergence, and the final solutions are obtained. Next, we extend to the imperfect channel state information of the Eves’ links, and investigate the robust security EE beamforming optimization problem by bringing the outage probability constraints. Based on this, we first transform the uncertain outage probability constraints into the certain ones by the Bernstein-type inequality and sphere boundary techniques, and then propose an alternatively iterative algorithm to obtain the solutions of the original problem based on the S-procedure, successive convex approximation, CCCP, and SDP techniques. Finally, the simulation results are conducted to show the effectiveness of the proposed schemes.

Intelligent reflecting surface (IRS) has garnered growing interest and attention due to its potential for facilitating and supporting wireless communications and sensing. This paper studies a semi-passive IRS-enabled sensing system, where an IRS consists of both passive reflecting elements and active sensors. Our goal is to minimize the Cramér-Rao bound (CRB) for parameter estimation under both point and extended target cases. Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS). To efficiently solve the corresponding optimization problem for the case with a multi-antenna BS, we propose an efficient algorithm by jointly optimizing the IRS phase shifts and the BS beamformers. Under the extended target case, the CRB for the target response matrix (TRM) estimation is minimized via the optimization of the BS transmit beamformers. Moreover, we explore the influence of various system parameters on the CRB and compare these effects to those observed under the point target case. Simulation results show the effectiveness of the semi-passive IRS and our proposed beamforming design for improving the performance of the sensing system.

This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. We leverage quantum phenomena such as superposition and entanglement to work on large-scale multi-dimensional data represented by quantum states. Under stochastic behaviors and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantum-empowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed Qe-DRL algorithm and its superior computational learning speed. Our proposed Qe-DRL algorithm outperforms other benchmarks in terms of energy efficiency performance.

This paper investigates an integrated sensing and communication (ISAC) system with reconfigurable intelligent surface (RIS). Our simultaneous beam training and target sensing (SBTTS) scheme enables the base station to perform beam training with the user terminals (UTs) and the RIS, and simultaneously to sense the targets. Based on our findings, the energy of the echoes from the RIS is accumulated in the angle-delay domain while that from the targets is accumulated in the Doppler-delay domain. The SBTTS scheme can distinguish the RIS from the targets with the mixed echoes from the RIS and the targets. Then we propose a positioning and array orientation estimation (PAOE) scheme for both the line-of-sight channels and the non-line-of-sight channels based on the beam training results of SBTTS by developing a low-complexity two-dimensional fast search algorithm. Based on the SBTTS and PAOE schemes, we further compute the angle-of-arrival and angle-of-departure for the channels between the RIS and the UTs by exploiting the geometry relationship to accomplish the beam alignment of the ISAC system. Simulation results verify the effectiveness of the proposed schemes.

This paper investigates beam training for extremely large-scale multiple-input multiple-output systems. By considering both the near field and far field, a triple-refined hybrid-field beam training scheme is proposed, where high-accuracy estimates of channel parameters are obtained through three steps of progressive beam refinement. First, the hybrid-field beam gain (HFBG)-based first refinement method is developed. Based on the analysis of the HFBG, the first-refinement codebook is designed and the beam training is performed accordingly to narrow down the potential region of the channel path. Then, the maximum likelihood (ML)-based and principle of stationary phase (PSP)-based second refinement methods are developed. By exploiting the measurements of the beam training, the ML is used to estimate the channel parameters. To avoid the high computational complexity of ML, closed-form estimates of the channel parameters are derived according to the PSP. Moreover, the Gaussian approximation (GA)-based third refinement method is developed. The hybrid-field neighboring search is first performed to identify the potential region of the main lobe of the channel steering vector. Afterwards, by applying the GA, a least-squares estimator is developed to obtain the high-accuracy channel parameter estimation. Simulation results verify the effectiveness of the proposed scheme.

Automatic modulation classification (AMC) is of crucial importance in the sixth generation wireless communication networks. Deep learning (DL)-based AMC schemes have attracted extensive attention due to their superior accuracy compared with the conventional methods. However, a pure data-driven DL method relies on a large amount of labeled training samples and the classification accuracy is poor, especially in the low signal-to-noise ratio (SNR). In order to tackle this problem, two data-and-knowledge dual-driven AMC schemes are designed. A novel data and semantic knowledge driven AMC scheme is proposed by exploiting the semantic attribute information of different modulations. Moreover, a prior knowledge driven multi-task learning visual model is established to improve the classification performance in low SNR. Furthermore, another novel data and multi-domain knowledge joint driven AMC scheme is proposed by using the semantic attribute knowledge and the prior knowledge based multi-task learning visual model. Extensive simulation results demonstrate that our proposed data-and-knowledge dual-driven AMC schemes achieve the best performance compared with the benchmark schemes in terms of classification accuracy. Moreover, it is shown that the expert knowledge spawns for AMC accuracy improvement and a decrease in the required number of training samples.

In this paper, an intelligent reflecting surface (IRS) is leveraged to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). To ensure the sensing quality while preventing the eavesdropping, dedicated sensing signals are transmitted by the BS. We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. Based on the availability of perfect channel state information (CSI) of all involved user links and the potential target location of interest at the BS, two scenarios are considered and two different optimization algorithms are proposed. For the ideal scenario where the CSI of the user links and the potential target location are perfectly known at the BS, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. On the other hand, for the more practical scenario where the CSI is imperfect and the potential target location is uncertain in a region of interest, a robust algorithm based on the$\cal S$-procedure and sign-definiteness approaches is proposed. Simulation results demonstrate the effectiveness of the proposed scheme in achieving a trade-off between the communication quality and the sensing quality, and also show the tremendous potential of IRS for use in sensing and improving the security of ISAC systems.

This paper studies a communication-centric integrated sensing and communication (ISAC) system, where a multi-antenna base station (BS) simultaneously performs downlink communication and target detection. A novel target detection and information transmission protocol is proposed, where the BS executes the channel estimation and beamforming successively and meanwhile jointly exploits the pilot sequences in the channel estimation stage and user information in the transmission stage to assist target detection. We investigate the joint design of the pilot matrix, training duration, and transmit beamforming to maximize the probability of target detection, subject to the minimum achievable rate required by the user. However, designing the optimal pilot matrix is rather challenging since there is no closed-form expression of the detection probability with respect to the pilot matrix. To tackle this difficulty, we resort to designing the pilot matrix based on the information-theoretic criterion to maximize the mutual information (MI) between the received observations and BS-target channel coefficients for target detection. We first derive the optimal pilot matrix for both channel estimation and target detection, and then propose a unified pilot matrix structure to balance minimizing the channel estimation error (MSE) and maximizing MI. Based on the proposed structure, a low-complexity successive refinement algorithm is proposed. In addition, we rigorously analyze the impact of pilot length and pilot matrix on two fundamental tradeoffs, namely MSE-MI and Rate-MI. Simulation results demonstrate that the proposed pilot matrix structure can well balance the MSE-MI and the Rate-MI tradeoffs, and show the significant region improvement of our proposed design as compared to other benchmark schemes. Furthermore, it is unveiled that as the communication channel is more spatially correlated, the Rate-MI region can be further enlarged.

The advantages of graph neural networks (GNNs) in leveraging the graph topology of wireless networks have drawn increasing attentions. This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users’ individual data rate requirements and the power budget of the base station (BS). By modeling the MU-MISO network as a graph, a GNN-based architecture named complex residual graph attention network (CRGAT) is proposed to directly map channel state information to beamforming vectors. The attention-enabled aggregation and the residual-assisted combination are adopted to enhance the learning capability and mitigate the oversmoothing issue. Furthermore, a novel activation function is proposed for the constraint due to the limited power budget at the BS. The CRGAT is trained via unsupervised learning with two proposed loss functions. An evaluation method is proposed for the learning-based approaches, based on which the effectiveness of the proposed CRGAT is validated in comparison with several convex optimization and learning based approaches. Numerical results are provided to reveal the advantages of the CRGAT including the millisecond-level response with limited optimality performance loss, the scalability to different number of users and power budgets, and the adaptability to different system settings.

In multiple-input multiple-output (MIMO) systems, channel estimation is of crucial importance to guarantee reliable recovery of ultra-high-speed MIMO signals. This paper proposes a novel channel estimation algorithm for the emerging MIMO-based orthogonal chirp-division multiplexing (OCDM) systems by utilizing the unique features of OCDM signals. In the proposed algorithm, a set of pilot signals is designed based on the Fresnel basis, which is essentially a family of orthogonal linear chirps. The pilots are assigned to different antennas for transmission occupying the same time slot and bandwidth. According to the convolution-preservation theorem of the Fresnel transforms, the transfer matrices of MIMO-OCDM systems can be readily estimated at the receiver without any inter-antenna interference, even if the pilots overlap in both the time and frequency domains. The proposed algorithm avoids bandwidth waste in conventional channel estimators, in which silent pilots will be required in time and/or frequency to ensure the received MIMO pilots separable. We show that the proposed algorithm is unbiased for the unique OCDM pilots and has better estimate accuracy and system performance. Finally, analysis and numerical results are provided to validate its advantages as a promising algorithm for emerging wireless access technology based on MIMO-OCDM.

In this paper, we consider an active reconfigurable intelligent surface (RIS) to assist the multiuser downlink transmission in the presence of practical hardware impairments (HWIs), including the HWIs at the transceivers and the phase noise at the active RIS. The active RIS is deployed to amplify the incident signals to alleviate the multiplicative fading effect, which is a limitation in the conventional passive RIS-aided wireless systems. We aim to maximize the sum rate through jointly designing the transmit beamforming at the base station (BS), the amplification factors and the phase shifts at the active RIS. To tackle this challenging optimization problem effectively, we decouple it into two tractable subproblems. Subsequently, each subproblem is transformed into a second order cone programming problem. The block coordinate descent framework is applied to tackle them, where the transmit beamforming and the reflection coefficients are alternately designed. In addition, another efficient algorithm is presented to reduce the computational complexity. Specifically, by exploiting the majorization-minimization approach, each subproblem is reformulated into a tractable surrogate problem, whose closed-form solutions are obtained by Lagrange dual decomposition approach and element-wise alternating sequential optimization method. Simulation results validate the effectiveness of our developed algorithms, and reveal that the HWIs significantly limit the system performance of active RIS-empowered wireless communications. Furthermore, the active RIS noticeably boosts the sum rate under the same total power budget, compared with the passive RIS.

Secure communications are of paramount importance in spectrum sharing networks due to the allocation and sharing characteristics of spectrum resources. To further explore the potential of intelligent reflective surfaces (IRSs) in enhancing spectrum sharing and secure transmission performance, a multiple intelligent reflection surface (multi-IRS)-assisted sensing-enhanced wideband spectrum sharing network is investigated by considering physical layer security techniques. An intelligent resource allocation scheme based on double deep Q networks (D3QN) algorithm and soft Actor-Critic (SAC) algorithm is proposed to maximize the secure transmission rate of the secondary network by jointly optimizing IRS pairings, subchannel assignment, transmit beamforming of the secondary base station, reflection coefficients of IRSs and the sensing time. To tackle the sparse reward problem caused by a significant amount of reflection elements of multiple IRSs, the method of hierarchical reinforcement learning is exploited. An alternative optimization (AO)-based conventional mathematical scheme is introduced to verify the computational complexity advantage of our proposed intelligent scheme. Simulation results demonstrate the efficiency of our proposed intelligent scheme as well as the superiority of multi-IRS design in enhancing secrecy rate and spectrum utilization. It is shown that inappropriate deployment of IRSs can reduce the security performance with the presence of multiple eavesdroppers (Eves), and the arrangement of IRSs deserves further consideration.

Beam refinement is a key technology to overcome the problem of limited resolution in beam training. However, most existing works on beam refinement are not suitable for the emerging extremely large-scale multiple-input-multiple-output (XL-MIMO) due to the differences in the channel characteristics. To fill in the gap, in this paper, beam refinement for XL-MIMO systems is investigated. Inspired by the similarities between the Taylor series of the Gaussian function and that of the beam gain, we propose to approximate the beam gain by the Gaussian function. Then, a low-complexity beam refinement based on the Gaussian approximation (BRGA) scheme, which quantizes the narrowed intervals after beam training into several samples and performs additional channel tests on the quantized grids, is proposed to improve the estimation accuracy of the beam training. Based on the measurements in the beam refinement stage, the BRGA-based least square (BRGA-LS) estimator is developed for high-resolution channel parameter estimation. To avoid the noise amplification effects of the BRGA-LS, the BRGA-based weighted least square (BRGA-WLS) estimator is further developed. Simulation results verify the effectiveness of the proposed scheme and show that the proposed BRGA scheme can greatly improve the accuracy of beam training with only a few additional channel tests.

Millimeter wave (mmWave) communications can achieve high data-rate transmission for high-speed trains (HSTs). However, the rapid change in path loss during the fast movement of HSTs poses a significant challenge to the mm Wave beamforming design. In this paper, a two-stage beam-forming (TSB) scheme is proposed to address this challenge for downlink HST mmWave communications. In the first stage, an algorithm based on semi-definite relaxation (SDR) and alternating minimization (AM) is proposed to stabilize the instantaneous receive signal-to-noise ratio (SNR) above a predefined threshold when the HSTs travel along the railway. In the second stage, the coverage of each beam used by the base station (BS) is widened to reduce the number of beam switches. Simulation results demonstrate that the proposed scheme requires fewer BS beams to cover the same railway range than the existing schemes while keeping the instantaneous receive SNR of the HSTs above the predefined threshold.

In this paper, simultaneous beam training and target sensing for reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems is investigated. The sensing ability of base station (BS) is exploited to acquire the channel state information of the RIS-aided ISAC. Based on our findings that the energy of the echoes from the RIS can be accumulated in the angle-delay domain while the energy of the echoes from the targets can be accumulated in the Doppler-delay domain, we can distinguish the RIS from the targets. Then we propose a simultaneous beam training and target sensing scheme, which enables the BS to perform the beam training with the RIS and to sense the targets simultaneously based on the mixed echoes from the RIS and the targets, and also enables the user equipment (UE) to perform collaborative sensing to figure out their position. Moreover, the beam alignment between the BS and the UE via the RIS can be directly computed without additional beam training overhead. Simulation results verify the effectiveness of the proposed scheme and show that it outperforms the existing schemes with much smaller training overhead, which benefits from the integration of the sensing units.

In this paper, radar-aided multiuser beam tracking and target detection are investigated for integrated sensing and communication (ISAC). A multiuser beam tracking scheme based on the collaboration of radar sensing and uplink communication is proposed. It is first proved that the echoes of each communication signal can be extracted from the mixed echoes of multiple communication signals, by performing point-wise division and two-dimensional discrete Fourier transform. Based on it, the scheme enables the road side unit (RSU) to perform multiuser beam tracking and target detection with only two radio frequency chains, no matter how many users are served by the RSU. To distinguish the users from the targets and further improve the beam tracking by extended Kalman filtering, uplink pilots from the users to the RSU are employed. Then the digital beamformer can be designed to mitigate the multiuser interference. Simulation results show that the proposed scheme outperforms the conventional beam tracking scheme and can approach the performance of independent beam tracking of each user.

This paper investigates a practical distributed reconfigurable intelligent surface (RIS)-assisted full-duplex wireless system. For the first time in the literature, the system resources minimization problem is considered by jointly optimizing the RIS phase shifts and their states (ON/OFF) subject to target sum rate constraints. The paper further considers a discrete phase shift model at the RISs. As the formulated problem is mixed-integer and non-convex, it is decoupled into two sub-problems: transmit beamforming and joint RIS phase shifts and RIS elements state optimization. The former problem is mathematically addressed using approximate solutions, while the latter problem is addressed using a novel reinforcement learning (RL) approach. Simulation results illustrate that the proposed RL algorithm is flexible for different target rate constraints. The results further show that the proposed framework efficiently saves a considerable number of reflecting elements by configuring their state.

In this paper, we investigate the energy efficiency (EE) problem in downlink reconfigurable intelligent surface (RIS)-aided secure cell-free networks. First, we formulate a max-min secure EE (SEE) optimization problem via jointly optimizing the distributed beamforming at base stations and phase shifts at RISs under the constraint of each base station transmit power. To deal with it, we divide the original optimization problem into two sub-ones and propose an alternative scheme. Specifically, we develop an iterative optimization algorithm to solve each sub-one based on the fractional programming, constrained convex-convex procedure and semi-definite programming techniques. After that, these two sub-ones are alternatively solved until convergence, and then the final solutions are obtained. Finally, the simulation results show the effectiveness of the proposed algorithm.

Recently, the advances of low-latency communication technologies and edge intelligence have enabled a wide range of task-oriented time-sensitive applications. This paper aims at designing adaptive service placement, task offloading, and bandwidth allocation for ultra-reliable and low-latency communication (URLLC)-aided edge networks. The main objective is to minimise both the total end-to-end (e2e) latency and number of installed services at edge servers. The optimal solutions are obtained by jointly optimising service placement decisions, task offloading portions and bandwidth allocation at dynamic timescales subject to network budgets and application requirements under uncertain environment. Selective simulation results are provided to validate the effectiveness of the proposed solution in term of reducing the latency as well as optimising service placement decisions.

This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. Under stochastic behaviours and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantumempowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed QeDRL algorithm and its superior computational learning speed.

This article focuses on minimizing the energy consumption of a fleet of unmanned aerial vehicles (UAVs) disseminating information to a set of Internet of Things devices. In the considered scenario, each device wants to download a subset of files from a library of files. Considering the storage capacity of the UAVs, a framework is provided that minimizes energy consumption by optimally selecting the contributing UAVs, placing files, and planning the trajectory of each contributing UAV. In this framework, a combinatorial optimization problem is formulated, which is hard to solve directly for a practical number of devices, files, and/or UAVs. In order to tackle this challenge, we develop three solution approaches, namely, a multichromosome genetic algorithm (GA), a hybrid genetic-ant colony algorithm, and a GA with heuristic file placement. Results show that the proposed solution approaches minimize the total energy consumption and provide near-optimal solutions. Results also illustrate that the proposed framework optimizes the number of UAVs participating in the information delivery mission.

Deep learning (DL)-based specific emitter identification (SEI) is a potential physical layer authentication technique for Industrial Internet-of-Things (IIoT) Security, which detects the individual emitter according to its unique signal features resulting from transmitter hardware impairments. The success of DL-based SEI often depends on sufficient training samples and the integrity of samples’ labels. The extensive deployment of wireless devices generates a huge amount of signals, but signals labeling is quite difficult and expensive with the high demand for expertise. In this article, we present an SEI method based on dual consistency regularization (DCR), which enables feature extraction and identification using a few labeled samples and a large number of unlabeled samples. With the help of pseudo labeling, we leverage consistency between the predicted class distribution of weakly augmented unlabeled training samples and that of strongly augmented training unlabeled samples, and consistency between semantic feature distribution of labeled samples and that of pseudo-labeled samples, which takes the unlabeled samples into account to model parameter tuning for a more accurate emitter identification. Extensive numerical results demonstrate that compared with well-known semi-supervised learning-based SEI methods, our method obtains 99.77% identification accuracy on a WiFi data set and 90.10% identification accuracy on an automatic dependent surveillance-broadcast (ADS-B) data set when only 10% of training samples are labeled, and improves the identification accuracy on the WiFi data set and the ADS-B data set by more than 19.07% and 5.30%, respectively. Our codes are available athttps://github.com/lovelymimola/DCR-Based-SemiSEI.

Mobile-edge computing (MEC) and intelligent reflecting surface (IRS) are envisioned as two promising technologies that enable massive connectivity in the future Internet of Things (IoT) networks. MEC allows IoT devices (IDs) to offload their computation intensive tasks and, thus, can prolong their lifespan. In contrast, the IRS can enhance the channel condition between IDs and the access points (APs), which are co-located with the MEC server. Wireless power transfer technique enabling energy harvesting for IDs helps realizing sustainable IoT network. This article applies IRS in a multi-ID MEC system for better latency performance. We first propose a multiple access scheme with hybrid frequency-division and nonorthogonal access technologies and then design a timing protocol for the IDs. Based on the above design, we study the latency optimization problem with the joint optimization of power allocation, the IRS phase shift matrix, and uplink and downlink beamformer under maximum power constraint for the IDs and AP. To tackle the formulated multivariable nonconvex problem, we split the target problem into several subproblems and provide a near-optimal low-complexity ID clustering scheme. Afterward, we derive optimal solutions to these subproblems, and a low-complexity fast-convergence alternating algorithm is proposed to minimize the overall latency. Presented simulation results verify the convergence of the alternating algorithm, and its superiority over the benchmarks.

Nonorthogonal multiple access (NOMA) has been viewed as a potential candidate for the upcoming generation of wireless communication systems. Comparing to traditional orthogonal multiple access (OMA), multiplexing users in the same time-frequency resource block can increase the number of served users and improve the efficiency of the systems in terms of spectral efficiency. Nevertheless, from a security viewpoint, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential. In this context, physical layer security (PLS) has been introduced as a supplement of protection to conventional encryption techniques by making use of the random nature of wireless transmission media for ensuring communication secrecy. The recent years have seen significant interests in PLS being applied to NOMA networks. Numerous scenarios have been investigated to assess the security of NOMA systems, including when active and passive eavesdroppers are present, as well as when these systems, are combined with relay and reconfigurable intelligent surfaces (RISs). Additionally, the security of the ambient backscatter (AmB)-NOMA systems are other issues that have lately drawn a lot of attention. In this article, a thorough analysis of the PLS-assisted NOMA systems research state-of-the-art is presented. In this regard, we begin by outlining the foundations of NOMA and PLS, respectively. Following that, we discuss the PLS performances for NOMA systems in four categories depending on the type of the eavesdropper, the existence of relay, RIS, and AmB systems in different conditions. Finally, a thorough explanation of the most recent PLS-assisted NOMA systems is given.

The Internet of Things (IoT) is a major driving force for future sixth-generation (6G) wireless systems. With the emergence of various novel IoT applications, more data should be collected and transmitted. However, IoT devices are constrained by battery, transmit power, and processing capacity. Featured by line-of-sight communication links, favorable channels, and better coverage, aerial access networks have been proposed to facilitate data transmission from IoT devices. In parallel, by shifting the computing and storage resources from the cloud to the edge of the network, edge computing [e.g., fog and mobile-edge computing (MEC)] can better support various computing-intensive and low-latency IoT applications. The integration of aerial access networks and edge computing, so-called aerial computing, is anticipated to provide not only traditional communication services but also advanced services for the IoT on a global scale.

The Angle-of-Arrival (AoA)-based approach is an appealing solution for unmanned aerial vehicle (UAV) positioning, and has received significant interest recently. In this article, we propose a novel framework for UAV three-dimensional (3-D) positioning, the core of which is to measure the two-dimensional (2-D) Angle-of-Departure (2D-AoD) and 2D-AoA via a bistatic multiple-input multiple-output (MIMO) radar. Unlike the existing positioning architectures, the MIMO radar is equipped with polarized array antennas. An estimator based on the parallel factor (PARAFAC) decomposition is developed. It first obtains the direction matrices via performing the PARAFAC decomposition of the array data. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization response vector, from which the 2D-AoD, 2D-AoA, and polarization status of the UAVs are achieved via incorporating the vector cross-product method and the least squares (LSs) technique. Finally, the 3-D positions of the UAVs are easily calculated via the location relationship between the 2D-AoD, 2D-AoA, and the coordinates of transmitting/receiving (Tx/Rx) array. The proposed framework is computationally friendly, and is capable of positioning anonymous UAV. Moreover, it is insensitive to the geometry of the Tx/Rx array, indicating that the proposed framework supports configurable Tx/Rx antennas. Simulation results are provided to verify our theoretical advantages.

Ambient backscatter communication (AmBC) and non-orthogonal multiple access (NOMA) are two promising technologies for the future wireless communication networks owing to their high energy and spectral efficiencies. The AmBC-aided NOMA symbiotic radio is a promising technology because of possessing advantages of AmBC and NOMA. Nonetheless, when a number of devices with limited power and computation capability access to the AmBC-based NOMA symbiotic networks, communication security becomes a critical issue. In this paper, we investigate physical-layer authentication (PLA) to identify the users and prevent illegal access and malicious activities for AmBC-based NOMA symbiotic networks. Moreover, channel estimation errors are considered when calculating the probability of false alarm (PFA) and probability of detection (PD) of the far user and near user. To enhance the authentication performance, three PLA schemes for the considered networks are designed according to the multiplexing form of the authentication tags: i) PLA with shared authentication tag (PLA-SAT); ii) PLA with space division multiplexing authentication tags; iii) PLA with time-division multiplexing authentication tags. To characterize the proposed PLA schemes, we first derive the PFA and the PD of the considered AmBC-based NOMA symbiotic networks. Then, the covertness is studied in terms of outage probability and asymptotic behavior in the high signal-to-noise ratio regime. Extensive analytical and computer simulated results show that: i) The PLA-SAT scheme has better performance than the other two authentication schemes with the same threshold; ii) The outage performance of systems employing authentication schemes is worse than those without authentication; iii) There exists a trade-off between robustness and covertness.

Multi-user integrated sensing and communication (ISAC) assisted by intelligent reflecting surface (IRS) has been recently investigated to provide a high spectral and energy efficiency transmission. This paper proposes a practical channel estimation approach for the first time to an IRS-assisted multi-user ISAC system. The estimation problem in such a system is challenging since the sensing and communication (SAC) signals interfere with each other, and the passive IRS lacks signal processing ability. A two-stage approach is proposed to transfer the overall estimation problem into sub-ones, successively including the direct and reflected channels estimation. Based on this scheme, the ISAC base station (BS) estimates all the SAC channels associated with the target and uplink users, while each downlink user estimates the downlink communication channels individually. Considering a low-cost demand of the ISAC BS and downlink users, the proposed two-stage approach is realized by an efficient neural network (NN) framework that contains two different extreme learning machine (ELM) structures to estimate the above SAC channels. Moreover, two types of input-output pairs to train the ELMs are carefully devised, which impact the estimation accuracy and computational complexity under different system parameters. Simulation results reveal a substantial performance improvement achieved by the proposed ELM-based approach over the least-squares and NN-based benchmarks, with reduced training complexity and faster training speed.

Reconfigurable intelligent surface (RIS) is able to create favorable reflecting channels for different users and piggyback additional data in the reflected signals. The former brings benefits to non-orthogonal multiple access (NOMA), while the latter enables a mechanism of symbiotic radio (SR). Inspired by these unique advantages, we consider a general SR-NOMA system model where an RIS is deployed to assist both the NOMA in an uplink multi-channel system and the Internet-of-Things (IoT) data transmission. This general model also allows for different performance objectives from the NOMA users. In particular, the users can be either energy-efficiency oriented or spectrum-efficiency oriented. To strike the performance trade-off between these two types of users, a performance metric called resource efficiency (RE) is leveraged to formulate the optimization problem. We jointly design the time-frequency resource allocation, multi-user power control and RIS phase shifts to maximize the weighted sum-RE of the system, subject to the quality-of-service constraints of the SR-NOMA system. An efficient alternating optimization framework with a series of algorithms, including matching theory, fractional programming method, and inner majorization-minimization method, is developed to solve this highly complex and non-convex problem.

The satellite-terrestrial networks (STN) utilize the spacious coverage and low transmission latency of the Low Earth Orbit (LEO) constellation to transfer requested content for subscribers especially in remote areas. With the development of storage and computing capacity of satellite onboard equipment, it is considered promising to leverage in-network caching technology on STN to improve content distribution efficiency. However, traditional caching and distribution schemes are not suitable in STN, considering dynamic satellite propagation links and time-varying topology. More specifically, the unevenness of user distribution heightens difficulties for assurance of user quality of experience. To address these problems, we first propose a density-based network division algorithm. The STN is divided into a series of blocks with different sizes to amortize the data delivery costs. To deploy the caching satellites, we analyze the link connectivity and propose an approximate minimum coverage vertex set algorithm. Then, a novel cache node selection algorithm is designed for optimal subscriber matching. On the basis of time-varying network model, the STN cache content updating mechanism is derived to enable a stable and sustainable quality of user experience. The simulation results demonstrate that the proposed user-oriented STN content distribution scheme can obviously reduce the average propagation delay and network load under different network conditions and has better stability and self-adaptability under continuous time variation.

Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distributed manner. While a similarity metric can provide client groups with specialized models according to their data distribution, this process can be time-consuming because the server needs to capture all data distribution first from all clients to perform the correct clustering. Due to resource and time constraints at the network edge, only a fraction of devices is selected every round, necessitating the need for an efficient scheduling technique to address these issues. Thus, this paper introduces a two-phased client selection and scheduling approach to improve the convergence speed while capturing all data distributions. This approach ensures correct clustering and fairness between clients by leveraging bandwidth reuse for participants spent a longer time training their models and exploiting the heterogeneity in the devices to schedule the participants according to their delay. The server then performs the clustering depending on predetermined thresholds and stopping criteria. When a specified cluster approximates a stopping point, the server employs a greedy selection for that cluster by picking the devices with lower delay and better resources. The convergence analysis is provided, showing the relationship between the proposed scheduling approach and the convergence rate of the specialized models to obtain convergence bounds under non-i.i.d. data distribution. We carry out extensive simulations, and the results demonstrate that the proposed algorithms reduce training time and improve the convergence speed by up to 50% while equipping every user with a customized model tailored to its data distribution.

Visible light communication (VLC) is envisioned as a core component of future wireless communication networks due to, among other reasons, the very large unlicensed bandwidth it offers and the fact that it does not cause any interference to existing radio frequency (RF) communication systems. In order to take advantage of both RF and VLC, most research on their coexistence has focused on hybrid designs where data transmission to any user could originate from either an RF or a VLC access point (AP). However, hybrid RF/VLC systems fail to exploit the distinct transmission characteristics (e.g., susceptibility of VLC transmissions to blockages, limited field-of-view of VLC APs and receivers, more coverage and better reliability of RF systems, etc.) of RF and VLC systems to fully reap the benefits they can offer. Aggregated RF/VLC systems, in which any user can be served simultaneously by both RF and VLC APs, have recently emerged as a more promising and robust design for the coexistence of RF and VLC systems. To this end, this paper, for the first time, investigates AP assignment, subchannel allocation (SA), and transmit power allocation (PA) to optimize the energy efficiency (EE) of aggregated RF/VLC systems while considering the effects of interference and VLC line-of-sight link blockages. A novel and challenging EE optimization problem is formulated for which an efficient joint solution based on alternating optimization is developed. More particularly, an energy-efficient AP assignment algorithm based on matching theory is proposed. Then, a low-complexity SA scheme that allocates subchannels to users based on their channel conditions is developed. Finally, an effective PA algorithm is presented by utilizing the quadratic transform approach and a multi-objective optimization framework. Extensive simulation results reveal that: 1) the proposed joint AP assignment, SA, and PA solution obtains significant EE, sum-rate, and outage performance gains with low complexity, and 2) the aggregated RF/VLC system provides considerable performance improvement compared to hybrid RF/VLC systems.

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

Integrated sensing and communication (ISAC) and intelligent reflecting surface (IRS) are viewed as promising technologies for future generations of wireless networks. This paper investigates the channel estimation problem in an IRS-assisted ISAC system. A deep-learning framework is proposed to estimate the sensing and communication (S&C) channels in such a system. Considering different propagation environments of the S&C channels, two deep neural network (DNN) architectures are designed to realize this framework. The first DNN is devised at the ISAC base station for estimating the sensing channel, while the second DNN architecture is assigned to each downlink user equipment to estimate its communication channel. Moreover, the input-output pairs to train the DNNs are carefully designed. Simulation results show the superiority of the proposed estimation approach compared to the benchmark scheme under various signal-to-noise ratio conditions and system parameters.

This paper studies the sum-rate maximization problem of a distributed reconfigurable intelligent surface (RIS)-assisted full-duplex wireless system, where the availability of finite-resolution phase shifts at the RIS is considered. The aim is to optimize the transmit beamformers and RIS phase shifts, subject to the practical discrete phase shift and power constraints. The optimization problem is decoupled into two sub-problems; transmit beamforming and RIS phase shifts optimization. The transmit beamforming problem is mathematically addressed using approximate and closed-form solutions, while the discrete RIS phase shifts are optimized using a reinforcement learning (RL) approach. The existence and absence of a strong direct line-of-sight is investigated to show the effect of the phase shift optimization on the sum-rate. Simulation results illustrate that the proposed RL for the discrete phase shifts optimization provides a near-optimal performance with a small number of bits even for a large number of RIS elements, while improving the sum-rate compared to the random phase shift scenario and reducing the computational complexity compared to the state-of-the-art works.

We address the problem of minimising latency with computation offloading in digital twin wireless edge networks in industrial Internet-of-Things environment via ultra-reliable and low latency communications links. The minimised latency is obtained by jointly optimising both communication and computation variables, namely transmit power, user association of IoT devices, offloading portions, the processing rate of users and edge servers. To deal with this challenging problem, we propose an iterative algorithm based on alternating optimisation approach combined with inner convex approximation framework. Simulation results demonstrate the proposed algorithm’s effectiveness in reducing the latency compared with other benchmark schemes.

We have aspired as a green and intelligent future, where humans, the built environment, and the nature are interconnected as a cyber-physical system. To such an Internet of Things, the sustainability and robustness of the power system is crucial, and the reliable operation of the battery-backed energy storage systems is key because of their abilities in power smoothing and shifting. However, detecting battery failures at the early-deployment stage is challenging due to the unavailability of anomalous measurement data and privacy concerns. In this paper, we propose an anomaly detection scheme for the energy storage systems without using prior information. We train autoencoders on the normal measurement data. Instead of training autoencoders in a centralized way, we train a global autoencoder over many energy storage systems in a federated manner without compromising privacy. Experimental results show that the proposed algorithm effectively detects anomalous batteries instantly after the system is set up without sharing sensitive data.

Fault mitigation in Connected Autonomous Vehicle (CAV) platoons is faster and more reliable if the fault structure is known. In this paper we propose using transmissibility operators, which are relationships that relate a set of velocities with another in the platoon, to classify the faults. Transmissibility operators were shown to be exceptional in signals estimation; however, its also shown to be noncausal and thus can only be used offline. To this end, we propose using Data Aggregation (DAgger), which is an extension in imitation learning to transfer the classification experience from transmissibility operators to a novice machine learning agent to be used online. A heterogeneous CAV platoon was modeled with three different faults separately. These faults are actuator disturbances, false data injection attacks, and communication time delay. The proposed algorithm is then tested on the platoon model and then applied to an experimental setup that consists of three autonomous robots. The overall classification accuracy achieved was 95.8% for the experiment.

We investigate the optimal resource allocation of mobile edge computing (MEC) with multiple Internet-of-Thing (IoT) devices in digital twin applications. Based on Markov decision process and model-free deep reinforcement learning (DRL) approach, we propose double deep RL-based online computation offloading method to implement the deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. In particular, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading task. The proposed real-time Lyapunov optimization and DRL algorithms achieve a low computational complexity and optimal processing time. Simulation results demonstrate that the proposed method can achieve near-optimal control performance with an enhanced energy consumption and significantly minimize the computation time.

Federated learning (FL) is a promising paradigm for future sixth-generation wireless systems to underpin network edge intelligence for smart cities applications. However, most of the data collected by the Internet of Things devices in such applications is unlabeled, necessitating the use of semi-supervised learning. Existing studies have introduced solutions to run semi-supervised FL; however, they overlooked the inherent critical impacts of the wireless characteristics at the network edge. We fill this gap by proposing novel solutions to run semi-supervised FL over wireless network edge, considering the limited computation and communication resources and deadline constraints and realizing that unlabeled data can be automatically labeled during the training rounds to improve the performance of the global model. The problem is first formulated as an optimization problem followed by a two-phase solution. In the first phase, we propose a bisection-based algorithm to find the transmit power and local processing speed that optimally fit the new injected labeled data. In the second phase, we propose three algorithms to control the local updates and injected samples that meet the deadline constraint. We analyze the performance of each algorithm concerning the tradeoffs between learning performance, training time, and total energy consumption. Targeting two applications in smart cities, human activity recognition and object detection, we conduct extensive simulations using realistic federated data sets under nonindependent and identically distributed settings. Numerical results show that the proposed algorithms effectively utilize unlabeled samples while accounting for the characteristics of wireless edge networks in smart cities.

The sixth-generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) toward a future of fully intelligent and autonomous systems. In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT. We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space–air–ground–underwater communications, Terahertz communications, massive ultrareliable and low-latency communications, and blockchain. Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely, healthcare IoTs, Vehicular IoTs and Autonomous Driving, Unmanned Aerial Vehicles, Satellite IoTs, and Industrial IoTs. Finally, we highlight interesting research challenges and point out potential directions to spur further research in this promising area.

With the rapid development of electronic and information technologies, the Internet of Everything (IoE) has become one of the trendiest topics in both academia and industry. Therein, many types of space/air/ground platforms need to be connected to networks for breaking down the isolation of information islands and providing various services. Space/air/ground platforms, such as satellites, unmanned aerial vehicles (UAVs), airships, balloons, terrestrial vehicles, and high-speed trains (HSTs) have emerged for accomplishing various complex tasks. Wireless communication is one of the most important technologies to support the real-time delivery of control commands and mission-related data. On the other hand, the space-air-ground integrated network has become a promising paradigm for the six-generation (6G) mobile communication network, where the aerospace and terrestrial vehicles may need to connect to existing mobile cellular networks or act as base stations (BSs) or relays to assist terrestrial wireless communications. To meet the ever-increasing demands of high capacity, wide coverage, low latency, and strong robustness for communications, it is promising to adopt large-scale antenna arrays at the transceivers to obtain considerable array gains and improve the channel quality. Antenna array-enabled beamforming technologies can facilitate spectrum reuse, interference mitigation, coverage enhancement, and physical-layer security. Antenna arrays can also be used to promote the sensing capability of space/air/ground networks, where the sensing information may be carefully processed to assist communications. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive, and tricky challenges in antenna array design, physical layer, multiple access control layer, and network layer. As a result, numerous new research issues require to be addressed, which cover a wide range of disciplines including communication theory, network theory, antenna theory, signal processing, protocol design, resource allocation, optimization, hardware implementation, and experimentation.

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/air/ground communications and networking.

Reconfigurable intelligent surfaces (RISs) and nonorthogonal multiple access (NOMA) have been recognized as key enabling techniques for the envisioned sixth generation (6G) of mobile communication networks. The key feature of RISs is to intelligently reconfigure the wireless propagation environment, which was once considered to be fixed and untunable. The key idea of NOMA is to utilize users’ dynamic channel conditions to improve spectral efficiency and user fairness. Naturally, the two communication techniques are complementary to each other and can be integrated to cope with the challenging requirements envisioned for 6G mobile networks. This survey provides a comprehensive overview of the recent progress on the synergistic integration of RISs and NOMA. In particular, the basics of both techniques are introduced first, and then, the fundamentals of RIS-NOMA are discussed for two communication scenarios with different transceiver capabilities. Resource allocation is of paramount importance for the success of RIS-assisted NOMA networks, and various approaches, including artificial intelligence (AI)-empowered designs, are introduced. Security provisioning in RIS-NOMA networks is also discussed as wireless networks are prone to security attacks due to the nature of the shared wireless medium. Finally, the survey is concluded with detailed discussions of the challenges arising in the practical implementation of RIS-NOMA, future research directions, and emerging applications.

Non-coherent (NC) schemes combined with massive antenna arrays are proposed to replace traditional coherent schemes in scenarios which require an excessive number of reference signals, since NC approaches avoid channel estimation and equalization. Differential$M$-ary phase shift keying is one of the most appealing NC schemes due to its implementation simplicity in realistic scenarios. However, the analytical constellation design for multiuser scenarios is intractable, as discussed in this paper. We propose to solve this problem by using optimization techniques relying on evolutionary computation. We design two approaches, namely Gaussian-approximated optimization and Monte-Carlo based optimization. They can provide both individual constellations for each user equipment and a bit mapping policy to minimize the bit error rate. We perform a complexity analysis and propose strategies for its reduction. We propose a set of constellations for different number of users and constellation sizes, and evaluate the link-level performance of some illustrative examples to verify that our solutions outperforms the existing ones. Finally, we show via simulations that NC outperforms the coherent schemes in high mobility and/or low signal-to-noise ratio scenarios.

This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes.

In this paper, we investigate the physical layer security in the reconfigurable intelligent surface (RIS)-aided cell-free networks. A maximum weighted sum secrecy rate problem is formulated by jointly optimizing the active beamforming (BF) at the base stations and passive BF at the RISs. To handle this non-trivial problem, we adopt the alternating optimization to decouple the original problem into two sub-ones, which are solved using the semidefinite relaxation and continuous convex approximation theory. To decrease the complexity for obtaining overall channel state information (CSI), we extend the proposed framework to the case that only requires part of the RIS’ CSI. This is achieved via deliberately discarding the RIS that has a small contribution to the user’s secrecy rate. Based on this, we formulate a mixed integer non-linear programming problem, and the linear conic relaxation is used to obtained the solutions. Meanwhile, we also study the system performance under the imperfect CSI. Finally, the simulation results show that the proposed schemes can obtain a higher secrecy rate than the existing ones.

In this paper, we study the secrecy performance of non-orthogonal multiple access (NOMA) enabled visible light communication (VLC) broadcast channels in the presence of an active eavesdropper (Eve). The considered VLC system consists of multiple separately distributed light-emitting diodes arrays and multiple randomly located users (UEs) in an indoor room. User clustering is conducted to reduce the implementation complexity of successive interference cancellations. Two cooperative precoding strategies based on zero-forcing (ZF) and maximum ratio transmission (MRT) are designed using the effective channel of each cluster. Based on each precoding strategy, a sum secrecy rate maximization problem is developed to obtain the near-optimal power allocation (PA) to strengthen UEs’ confidential transmission and degrade Eve’s reception under minimum secrecy rate requirement, peak amplitude, non-negativity, and power constraints. To tackle the challenging non-convex problem for each precoding strategy, equivalent transformations and arithmetic-geometric mean approximation are conducted to convert the original problem into a series of geometric programming (GP) problems. Based on the reformulated problems, iterative algorithms are proposed to obtain near-optimal solutions by solving the GP problems through successive convex approximations. The convergence and complexity analysis of the proposed algorithms are studied. Simulation results show that the sum security performance of the proposed PA approach outperforms the conventional PA approaches in both ZF-based and MRT-based precoder schemes. The effectiveness of applying NOMA compared with the orthogonal multiple access-based scheme is also validated for the proposed system.

Visible light communication (VLC) has attracted a significant amount of research interest due to its ability to support high data-rates. However, the issue of inter-cell interference (ICI) caused by resource sharing and the line-of-sight (LoS) blockage problem are significant challenges that need to be considered in the design and analysis of VLC systems. This paper investigates the resource allocation problem for the downlink of an orthogonal frequency-division multiple access-based multi-cell VLC system, while considering ICI and LoS blockage. This is carried out under various system configurations employing different transmission modes. Specifically, the joint problem of subchannel allocation (SA) and power allocation (PA) to maximize the sum-rate is formulated as a combinatorial and highly non-convex optimization problem due to the binary and continuous optimization variables. To obtain an efficient solution, the original problem is first separated into the SA problem and the PA problem. Two simple, yet efficient, procedures based on the quality of the channel conditions and matching theory are proposed for the SA problem, respectively. Then, the quadratic transform approach is exploited to develop an algorithm for the PA problem. Simulation results demonstrate the effectiveness of the proposed solutions in terms of their fast convergence and overall performance.

The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the two-timescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a low-complexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations.

A novel device-to-device (D2D) aided cooperative non-orthogonal multiple access (NOMA) scheme (termed as D2D-SG-NOMA) is proposed, where two similar gain (SG) near users (NUs) with the capability of D2D communication and one far user (FU) are served within two time slots. The NOMA pair is formed with a NU and the FU. The paired NU is employed as a decode-and-forward relay to assist FU. Contrarily, the unpaired NU can receive signals simultaneously from the base station (BS) and the other NU during the second time slot. Two different scenarios (i.e., S1and S2) are investigated insightfully. In S1, the direct link between the BS and FU does not exist, whereas the direct link between the BS and FU exists in S2. The delay-tolerant capacity (DTC), outage probability, diversity order, and delay-limited capacity are investigated along with analytical formulation. The D2D-SG-NOMA achieves an increase of around 66% and 85% in DTC at 0 dB signal-to-noise (SNR) under S1and S2, respectively than the existing NOMA scheme with successive relaying (termed as SR-NOMA). Contrarily, a reduced DTC improvement (i.e., around 3% and 10% in S1and S2, respectively) is obtained at 40 dB SNR due to increased inter-symbol interference.

In physical layer security, the geographical secure region as a practical security metric has received considerable interest. This paper derives the distribution functions of signal-to-interference-plus-noise ratios at Bob and Eve with Rician fading channels, which is used to calculate the secrecy outage probability for different Eve’s locations. Then, the secure region can be determined in geographical two-dimensional plane by comparing the secrecy outage probability with a threshold. Based on this, two optimization problems of the beamforming vector at Bob used for transmitting the artificial noise are constructed to expand the secure region, with and without the knowledge of Eve’s location, respectively. Simulation results are provided to depict the secure region with different antenna numbers, and illustrate that the secure region can be effectively expanded by using the optimized beamforming vector at Bob.

Large intelligent surface (LIS) has recently emerged as a potential enabling technology for 6G networks, offering extended coverage and enhanced energy and spectral efficiency. In this work, motivated by its promising potentials, we investigate the error rate performance of LIS-assisted nonorthogonal multiple access (NOMA) networks. Specifically, we consider a downlink NOMA system, in which data transmission between a base station (BS) and L NOMA users is assisted by an LIS comprising M reflective elements (REs). First, we derive the probability density function (PDF) of the end-to-end wireless fading channels between the BS and NOMA users. Then, by leveraging the obtained results, we derive an approximate expression for the pairwise error probability (PEP) of NOMA users under the assumption of imperfect successive interference cancellation. Furthermore, accurate expressions for the PEP for M = 1 and large M values ( M > 10) are presented in closed-form. To gain further insights into the system performance, an asymptotic expression for PEP in high signal-to-noise ratio regime, asymptotic diversity order, and tight union bound on the bit error rate are provided. Finally, numerical and simulation results are presented to validate the derived mathematical results.

Terahertz (THz) communication has been regarded as one promising technology to enhance the transmission capacity of future Internet-of-Things (IoT) users due to its ultrawide bandwidth. Nonetheless, one major obstacle that prevents the actual deployment of THz lies in its inherent huge attenuation. Intelligent reflecting surface (IRS) and multiple-input-multiple-output (MIMO) represent two effective solutions for compensating the large path loss in THz systems. In this article, we consider an IRS-aided multiuser THz MIMO system with orthogonal frequency-division multiple (OFDM) access, where the sparse radio frequency chain antenna structure is adopted for reducing the power consumption. The objective is to maximize the weighted sum rate via jointly optimizing the hybrid analog/digital beamforming at the base station (BS) and reflection matrix at the IRS. Since the analog beamforming and reflection matrix need to cater all users and subcarriers, it is difficult to directly solve the formulated problem, and thus, an alternatively iterative optimization algorithm is proposed. Specifically, the analog beamforming is designed by solving a MIMO capacity maximization problem, while the digital beamforming and reflection matrix optimization are both tackled using semidefinite relaxation (SDR) technique. Considering that obtaining perfect channel state information (CSI) is a challenging task in IRS-based systems, we further explore the case with the imperfect CSI for the channels from the IRS to users. Under this setup, we propose a robust beamforming and reflection matrix design scheme for the originally formulated nonconvex optimization problem. Finally, simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

Unmanned aerial vehicle (UAV) is a key enabler for communication systems beyond the fifth generation due to its applications in almost every field, including mobile communications and vertical industries. However, there exist many challenges in 3-D UAV placement, such as resource and power allocation, trajectory optimization, and user association. This problem becomes even more complex as UAV changes its height, which in turn varies the channel conditions and reduces the coverage on account of high co-channel interference. To maximize the user coverage in uplink transmission, we propose to jointly optimize the 3-D UAV placement and path-loss compensation factor. Moreover, we also optimize the latter for various UAV deployment heights in the suburban environment. Simulation results have demonstrated that the joint optimization of the UAV height and path-loss compensation factor results in better coverage and throughput performance as compared to the baseline scheme.

This article focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Secondly, the 3D-Newtonized orthogonal matching pursuit algorithm is utilized for the extraction of the UL channel parameters, including channel gains, directions of arrival, delays, and Doppler frequencies, over the antenna-time-frequency domain. Thirdly, we carefully analyze energy dispersion and power leakage of the 3D angle-delay-Doppler channels. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides and to assure that the observation regions for different users do not overlap over the 3D cubic area, i.e., angle-delay-Doppler domain. After scheduling, different users can map their respective data to the scheduled delay-Doppler domain grids, and simultaneously send the data to base station (BS) without inter-user interference in the same OTFS block. Correspondingly, the signals at desired grids within the 3D resource space of BS are separately collected to implement the 3D channel estimation and maximal ratio combining-based data recovery over the angle-delay-Doppler domain. Then, we construct a low complexity beamforming scheme over the angle-delay-Doppler domain to achieve inter-user interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme.

Specific emitter identification (SEI) is a promising technology to discriminate the individual emitter and enhance the security of various wireless communication systems. SEI is generally based on radio frequency fingerprinting (RFF) originated from the imperfection of emitter's hardware, which is difficult to forge. SEI is generally modeled as a classification task and deep learning (DL), which exhibits powerful classification capability, has been introduced into SEI for better identification performance. In the recent years, a novel DL model, named as complex-valued neural network (CVNN), has been applied into SEI methods for directly processing complex baseband signal and improving identification performance, but it also brings high model complexity and large model size, which is not conducive to the deployment of SEI, especially in Internet-of-things (IoT) scenarios. Thus, we propose an efficient SEI method based on CVNN and network compression, and the former is for performance improvement, while the latter is to reduce model complexity and size with ensuring satisfactory identification performance. Simulation results demonstrated that our proposed CVNN-based SEI method is superior to the existing DL-based methods in both identification performance and convergence speed, and the identification accuracy of CVNN can reach up to nearly 100% at high signal-to-noise ratios (SNRs). In addition, SlimCVNN just has 10% ~ 30% model sizes of the basic CVNN, and its computing complexity has different degrees of decline at different SNRs; there is almost no performance gap between SlimCVNN and CVNN. These results demonstrated the feasibility and potential of CVNN and model compression.

This paper investigates a dual-unmanned aerial vehicle (UAV)-enabled secure communication system, in which, a UAV moves around to send confidential messages to a mobile user while another cooperative UAV transmits artificial noise signals to confuse malicious eavesdroppers. Both UAVs have energy constraints and the location information of eavesdroppers is imperfect. We consider a worst-case secrecy rate maximization problem of the mobile user over all time slots. This optimization problem is solved by jointly designing the three-dimensional (3D) trajectory of UAVs and the time allocation (recharging and service or jamming) under practical constraints including maximum UAV speed, UAV collision avoidance, UAV positioning error, and UAV energy harvesting. Specifically, we adopt a more practical UAV-ground channel model with both large-scale and small-scale fading components. Due to the non-convex feasible region constructed by the complicated constraints, directly finding the optimal solution of the original problem is intractable. To address this issue, we decouple the original optimization problem into three subproblems and develop an iterative algorithm to find its suboptimal solution by using the block coordinate descent technique. To solve each subproblem, certain advanced optimization tools, such as integer relaxation, S-procedure, and successive convex approximation techniques, are utilized. Numerical simulation results are provided to corroborate the theoretical derivations and to evaluate the performance of the proposed algorithm. Additionally, the numerical results assist to draw new insights on the 3D UAV trajectory by comparing the performance with conventional two-dimensional (2D) schemes.

Non-orthogonal multiple access (NOMA) and ambient backscatter communication have been envisioned as two promising technologies for the Internet-of-things due to their high spectral efficiency and energy efficiency. Motivated by this fact, we consider an ambient backscatter NOMA system in the presence of a malicious eavesdropper. Under the realistic assumptions of residual hardware impairments (RHIs), channel estimation errors (CEEs) and imperfect successive interference cancellation (ipSIC), we investigate the physical layer security (PLS) of the ambient backscatter NOMA systems with emphasis on reliability and security. In order to further improve the security of the considered system, an artificial noise scheme is proposed where the radio frequency (RF) source acts as a jammer that transmits interference signals to the legitimate receivers and eavesdropper. On this basis, the analytical expressions for the outage probability (OP) and the intercept probability (IP) are derived. To gain more insights, the asymptotic analysis and corresponding diversity orders for the OP in the high signal-to-noise ratio (SNR) regime are carried out, and the asymptotic behaviors of the IP in the high main-to-eavesdropper ratio (MER) region are explored as well. Finally, the correctness of the theoretical analysis is verified by the Monte Carlo simulation results. These results show that compared with the non-ideal conditions, the reliability of the considered system is high under ideal conditions, but the security is low.

As a new energy saving approach for green communications, energy harvesting (EH) could be suitable technique to facilitate massive connections for large number of devices in such networks. The spectrum shortage occurs in huge number of devices which access with small-cell and macro-cell networks. To tackle these challenges, we develop a tractable framework relying on prominent techniques such as non-orthogonal multiple access (NOMA), antenna selection and energy harvesting. In this paper, we aim at practical scenarios of small cell networks by jointly evaluating capable of interference management and EH. We benefit from transmission approaches including full duplex (FD) and bi-directional transmission to improve the main performance system metrics such as outage probability and throughput. Three useful schemes are explored by considering EH and inter-cell interference. We derive the closed-form and asymptotic expressions for system metrics. We then perform extensive simulations with different system configurations to confirm the effectiveness of the proposed small-cell NOMA systems.

Reconfigurable intelligent surface (RIS) is a revolutionary technology for achieving high rate and large coverage in future wireless networks by smartly reflecting the signals with adjustable phase shifts. To design the reflection beamforming, accurate individual channel state information is required at the RIS, which is a challenge task due to the lack of signal processing ability in passive mode. In this paper, we add signal processing units for a few antennas at the RIS to partially acquire the channels and extrapolate them to the full channels, in which the active antenna selection is a key point but has not been addressed yet. We construct an active antenna selection network that utilizes the probabilistic sampling theory to select the optimal locations of these active antennas. With this active antenna selection network, we further design two deep learning-based schemes, i.e., the channel extrapolation scheme and the beam searching scheme. The former utilizes the selection network and a convolutional neural network to extrapolate the full channels from the partial channels, while the latter adopts a fully-connected neural network to achieve the direct mapping from the partial channels to the optimal beamforming vector with maximal transmission rate. Simulation results show that the proposed optimal antenna selection outperforms the trivial uniform antenna selection, and the performance of beam searching is more stable than that of channel extrapolation with fewer active antennas.

In this paper, we study the automatic modulation classification in a non-orthogonal multiple access system. To mitigate the effect of interference, a likelihood-based algorithm and a fourth-order cumulant-based algorithm are proposed. Different from the maximum likelihood classifier for a single signal without interference, a likelihood function of the far and near users' signals is derived. Then, a marginal probability for the far user is obtained by using the Bayesian formula. Hence, the modulation type can be determined by maximizing the marginal probability. The high computational complexity of the likelihood-based algorithm renders it impractical; accordingly, it serves as a theoretical performance bound. On the other hand, we construct a feature vector through the estimated fourth-order cumulants of the received signal including the superposed signal and noise. For each modulation pair, using the mean and covariance matrix of the estimated feature vector, its probability density function can be obtained. Then, the key is to calculate the mean and covariance matrix of the estimated feature vector. To solve this problem, the moments of the superposed signal are derived. Therefore, modulation classification can be performed by maximizing the probability density function. Extensive simulations verify that the two proposed algorithms perform well under a wide range of signal-to-noise ratios and observation lengths.

This paper investigates the joint problem of access point (AP) assignment and power allocation (PA) in a three-tier hybrid radio frequency/visible light communication (VLC) heterogeneous network (HetNet). The main goal is to maximize the HetNet’s sum-rate under practical constraints such as APs’ power budgets and users’ quality-of-service (QoS) requirements, while maintaining an acceptable level of illumination in the VLC system. When this design problem is formulated mathematically, it turns out to be a combinatorial decision problem that involves non-linear constraints, and hence is NP hard. To efficiently obtain good quality solutions for the formulated problem, a reformulation into thecollege admission modelis first performed. Then, a distributed and low-complexity algorithm based on matching theory and an efficient heuristic PA scheme are proposed to obtain a good quality suboptimal solution for the joint problem. Simulation results highlight the robustness of the proposed solution and its significant gain in network sum-rate as compared to different benchmark schemes. The effect of various system parameters such as the minimum QoS and maximum illumination requirements on the performance of the proposed solution is studied. Finally, the theoretical analysis of convergence, stability, and complexity of the proposed technique is performed.

This paper investigates the joint problem of access point (AP) assignment and power allocation in a three-tier hybrid radio frequency/visible light communication (VLC) heterogeneous network (HetNet). The target is to maximize the HetNet's sum-rate under practical constraints such as APs' power budgets and users' quality-of-service constraints, while maintaining an acceptable level of illumination in the VLC system. This is an NP-hard problem due to the binary and continuous optimization variables. To obtain an efficient solution, the original problem is first reformulated into the well-known college admissions model. Then, a distributed and low-complexity algorithm based on matching theory and convex optimization is proposed to solve the joint problem. Simulation results highlight the robustness of the proposed algorithm and its significant gain in network sum-rate as compared to the considered benchmarks. Finally, the theoretical analysis on convergence, stability, and complexity of the proposed algorithm is provided.

This paper focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides. Correspondingly, the signals at desired grids within the 3D resource space of the base station are separately collected to implement the 3D channel estimation and maximal ratio combining-based data detection. Then, we construct a low-complexity beamforming scheme over the angle-delay-Domain domain to achieve interuser interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme.

In this paper, we apply the long short-term memory (LSTM), an advanced recurrent neural network based machine learning (ML) technique, to the problem of transmitter selection (TS) for secrecy in an underlay small-cell cognitive radio network with unreliable backhaul connections. The cognitive communication scenario under consideration has a secondary small-cell network that shares the same spectrum of the primary network with an agreement to always maintain a desired outage probability constraint in the primary network. Due to the interference from the secondary transmitter common to all primary transmissions, the secrecy rates for the different transmitters are correlated. LSTM exploits this correlation and matches the performance of the conventional technique when the number of transmitters is small. As the number grows, the performance degrades in the same manner as other ML techniques such as support vector machine, k-nearest neighbors, naive Bayes, and deep neural network. However, LSTM still significantly outperforms these techniques in misclassification ratio and secrecy outage probability. It also reduces the feedback overhead against conventional TS.

Energy efficiency (EE) and spectral efficiency (SE) have been identified as key performance indicators in the design of future cellular networks. However, the available radio frequency (RF) spectrum is becoming highly saturated, thus making it difficult for network operators to achieve significant throughput and SE enhancement without increasing their power consumption. To that end, exploiting the abundant unlicensed spectrum in the visible light band to complement RF communication has become an important research direction in the design of wireless systems. Visible light communication (VLC) combines illumination and communication while significantly reducing the power consumption and related carbon footprint of wireless systems. This paper investigates the introduction of a VLC system in a two-tier RF heterogeneous network (HetNet). The EE and SE performance of the resulting three-tier HetNet is investigated, and a novel energy efficient resource allocation scheme is proposed. More specifically, the joint problem of user association and power control to maximize the EE is formulated as a fractional programming problem under the transmit power and quality-of-service requirements constraints. To tackle the nonconvexity of the problem, the original EE problem is first transformed into a parametric subtractive form. Then, the joint problem is separated into a user association and power control sub-problems. An efficient iterative algorithm is proposed to solve these two sub-problems, alternately. The performance of the proposed algorithm in terms of total network throughput, EE, and SE for different user densities is verified using simulation results.

This paper addresses the problem of minimizing a network-wide energy consumption in Internet of underwater things (IoUT) devices which are given a mission to survey an underwater area of interest by letting each device in the IoUT act as a sensor, an aggregator, a relay, or an inactivate device. A framework is provided, in which a role is assigned to each device in the IoUT. In this framework, we formulate an optimization problem to minimize the total energy consumption with constraints over binary role assignment decision variables. A genetic algorithm (GA) is devised to solve the formulated optimization problem. Simulation results show that the proposed framework can significantly save energy compared to a baseline approach, where there is no data aggregation. Results also illustrate that the proposed GA provides performance close to the optimal solution, which is obtained through exhaustive search.

This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob); Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GTJ method.

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

A new queue-length aware online scheduling scheme is proposed for a buffer-aided wireless-powered communication network (WPCN) with non-orthogonal multiple access (NOMA). The throughput of the considered network is maximized by designing the optimal resource allocation scheme, while preserving the stability of both energy and data queues. The formulated optimization problem is particularly challenging, since it is a long-term mixed-integer optimization problem. In order to solve it efficiently, we first transform the long-term optimization problem into a series of short-term ones at each time slot by taking advantage of the Lyapunov optimization framework, which can be efficiently solved. The analytical expression of the rate allocation reveals that in contrast to the case of WPCN without buffering, the optimal decoding order depends on the length of data buffer. Simulation results show that the proposed scheme outperforms the non-buffering scheme in terms of the long-term time-average sum rate.

This paper addresses the problem of minimizing the energy consumption of data gathering from a set of Internet-of-things (IoT) devices using an unmanned aerial vehicle (UAV). The spatial correlation among the data of the IoT devices is considered. A framework is provided, in which a subset of devices are selected to contribute, and the optimal path that the UAV should follow, along with the aggregation points at which the UAV stops and aggregates the data in an energy-efficient fashion is also considered. In this framework, an optimization problem is formulated to minimize the energy expenditure of the IoT devices and UAV while the latter tours to aggregate the required information from the former. A solution based on a greedy algorithm is provided, in which the optimization problem is decomposed into two complementary sub-problems. The first sub-problem selects the contributing devices using a genetic algorithm. The second sub-problem optimizes the locations of the data aggregation points and assigns the active devices to each aggregation point. Simulation results show that the proposed framework can save significant energy.

The study of spectral efficiency (SE) in cellular networks has attracted significant research interest in the wireless community. Heterogeneous networks (HetNets) have emerged as a new paradigm to meet the fast growing demands for higher capacity and coverage in cellular networks. Previous works have focused on investigating either the power and bandwidth allocation problem or the power allocation problem for SE maximization in HetNets. These works were carried out under the assumption that users have already been associated. This paper presents a simple and effective method to optimize the SE of two-tier HetNets. Specifically, the joint optimization of user association and power control is formulated as a mixed integer programming problem. To tackle the non-convexity of the optimization problem, the Lagrange duality theory is applied to decompose the original problem into a user association sub- problem and a power control sub-problem, which are solved alternatively. Simulation results are used to demonstrate the fast convergence rate and the noticeable performance gains of the proposed algorithm as compared with conventional user association schemes under the assumption of equal power allocation.

Spatial modulation (SM) is a promising multiple- input multiple-output system used to increase spectral efficiency. The maximum likelihood (ML) decoder jointly detects the transmitted SM symbol, which is of high complexity. In this paper, a novel reliable sphere decoder (RSD) algorithm based on tree-search is proposed for the SM system. The basic idea of the proposed RSD algorithm is to reduce the size of the tree- search, and then, a smart searching method inside the reduced tree-search is performed to find the solution. The proposed RSD algorithm provides a significant reduction in decoding complexity compared to the ML decoder and existent decoders as well. Moreover, the RSD algorithm provides a flexible tradeoff between the bit error rate (BER) performance and decoding complexity, so as to be reliable for a wide range of practical hardware implementations. The BER performance and decoding complexity analysis for the RSD algorithm are studied, and Monte Carlo simulations are then provided to demonstrate the findings.

In this paper, we study the energy efficiency (EE) performance of a three-node underwater full-duplex relay network, where the relay is an energy harvesting node. Since the arrival of harvested energy is intermittent from the ambient environment, energy-efficient data transmission can prolong the lifespan of the network. By exploiting the causal system information, we aim to maximize the long-term end-to-end EE of the network through adaptive power control at the relay node. The system is described through a Markov decision process, and the reinforcement learning framework is applied to obtain the energy-efficient transmission policy. Simulation results demonstrate the long-term average EE performance of the obtained transmission policy, which outperforms two benchmark approaches.

This paper investigates a secrecy energy efficiency (SEE) optimization problem for a multiple-input multiple-output non-orthogonal multiple access network. In particular, a multi-antenna transmitter intends to send two integrated service messages: a confidential message for the stronger user and a broadcast message for both stronger and weaker users. It is assumed that both users are equipped with multi-antennas. In this secure wireless network, we consider the transmit covariance matrices design of confidential and broadcast message, under broadcast energy efficiency (BEE) constraint. In addition, it is assumed that the weaker user might turn out to be a potential eavesdropper due to the broadcast nature of wireless transmission. We formulate this transmit covariance matrices design as an SEE maximization problem which is non-convex in its original form due the non-linear fractional objective function and constraints. To realize the solution for this problem, we utilize non-linear fractional programming and difference of concave (DC) functions approach which facilitate to reformulate it into a tractable form. Based on the Dinkelbach's algorithm and DC approximation method, we propose iterative algorithms to determine a solution to the original SEE maximization problem. Numerical results are provided to demonstrate the performance of the proposed transmit covariance matrices design to maximize the SEE.

This article presents a novel decision fusion algorithm for Internet-of-Things-based wireless sensor networks, where multiple sensors transmit their decisions about a certain phenomenon to a remote fusion center (FC) over a wide area network. The proposed algorithm denoted as the individual likelihood approximation (ILA) can significantly reduce the decision fusion error probability performance while maintaining the low computational complexity of other state-of-the-art fusion algorithms. The performance of the ILA rule is evaluated in terms of the global fusion probability of error, and an efficient analytical expression is derived in terms of a single integral. The analytical results corroborated by Monte Carlo simulation show that the ILA significantly outperforms all other considered rules, such as the Chair-Varshney (CV) and MaxLog rules. Moreover, the impact of the link from the cluster head to the FC, which is modeled as a binary symmetric channel with unknown transition probabilities, has been investigated. It is shown that the probability of error over such links should not exceed 10-3to avoid severe performance degradation. Furthermore, we derive a closed-form expression for the system fusion error probability of the CV rule for the most general system parameters.

Novel communication technology based on large intelligent surface (LIS) [1] has arisen recently, with the aim to enhance the signal quality at the receiver. In this paper, a practical structure of LIS-based spatial modulation (LIS-SM) is proposed, in order to utilize both transmit and receive antenna indices. Meanwhile, the theoretical average bit error rate (ABER) performance bound of the developed LIS-SM scheme is investigated. For the sake of achieving further spatial diversity gain, we extend its employment to the antenna selection (AS) scenario, and a low-complexity selection algorithm is designed on the basis of minimum squared Euclidian distance and signal-to-leakage-and-noise ratio as well as the idea of greedy elimination algorithm. Performance analysis shows that AS-aided LIS-SM is more robust in terms of ABER compared with conventional LIS-SM. Moreover, complexity analysis also depicts that the proposed fast selection algorithm achieves much lower complexity yet a comparable ABER performance, compared to the traditional exhaustive search.

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

Multi-user spatial modulation (SM) assisted by sparse code multiple access (SCMA) has been recently proposed to provide uplink high spectral efficiency transmission. The message passing algorithm (MPA) is employed to detect the transmitted signals, which suffers from high complexity. This paper proposes three low-complexity algorithms for the first time to the SM-SCMA. The first algorithm is referred to as successive user detection (SUD), while the second algorithm is the modified version of SUD, namely modified SUD (MSUD). Then, for the first time, the tree-search of the SM-SCMA is constructed. Based on that tree-search, another variant of the sphere decoder (SD) is proposed for the SM-SCMA, referred to as fixed-complexity SD (FCSD). SUD provides a benchmark for decoding complexity at the expense of bit-error-rate (BER) performance. Further, MSUD slightly increases the complexity of SUD with a significant improvement in BER performance. Finally, FCSD provides a near-optimum BER with a considerable reduction of the complexity compared to the MPA decoder and also supports parallel hardware implementation. The proposed algorithms provide flexible design choices for practical implementation based on system design demands. The complexity analysis and Monte-Carlo simulations of the BER are provided for the proposed algorithms.

The application of non-orthogonal multiple access (NOMA) to downlink multi-user multiple-input single-output systems involves the design of a beamforming strategy in which the spatial dimension provided by each beam is shared among several users performing NOMA. This approach requires the management of both inter-cluster and intra-cluster interference. Moreover, the beamforming design is subject to instantaneous knowledge of the channel state information at the transmitter (CSIT). We propose a novel transmission scheme that combines blind interference alignment and NOMA. The proposed scheme fully cancels the inter-cluster interference for all users without the need for instantaneous CSIT, which is limited to the knowledge of the large scale effects of the channel in order to implement NOMA within each cluster. Considering user pairing, i.e., each cluster is composed of two users, we derive a method for determining the NOMA power coefficients that maximize the sum-rate, the user fairness or satisfy first the rate of a specific user by simply solving a polynomial function. Furthermore, we propose an alternative methodology based on some approximations in order to provide sub-optimal closed-form expressions of these NOMA power coefficients. Simulation results show that the proposed scheme outperforms conventional MISO-NOMA taking into consideration the costs of providing CSIT.

This article proposes an optimization framework for power and time resource allocation during time sharing non-orthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial vehicle (UAV) in the context of a large-scale scenario. The objective of the proposed UAV-TS-NOMA system and optimization framework is to jointly maximize the energy efficiency (EE) and the downlink throughput fairness among users within the UAV communication range. The idea behind is to propose a communication system that: i) merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm and ii) maximizes the EE and the downlink fairness among users. The resulting model finds applicability in performing energy efficient and throughput fair transmissions into power-constrained communication scenarios. Performance investigations regarding the proposed framework in finding the optimal set of resources which maximizes jointly the above mentioned network metrics, have shown the advantage of the proposed two-step optimization framework in finding the optimal configuration of both power and time resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. In addition, it is shown how under particular conditions the proposed framework jointly optimizes the aforementioned network metrics in only one step.

The application of non-orthogonal multiple access (NOMA) in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems can enhance spectral efficiency. In this paper, we propose an angle-domain NOMA scheme over the multi-cell mmWave massive MIMO networks. This scheme is optimized through both user scheduling and precoders/decoders design to maximize the system sum rate, where the precoders are decomposed into outer and inner ones. We construct the outer precoders with the help of the users' spatial angle information, i.e., beam signatures, and propose two design strategies for both inner precoders and decoders, i.e., joint optimization of precoders/decoders (JOPD) and cooperative NOMA (C-NOMA). Specifically, in JOPD, the precoders/decoders are obtained through maximizing a nonconvex function subject to the users' quality-of-service (QoS) constraints, where an alternate optimization algorithm based on the constrained concave-convex procedure is proposed for its solutions. In C-NOMA, we adopt interference alignment to cooperatively serve the cell-edge users and achieve simplified yet effective precoders/decoders. Furthermore, we optimize C-NOMA through power allocation. Afterwards, user scheduling algorithms are proposed for both JOPD and C-NOMA. Extensive simulations verify that the proposed schemes exhibit improved performance in terms of both sum rate and users' QoS compared to that of existing mmWave NOMA schemes.

In this paper, we consider an energy-constrained unmanned aerial vehicle (UAV)-enabled mobile relay assisted secure communication system in the presence of a legitimate source-destination pair and multiple eavesdroppers with imperfect locations. The energy-constrained UAV employs the power splitting (PS) scheme to simultaneously receive information and harvest energy from the source, and then exploits the time switching (TS) protocol to perform information relaying. Furthermore, we consider a full-duplex destination node which can simultaneously receive confidential signals from the UAV and cooperatively transmit artificial noise (AN) signals to confuse malicious eavesdroppers. To further enhance the reliability and security of this system, we formulate a worst case secrecy rate maximization problem, which jointly optimizes the position of the UAV, the AN transmit power, as well as the PS and TS ratios. The formulated problem is non-convex and generally intractable. In order to circumvent the non-convexity, we decouple the original optimization problem into three subproblems; this facilitates the design of a suboptimal iterative algorithm. In each iteration, we propose a multi-dimensional search and numerical method to handle the subproblem. Numerical simulation results are provided to demonstrate the effectiveness and superior performance of the proposed joint design versus the conventional schemes in the literature.

Energy efficiency (EE) and spectral efficiency (SE) are two of the key performance metrics in future wireless networks, covering both design and operational requirements. For previous conventional resource allocation techniques, these two performance metrics have been considered in isolation, resulting in severe performance degradation in either of these metrics. Motivated by this problem, in this paper, we propose a novel beamforming design that jointly considers the trade-off between the two performance metrics in a multiple-input single-output non-orthogonal multiple access system. In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good trade-off between the two performance metrics. However, this MOO problem is not mathematically tractable and, thus, it is difficult to determine a feasible solution due to the conflicting objectives, where both need to be simultaneously optimized. To overcome this issue, we exploit a priori articulation scheme combined with the weighted sum approach. Using this, we reformulate the original MOO problem as a conventional single objective optimization (SOO) problem. In doing so, we develop an iterative algorithm to solve this non-convex SOO problem using the sequential convex approximation technique. Simulation results are provided to demonstrate the advantages and effectiveness of the proposed approach over the available beamforming designs.

In this article, multiuser beam training based on hierarchical codebook for millimeter wave massive multi-input multi-output is investigated, where the base station (BS) simultaneously performs beam training with multiple user equipments (UEs). For the UEs, an alternative minimization method with a closed-form expression (AMCF) is proposed to design the hierarchical codebook under the constant modulus constraint. To speed up the convergence of the AMCF, an initialization method based on Zadoff-Chu sequence is proposed. For the BS, a simultaneous multiuser beam training scheme based on an adaptively designed hierarchical codebook is proposed, where the codewords in the current layer of the codebook are designed according to the beam training results of the previous layer. The codewords at the BS are designed with multiple mainlobes, each covering a spatial region for one or more UEs. Simulation results verify the effectiveness of the proposed hierarchical codebook design schemes and show that the proposed multiuser beam training scheme can approach the performance of the beam sweeping but with significantly reduced beam training overhead.

In this paper, we investigate energy harvesting capabilities of non-orthogonal multiple access (NOMA) scheme integrated with the conventional time division multiple access (TDMA) scheme, which is referred to as hybrid TDMA-NOMA system. In a such hybrid scheme, users are divided into a number of groups, with the total time allocated for transmission is shared between these groups through multiple time slots. In particular, a time slot is assigned to serve each group, whereas the users in the corresponding group are served based on power-domain NOMA technique. Furthermore, simultaneous wireless power and information transfer technique is utilized to simultaneously harvest energy and decode information at each user. Therefore, each user splits the received signal into two parts, namely, energy harvesting part and information decoding part. In particular, we jointly determine the power allocation and power splitting ratios for all users to minimize the transmit power under minimum rate and minimum energy harvesting requirements at each user. Furthermore, this joint design is a non-convex problem in nature. Hence, we employ successive interference cancellation to overcome these non- convexity issues and determine the design parameters (i.e., the power allocations and the power splitting ratios). In simulation results, we demonstrate the performance of the proposed hybrid TDMA-NOMA design and show that it outperforms the conventional TDMA scheme in terms of transmit power consumption.

In this paper, a simultaneous multiuser hierarchical beam training scheme for multiuser mmWave massive MIMO systems is proposed based on the designed adaptive hierarchical codebook. Different from the existing work sequentially performing the beam training for different users with the same hierarchical codebook, in our work the hierarchical codebook is designed in an adaptive manner, where the codewords in the current layer are designed according to the beam training results of the previous layer. In particular, multi-mainlobe codewords are designed for simultaneously beam training with all the users, where each mainlobe of the multi-mainlobe codeword covers a spatial region that one or more users are probably in. Except for the bottom layer, there are only two codewords at each layer in the designed adaptive hierarchical codebook, which only requires two times of simultaneous beam training for all the users no matter how many users the BS serves. Simulation results verify the effectiveness of our scheme and show that our scheme can approach the performance of the beam scanning but with considerable reduction in training overhead.

In this paper, we propose a novel non-orthogonal multiple access (NOMA)-assisted cooperative overlay spectrum-sharing network, in which a full- duplex (FD) secondary transmitter (ST) is selected for relaying the primary transmitter's information and transmitting its own to the primary receiver and secondary receivers, respectively, using NOMA approach. In order to encourage the ST to perform secondary cooperative transmission and maintain its operation, simultaneous wireless information and power transfer (SWIPT) technique is exploited to scavenge energy from the primary signal. To assess the performance of the proposed system, the outage probabilities for the primary and secondary networks are derived in tight closed-form approximations. Numerical results show that the combination of FD, SWIPT, and NOMA technologies greatly benefits the cooperative overlay spectrum- sharing network in terms of outage probability, when compared with that of half-duplex NOMA and conventional orthogonal multiple access networks.

This paper investigates the coexistence of non- orthogonal multiple access (NOMA) and full-duplex (FD), where the NOMA successive interference cancellation technique is applied simultaneously to both uplink (UL) and downlink (DL) transmissions in the same time-frequency resource block. Specifically, we jointly optimize the user association (UA) and power control to maximize the overall sum rate, subject to user-specific quality-of-service and total transmit power constraints. To be spectrally-efficient, we introduce the tensor model to optimize the UL users' decoding order and the DL users' clustering, which results in a mixed-integer non- convex problem. For solving this problem, we first relax the binary variables to be continuous, and then propose a low-complexity design based on the combination of the inner convex approximation framework and the penalty method. Numerical results show that the proposed algorithm significantly outperforms the conventional FD-based schemes, FD-NOMA and its half-duplex counterpart with random UA.

In this paper, we propose using artificial noise (AN) and zero-forcing (ZF) beamforming to protect massive multiple-input multiple-output (MIMO) non- orthogonal multiple access (NOMA) networks. In particular, the ergodic legitimate, eavesdropping, and secrecy rates are derived while taking imperfect channel estimation into account. From the obtained ergodic rates, the effects of ZF precoder, AN, and the number of antennas at the base station are revealed. Results show that the ZF precoder can enhance secrecy performance when compared with maximum ratio transmission precoder. Furthermore, an optimization algorithm is proposed to maximize the sum secrecy rate of the proposed network, while guaranteeing a target secrecy rate at each user equipment. Numerical results verify the correctness of the analysis and the effectiveness of the proposed algorithm.

In this paper, the secrecy performance of a massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network is studied in the presence of a multiple-antenna eavesdropper. The ergodic secrecy rates for the downlink transmission in the considered system are derived to provide important insights. Then, by using these results, a joint power allocation scheme is proposed for both uplink training and downlink data transmission phases to maximize the sum ergodic secrecy rates. Because the utility function of interest is non-concave and the involved constraints are non-convex, a new iterative algorithm is proposed, which can find at least a local optimum. The obtained results reveal that the secrecy performance of NOMA networks benefits from deploying massive MIMO techniques. They also indicate that the proposed optimization algorithm enhances the secrecy performance of the considered system.

In this paper, we propose two beamforming designs for a multiple-input single-output non-orthogonal multiple access system considering the energy efficiency (EE) fairness between users. In particular, two quantitative fairness-based designs are developed to maintain fairness between the users in terms of achieved EE: max-min energy efficiency (MMEE) and proportional fairness (PF) designs. While the MMEE-based design aims to maximize the minimum EE of the users in the system, the PF-based design aims to seek a good balance between the global energy efficiency of the system and the EE fairness between the users. Detailed simulation results indicate that our proposed designs offer many-fold EE improvements over the existing energy-efficient beamforming designs.

In this paper, we propose a beamforming design that jointly considers two conflicting performance metrics, namely the sum rate and fairness, for a multiple-input single-output non-orthogonal multiple access system. Unlike the conventional rate-aware beamforming designs, the proposed approach has the flexibility to assign different weights to the objectives (i.e., sum rate and fairness) according to the network requirements and the channel conditions. In particular, the proposed design is first formulated as a multi-objective optimization problem, and subsequently mapped to a single objective optimization (SOO) problem by exploiting the weighted sum approach combined with a prior articulation method. As the resulting SOO problem is non-convex, we use the sequential convex approximation technique, which introduces multiple slack variables, to solve the overall problem. Simulation results are provided to demonstrate the performance and the effectiveness of the proposed approach along with detailed comparisons with conventional rate-aware-based beamforming designs.

This paper considers a cellular system with multihop device-to-device (D2D) communications to extend the coverage of a cell, for user equipments (UEs) experiencing service outage. The D2D system is an inband underlay system. The aim is to satisfy the signal-to-noise-ratio (SNR) requirements on the downlink connections on every pre-allocated resource block, signal-to-interference-plus-noise-ratio (SINR) requirements for every D2D sidelink connection on each of their pre-allocated resource blocks, and a maximum allowable interference at the base station receiver on all uplink frame resource blocks. It is desired to perform joint power control and routing to minimize the expended D2D UE energy in the system while meeting these requirements. An optimization problem is formulated that turns out to be a mixed integer non-linear program. For that, we use a generalized Bender's decomposition approach, which breaks down the problem formulation into a master sub-problem, an auxiliary sub-problem and a feasibility sub-problem. In this paper, we focus on developing an efficient solution method for the relaxed version of the master sub-problem that is responsible for generating lower bounds on the optimal objective function value. A benchmark sub-optimal disjoint scheme for the same problem is also proposed, which performs routing and power control separately. Simulations are conducted to compare the performance of both schemes and results show that the joint scheme is superior when compared with the disjoint scheme in terms of the expended UE energy, the expended base station power and success in satisfying the SINR, SNR, and interference bound requirements.

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, benefits offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances, including Internet of Things (IoT). As the IoT integrates many heterogeneous networks, such as wireless sensor networks, wireless local area networks, mobile communication networks (3G/4G/LTE/5G), wireless mesh networks, and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million health care workers by 2035. Hence, it is important to develop wearable health care systems to perform self-health monitoring. In general, wearable health care systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches, and intelligent signal processing techniques play important roles for the developments of the wearable health care systems. This Special Issue aims at providing a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IoT.

In this paper, energy efficient resource allocation is considered for an uplink hybrid system, where non-orthogonal multiple access is integrated into orthogonal multiple access (OMA). To ensure the quality of service for the users, a minimum rate requirement is predefined for each user. An energy efficiency (EE) maximization problem is formulated by jointly optimizing the user clustering, channel assignment, and power allocation (PA). To address this problem, a many-to-one bipartite graph is first constructed considering the users and resource blocks (RBs) as the two sets of nodes. Based on swap matching, a joint user-RB association and PA scheme is proposed, which converges within a limited number of iterations. Moreover, for the PA under a given user-RB association, a feasibility condition is first derived. If feasible, a low-complexity algorithm is proposed, which obtains optimal EE for any successive interference cancellation (SIC) order and an arbitrary number of users. In addition, for the special case of two users per cluster, analytical solutions are provided for the two orders in which SIC can be implemented. These solutions shed light on how the power is allocated for each user to maximize the EE. Numerical results are presented, which show that the proposed joint user-RB association and PA algorithm outperforms other hybrid multiple-access-based and OMA-based schemes.

In this paper, a novel low-complexity detection algorithm for spatial modulation (SM), referred to as the minimum-distance of maximum-length (m-M) algorithm, is proposed and analyzed. The proposed m-M algorithm is a smart searching method that is applied for the SM tree-search decoders. The behavior of the m-M algorithm is studied for three different scenarios: 1) perfect channel state information at the receiver side (CSIR); 2) imperfect CSIR of a fixed channel estimation error variance; and 3) imperfect CSIR of a variable channel estimation error variance. Moreover, the complexity of the m-M algorithm is considered as a random variable, which is carefully analyzed for all scenarios, using probabilistic tools. Based on a combination of the sphere decoder (SD) and ordering concepts, the m-M algorithm guarantees to find the maximum-likelihood (ML) solution with a significant reduction in the decoding complexity compared with SM-ML and existing SM-SD algorithms; it can reduce the complexity up to 94% and 85% in the perfect CSIR and the worst scenario of imperfect CSIR, respectively, compared with the SM-ML decoder. The Monte Carlo simulation results are provided to support our findings as well as the derived analytical complexity reduction expressions.

When considering the future generation wireless networks, non-orthogonal multiple access (NOMA) represents a viable multiple access technique for improving the spectral efficiency. The basic performance of the NOMA is often enhanced using downlink beamforming and power allocation techniques. Although downlink beamforming has been previously studied with different performance criteria, such as sum-rate and max-min rate, it has not been studied in the multiuser, multiple-input single-output (MISO) case, particularly with the energy efficiency criteria. In this paper, we investigate the design of an energy efficient beamforming technique for downlink transmission in the context of a multiuser MISO-NOMA system. In particular, this beamforming design is formulated as a global energy efficiency (GEE) maximization problem with minimum user rate requirements and transmit power constraints. By using the sequential convex approximation technique and the Dinkelbach's algorithm to handle the non-convex nature of the GEE-Max problem, we propose two novel algorithms for solving the downlink beamforming problem for the MISO-NOMA system. Our evaluation of the proposed algorithms shows that they offer similar optimal designs and are effective in offering substantial energy efficiencies compared with the designs based on conventional methods.

Non-orthogonal multiple access (NOMA) has been envisioned as a promising multiple access technique for 5G and beyond wireless networks due to its significant enhancement of spectral efficiency. In this paper, we investigate a robust energy efficiency design for multi-user multiple-input single-output (MISO) NOMA systems, where the imperfect channel state information is available at the base station (BS). A clustering algorithm is applied to group the users into different clusters, and then, the NOMA technique is employed to share the available resources fairly among the users in each cluster. To remove the interference between clusters, two different types of zero-forcing (ZF) designs, namely, hybrid-ZF and full-ZF, are employed at the BS. The full-ZF scheme completely removes the interference leakage at the cost of more number of antennas, and the hybrid-ZF scheme partially mitigates the interference leakage. To solve the problem, Dinkelbach’s algorithm is employed to convert the non-linear fractional programming problem into a simple subtractive form. Finally, simulation results reveal that hybrid-ZF outperforms the full-ZF scheme with a few clusters, while full-ZF shows a better performance with higher number of clusters. Numerical results confirm that our proposed robust scheme outperforms the non-robust scheme in terms of the rate-satisfaction ratio at each user.

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes.

This paper investigates the coexistence of non-orthogonal multiple access (NOMA) and full-duplex (FD) to improve both spectral efficiency (SE) and user fairness. In such a scenario, NOMA based on the successive interference cancellation technique is simultaneously applied to both uplink (UL) and downlink (DL) transmissions in an FD system. We consider the problem of jointly optimizing user association (UA) and power control to maximize the overall SE, subject to user-specific quality-of-service and total transmit power constraints. To be spectrally-efficient, we introduce the tensor model to optimize UL users’ decoding order and DL users’ clustering, which results in a mixed-integer non-convex problem. For practically appealing applications, we first relax the binary variables and then propose two low-complexity designs. In the first design, the continuous relaxation problem is solved using the inner convex approximation framework. Next, we additionally introduce the penalty method to further accelerate the performance of the former design. For a benchmark, we develop an optimal solution based on brute-force search (BFS) over all possible cases of UAs. It is demonstrated in numerical results that the proposed algorithms outperform the conventional FD-based schemes and its half-duplex counterpart, as well as yield data rates close to those obtained by BFS-based algorithm.

To improve the system throughput, this paper proposes a network non-orthogonal multiple access (N-NOMA) technique for the uplink coordinated multi-point transmission (CoMP). In the considered scenario, multiple base stations collaborate with each other to serve a single user, referred to as the CoMP user, which is the same as for conventional CoMP. However, unlike conventional CoMP, each base station in N-NOMA opportunistically serves an extra user, referred to as the NOMA user, while serving the CoMP user at the same bandwidth. The CoMP user is typically located far from the base stations, whereas users close to the base stations are scheduled as NOMA users. Hence, the channel conditions of the two kinds of users are very distinctive, which facilitates the implementation of NOMA. Compared to the conventional orthogonal multiple access-based CoMP scheme, where multiple base stations serve a single CoMP user only, the proposed N-NOMA scheme can support larger connectivity by serving the extra NOMA users, and improve the spectral efficiency by avoiding the CoMP user solely occupying the spectrum. A stochastic geometry approach is applied to model the considered N-NOMA scenario as a Poisson cluster process, based on which insightful closed-form or quasi closed-form analytical expressions for outage probabilities and ergodic rates are obtained. Numerical results are presented to show the accuracy of the analytical results and also demonstrate the superior performance of the proposed N-NOMA scheme.

High power consumption and expensive hardware are two bottlenecks for practical massive multiple-input multiple-output (mMIMO) systems. One promising solution is to employ low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). In this paper, we consider a general multipair mMIMO relaying system with a mixed-ADC/DAC architecture, in which some antennas are connected to low-resolution ADCs/DACs, while the rest of the antennas are connected to high-resolution ADCs/DACs. Leveraging on the additive quantization noise model, both exact and approximate closed-form expressions for the achievable rate are derived. It is shown that the achievable rate can approach the unquantized one by using only 2-3 bits of resolutions. Moreover, a power scaling law is presented to reveal that the transmit power can be scaled down inversely proportional to the number of antennas at the relay. We further propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, a tradeoff between the achievable rate and power consumption for different numbers of low-resolution ADCs/DACs is investigated by deriving the energy efficiency. Our results reveal that the large antenna array can be exploited to enable the mixed-ADC/DAC architecture, which significantly reduces the power consumption and hardware cost for practical mMIMO systems.

Blind enumeration of the number of transmit antennas and blind identification of multiple-input multiple-output (MIMO) schemes are two pivotal steps in MIMO signal identification for both military and commercial applications. Conventional approaches treat them as two independent problems, namely the source number enumeration and the presence detection of space-time redundancy. In this paper, we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO schemes simultaneously. By restructuring the received signals, we derive three subspace-rank features based on the signal subspace-rank to determine the number of transmit antennas and identify space-time redundancy. Then, a Gerschgorin radii-based method and a feed-forward neural network are employed to calculate these three features, and a minimal weighted norm-1 distance metric is utilized for decision making. In particular, our approach can identify additional MIMO schemes, which most previous works have not considered, and is compatible with both single-carrier and orthogonal frequency division multiplexing (OFDM) systems. The simulation results verify the viability of our proposed approach for single-carrier and OFDM systems and demonstrate its favorable identification performance for a short observation period with acceptable complexity.

Previous approaches for blind identification of space-frequency block codes (SFBCs) do not perform well for short observation periods due to their inefficient utilization of frequency-domain redundancy. This paper proposes a hypothesis test (HT)-based algorithm and a support vector machine (SVM)-based algorithm for the SFBC signals' identification over frequency-selective fading channels to exploit two-dimensional space-frequency domain redundancy. Based on the central limit theorem, space-domain redundancy is used to construct the cross-correlation function of the estimator and frequency-domain redundancy is incorporated in the construction of the statistics. The difference between two proposed algorithms is that the HT-based algorithm constructs a chi-square statistic and employs an HT to make the decision, while the SVM-based algorithm constructs a non-central chi-square statistic with unknown mean as a strongly distinguishable statistical feature and uses SVM to make the decision. Both the algorithms do not require knowledge of the channel coefficients, modulation type, or noise power, and the SVM-based algorithm does not require timing synchronization. The simulation results verify the superior performance of the proposed algorithms for short observation periods with comparable computational complexity to conventional algorithms, as well as their acceptable identification performance in the presence of transmission impairments.

In this paper, multihop device-to-device (D2D) communications for cell coverage extension are studied. An in-band underlay D2D mode is considered, and the aim is to satisfy the signal-to-noise-ratio requirements for pre-allocated resource blocks (RBs) on the downlink connections, the signal-to-interference-plus-noise-ratio requirements on pre-allocated RBs for every D2D sidelink connection, and a maximum allowable interference at the base station receiver on all uplink RBs. Power control and routing are performed to minimize the expended user equipment energy in the system while meeting these requirements. An optimization problem is formulated that turns out to be a mixed-integer nonlinear program, which is solved using the generalized Benders decomposition (GBD). The GBD breaks down the formulation into a master sub-problem, an auxiliary sub-problem, and a feasibility sub-problem. In this paper, we focus on finding efficient solution methods for the relaxed version of the master sub-problem that is responsible for generating lower bounds on the optimal objective function. Also, an efficient solution technique for the feasibility sub-problem is proposed. Furthermore, a benchmark disjoint scheme for the same problem is proposed, which performs routing and power control separately. The simulations are conducted to compare the performance of both schemes, which show the superiority of joint routing and power control scheme.

This paper considers a full-duplex (FD) multiuser multiple-input single-output system where a base station simultaneously serves both uplink (UL) and downlink (DL) users on the same time-frequency resource. The crucial barriers in implementing FD systems reside in the residual self-interference and co-channel interference. To accelerate the use of FD radio in future wireless networks, we aim at managing the network interference more effectively by jointly designing the selection of half-array antenna modes (in the transmit or receive mode) at the base station with time phases and user assignments. The first problem of interest is to maximize the overall sum rate subject to quality-of-service requirements, which is formulated as a highly non-concave utility function followed by non-convex constraints. To address the design problem, we propose an iterative low-complexity algorithm by developing new inner approximations, and its convergence to a stationary point is guaranteed. To provide more insights into the solution of the proposed design, a general max-min rate optimization is further considered to maximize the minimum per-user rate while satisfying a given ratio between UL and DL rates. Furthermore, a robust algorithm is devised to verify that the proposed scheme works well under channel uncertainty. The simulation results demonstrate that the proposed algorithms exhibit fast convergence and substantially outperform existing schemes.

Recently, it was shown that non-orthogonal multiple access (NOMA) became a hot topic due to its capacity for ameliorating spectral efficiency. In this paper, power allocation in heterogeneous NOMA networks with multiple users are formulated as a Stackelberg game. The competition between the leaders and followers is considered as the energy efficiency (EE) maximization between the small base stations (SBSs) and macro base stations (MBSs). We propose an algorithm to obtain optimal power allocation in MBSs layer and SBSs layer, respectively. Then, Stackelberg iteration is used among MBSs and SBSs to reach the equilibrium point during the game. Simulation results demonstrate the effectiveness of proposed algorithms.

This paper considers a cellular system with multihop device-to-device (D2D) communications to extend the coverage of a cell, for user equipment (UEs) experiencing service outage. The D2D system we consider is an inband underlay system. The target is to perform power control and routing to minimize the expended UE energy while satisfying the signal-to-noise-ratio (SNR) requirements on the downlink connections on every pre-allocated resource block (RB), signal-to-interference-plus- noise-ratio (SINR) requirements for every D2D sidelink connection on every pre-allocated RB, and a maximum allowable interference at the base- station (BS) receiver on all uplink frame RBs. An optimization problem is formulated that turns out to be a mixed integer non-linear program. We propose the generalized Benders decomposition which breaks down the formulation to a master sub-problem, an auxiliary sub-problem and a feasibility sub-problem. In this paper, we focus on finding efficient solution methods for the auxiliary sub-problem that is necessary for generating under-estimators to one of two types of Benders' cuts. These cut-of infeasible power allocations in a relaxed version of the master problem, and tighten the lower bounds on the optimal objective function value. Simulations are conducted to show how multihop D2D routing improves the cell coverage, by reducing the probability of service outage. The simulation results also show the effect of the requirements of SNR, SINR and interference at the BS on the expended UE energy.

In this paper, energy efficient power allocation is considered for a multiuser uplink non-orthogonal multiple access (NOMA) system under quality of service (QoS) constraints. Due to the QoS requirements, the considered energy-efficiency (EE) maximization problem may be infeasible and it is first required to determine the feasibility conditions. Then in a feasible region, the considered problem is shown to be pseudo-concave and can be solved by employing Dinkelbach's algorithm. Moreover, for the two extreme cases with very low and high maximum transmit power constraints, analytical results are provided, which shed light on how power is allocated to each user to maximize the EE. Simulation results are presented, which show that with a low maximum transmit power constraint, NOMA may perform worse than the conventional orthogonal multiple access (OMA) in terms of EE. However, under a high maximum transmit power constraint, NOMA always outperforms OMA.

Full-dimensional (FD) channel state information at transmitter (CSIT) has always been a major limitation of the spectral efficiency of cellular multi-input multi-output (MIMO) networks. This letter proposes an FD-directional spatial channel estimation algorithm for frequency division duplex massive FD-MIMO systems. The proposed algorithm uses the statistical spatial correlation between the uplink (UL) and downlink (DL) channels of each user equipment. It spatially decomposes the UL channel into azimuthal and elevation dimensions to estimate the array principal receive responses. An FD spatial rotation matrix is constructed to estimate the corresponding transmit responses of the DL channel, in terms of the frequency band gap between the UL and DL channels. The proposed algorithm shows significantly promising performance, approaching the ideal perfect-CSIT case without UL feedback overhead.

Consider a full-duplex (FD) multiuser system where an FD base station (BS) is designed to concurrently serve both downlink and uplink users in the presence of half-duplex eavesdroppers (Eves). The target problem is to maximize the minimum secrecy rate (SR) among all legitimate users. A novel user grouping-based fractional time allocation is proposed as an alternative solution, where information signals at the FD-BS are accompanied by artificial noise to degrade the Eves' channels. The SR problem has a highly non-concave and non-smooth objective, subject to non-convex constraints due to coupling between the optimization variables. Nevertheless, we develop a path-following low- complexity algorithm, which involves only a simple convex program of moderate dimensions at each iteration. Numerical results demonstrate the merit of the proposed approach compared to existing well-known ones, i.e., conventional FD and FD non-orthogonal multiple access.

In spatial modulation (SM) and quadrature SM (QSM), the maximum-likelihood (ML) decoder provides the optimum solution with high decoding complexity at the receiver side. This paper presents a novel low-complexity algorithm for decoding the SM and QSM symbols, referred to as the min-max algorithm. This is an intelligent searching algorithm, particularly designed for the tree-search of the SM and QSM decoders. The proposed algorithm expands the minimum Euclidean distance (ED) by adding a single node at each step, without considering the order of the branches. The expanding process stops if the minimum ED occurs at the end of a fully expanded branch. It is shown that the proposed algorithm achieves the optimum ML bit error rate performance with a significant reduction in the decoding complexity comparing with SM-ML and QSM-ML, as well as other existing sphere decoding algorithms. Simulations and mathematical analysis are provided to assess the decoding performance and complexity of the proposed algorithm.

This work proposes the blind modulation classification (MC) of different variants of quadrature phase-shift-keying (QPSK), i.e.,$\pi/4$-QPSK, offset QPSK (OQPSK), and minimum-shift-keying (MSK), and 8-PSK. The problem of MC for multiple antenna systems is investigated over frequency-selective fading channels with transmission impairments, i.e., phase, timing, and frequency offsets, without having prior information about channel coefficients. The MC algorithm proposed in this paper employs the cyclostationarity and higher-order statistical properties of the received baseband signal for classification. The MC is performed in three stages: at the first stage, the second-order cyclic cumulant uses the position of the cycle frequency to classify MSK and OQPSK. At the second and third stages, the fourth-order correlation function of the signals received from different antennas exhibits peaks, which are employed as discriminating features for QPSK,$\pi/4$·QPSK, and 8-PSK. Monte Carlo simulations are used to evaluate the performance of the proposed algorithm.

The increasing adoption of the Internet of Things has led to the need for systems with higher spectral and energy efficiency (EE) in order to enable communication. Larger data rate demands had led researchers to look at millimeter wave (mmWave) bands to boost network rates. This paper investigates the downlink performance of a three-tier heterogeneous network that consists of sub-6 GHz macrocells overlaid with small cells operating on both the mmWave and sub-6 GHz bands. A model is developed using tools from stochastic geometry to analyze the coverage, rate, area spectral efficiency, and EE of such a network. Various deployment strategies and their impacts on the considered metrics are studied. Simulation results are used to verify the validity of the proposed model.

We consider a full-duplex (FD) multiuser system where an FD base station (BS) is designed to simultaneously serve both downlink (DL) and uplink (UL) users in the presence of half-duplex eavesdroppers (Eves). The problem is to maximize the minimum (max-min) secrecy rate (SR) among all legitimate users, where the information signals at the FD-BS are accompanied with artificial noise to debilitate the Eves' channels. To enhance the max-min SR, a major part of the power budget should be allocated to serve the users with poor channel qualities, such as those far from the FD-BS, undermining the SR for other users, and thus compromising the SR per-user. In addition, the main obstacle in designing an FD system is due to the self-interference (SI) and co-channel interference (CCI) among users. We therefore propose an alternative solution, where the FD-BS uses a fraction of the time block to serve near DL users and far UL users, and the remaining fractional time to serve other users. The proposed scheme mitigates the harmful effects of SI, CCI, and multiuser interference, and provides system robustness. The SR optimization problem has a highly nonconcave and nonsmooth objective, subject to nonconvex constraints. For the case of perfect channel state information (CSI), we develop a low-complexity path-following algorithm, which involves only a simple convex program of moderate dimension at each iteration. We show that our path-following algorithm guarantees convergence at least to a local optimum. Then, we extend the path-following algorithm to the cases of partially known Eves' CSI, where only statistics of CSI for the Eves are known, and worst-case scenario in which Eves can employ a more advanced linear decoder. The merit of our proposed approach is further demonstrated by extensive numerical results.

This letter studies the potential of harvesting energy from the self-interference of a full-duplex base station. The base station is equipped with a self-interference cancellation switch, which is turned off for a fraction of the transmission period in order to harvest the energy from the self-interference that arises due to the downlink transmission. For the remaining transmission period, the switch is on such that the uplink transmission takes place simultaneously with the downlink transmission. A novel energy-efficiency maximization problem is formulated for the joint design of downlink beamformers, uplink power allocations, and the transmission time-splitting factor. The optimization problem is nonconvex, and hence, a rapidly converging iterative algorithm is proposed by employing the successive convex approximation approach. Numerical simulation results show significant improvement in the energy-efficiency by allowing self-energy recycling.

Network coding is an effective idea to boost the capacity of wireless networks, and a variety of studies have explored its advantages in different scenarios. However, there is not much analytical study on throughput and end-to-end delay of network coding in multi-hop wireless networks considering the specifications of IEEE 802.11 Distributed Coordination Function. In this paper, we utilize queuing theory to propose an analytical framework for bidirectional unicast flows in multi-hop wireless mesh networks. We study the throughput and end-to-end delay of inter-flow network coding under the IEEE 802.11 standard with CSMA/CA random access and exponential back-off time considering clock freezing and virtual carrier sensing, and formulate several parameters such as the probability of successful transmission in terms of bit error rate and collision probability, waiting time of packets at nodes, and retransmission mechanism. Our model uses a multi-class queuing network with stable queues, where coded packets have a nonpreemptive higher priority over native packets, and forwarding of native packets is not delayed if no coding opportunities are available. Finally, we use computer simulations to verify the accuracy of our analytical model.

One of the main challenges in high-speed mobile communications is the presence of large Doppler spreads. Thus, accurate estimation of maximum Doppler spread (MDS) plays an important role in improving the performance of the communication link. In this paper, we derive the data-aided (DA) and non-data-aided (NDA) Cramér-Rao lower bounds (CRLBs) and maximum likelihood estimators (MLEs) for the MDS in multiple-input multiple-output (MIMO) frequency-selective fading channel. Moreover, a low-complexity NDA-moment-based estimator (MBE) is proposed. The proposed NDA-MBE relies on the second- and fourth-order moments of the received signal, which are employed to estimate the normalized squared autocorrelation function of the fading channel. Then, the problem of MDS estimation is formulated as a non-linear regression problem, and the least-squares curve-fitting optimization technique is applied to determine the estimate of the MDS. This is the first time in the literature, when DA- and NDA-MDS estimation is investigated for MIMO frequency-selective fading channel. Simulation results show that there is no significant performance gap between the derived NDA-MLE and NDA-CRLB, even when the observation window is relatively small. Furthermore, the significant reduced-complexity in the NDA-MBE leads to low root-mean-square error over a wide range of MDSs, when the observation window is selected large enough.

A heterogeneous network (HetNet) of a macrocell base station equipped with a large-scale massive multi-in multi-out (MIMO) antenna array overlaying a number of small cell base stations (small cells) can provide high quality of service (QoS) to multiple users under low transmit power budget. However, the circuit power for operating such a network, which is proportional to the number of transmit antennas, poses a problem in terms of its energy efficiency (EE). This paper addresses the beamforming design at the base stations to optimize the network EE under the QoS constraints and a transmit power budget. Beamforming tailored for weak, strong, and medium cross-tier interference HetNets is proposed. In contrast to the conventional transmit strategy for power efficiency in meeting the users' QoS requirements, which suggest the use of a few hundred antennas, it is found out that the overall network EE quickly drops if this number exceeds 50. It is found that, for a given number of antennas, HetNet is more energy efficient than massive MIMO when considering the overall energy consumption.

In this paper, we consider an active eavesdropping scenario in a cooperative system consisting of a source, a destination, and an active eavesdropper with multiple decode-and-forward relays. Considering an existing assumption in which an eavesdropper is also a part of network, a proactive relay selection by the eavesdropper is proposed. The best relay which maximizes the eavesdropping rate is selected by the eavesdropper. A relay selection scheme is also proposed to improve the secrecy of the system by minimizing the eavesdropping rate. Performances of these schemes are compared with two passive eavesdropping scenarios in which the eavesdropper performs selection and maximal ratio combining on the relayed links. A realistic channel model with independent non-identical links between nodes and direct links from the source to both the destination and eavesdropper are assumed. Closed- form expressions for the secrecy outage probability (SOP) of these schemes in Rayleigh fading channel are obtained. It is shown that the relay selection by the proactive eavesdropper is most detrimental to the system as not only the SOP increases with the increase in the number of relays, but its diversity also remains unchanged.

This paper considers a full-duplex system for a base station (BS) serving both uplink and downlink users simultaneously on the same frequency. A new design of the selection of the half-array antenna mode at the BS (transmit or receive mode) over the time phases and user assignments is proposed and its beamforming design and power allocation problem are optimized under the effect of both residual self- interference and co-channel interference. The aim is to maximize the overall sum rate subject to the users' quality of service requirements, which is formulated as a highly nonlinear function subject to non-convex constraints. To solve this non-convex problem, we propose an iterative low- complexity algorithm. Simulation results demonstrate that the proposed algorithm provides a fast convergence and substantially outperforms all existing schemes.

In this paper, we consider the power allocation (PA) problem in cognitive radio networks (CRNs) employing nonorthogonal multiple access (NOMA) technique. Specifically, we aim to maximize the number of admitted secondary users (SUs) and their throughput, without violating the interference tolerance threshold of the primary users (PUs). This problem is divided into a two-phase PA process: a) maximizing the number of admitted SUs; b) maximizing the minimum throughput among the admitted SUs. To address the first phase, we apply a sequential and iterative PA algorithm, which fully exploits the characteristics of the NOMA-based system. Following this, the second phase is shown to be quasiconvex and is optimally solved via the bisection method. Furthermore, we prove the existence of a unique solution for the second phase and propose another PA algorithm, which is also optimal and significantly reduces the complexity in contrast with the bisection method. Simulation results verify the effectiveness of the proposed two-phase PA scheme.

Channel state information (CSI) acquisition is a crucial issue in downlink FDD-based massive multi-input multioutput (MIMO) networks, where the channel reciprocity is not applicable. Thus, users are expected to feedback the bestmatch quantized channels to serving transmitters. Hence, an extensively large size of the feedback overhead is needed, which is linearly scaled at each user with the number of transmit antennas at the base-station (BS). In turn, the uplink (UL) channel capacity may be consumed and the overall performance becomes fundamentally limited by the downlink (DL) channel quantization precision. An alternative CSI acquisition scheme is critically needed. In this paper, we propose a novel FDD massive MIMO system based on a spatial DL channel estimation scheme; it relies on the statistical spatial correlation of the UL and DL channel clusters, given an arbitrary frequency band gap between the UL and DL channels. A transformation matrix is constructed to precode the observed UL channel on the estimated dominant DL angles of departure. The proposed scheme significantly outperforms the recent state-of-the-art techniques, without the cost of user feedback overhead bits and prior knowledge of the channel statistics.

On behalf of the Technical Program Committee, we welcome all of you to the 13thIEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC 2017) in the beautiful Valencia, Spain! We are indeed delighted that this year's IEEE IWCMC accomplishes its goal under the conference theme “Plethora Communications” and continues its tradition of providing a premier forum for presentation of research results and experience reporting on the cutting edge research in the general areas of wireless communications and mobile computing. This year, we received more than 900 submissions from 32 countries. Each paper received at least three peer technical reviews, comprised of more than 500 TPC members from academia, government laboratories, and industries. After carefully examining all review reports, the IEEE IWCMC 2017 TPC finally selected about 36% high-quality papers for presentation at the conference and publication in the IEEE IWCMC 2017 proceedings.

We consider a multi-carrier and densely deployed small cell network, where small cells are powered by renewable energy source and operate in a full-duplex mode. We formulate an energy and traffic aware resource allocation optimization problem, where a joint design of the beamformers, power and sub-carrier allocation, and users scheduling is proposed. The problem minimizes the sum data buffer lengths of each user in the network by using the harvested energy. A practical uplink user rate-dependent decoding energy consumption is included in the total energy consumption at the small cell base stations. Hence, harvested energy is shared with both downlink and uplink users. Owing to the non-convexity of the problem, a faster convergence sub-optimal algorithm based on successive parametric convex approximation framework is proposed. The algorithm is implemented in a distributed fashion, by using the alternating direction method of multipliers, which offers not only the limited information exchange between the base stations, but also fast convergence. Numerical results advocate the redesigning of the resource allocation strategy when the energy at the base station is shared among the downlink and uplink transmissions.

In this paper, an incremental selective decode-and-forward (ISDF) relay strategy is proposed for power line communication (PLC) systems to improve the spectral efficiency. Traditional decode-and-forward (DF) relaying employs two time slots by using half-duplex relays which significantly reduces the spectral efficiency. The ISDF strategy utilizes the relay only if the direct link quality fails to attain a certain information rate, thereby improving the spectral efficiency. The path gain is assumed to be log-normally distributed with very high distance dependent signal attenuation. Furthermore, the additive noise is modeled as a Bernoulli-Gaussian process to incorporate the effects of impulsive noise contents. Closed-form expressions for the outage probability and the fraction of times the relay is in use, and an approximate closed-form expression for the average bit error rate (BER) are derived for the binary phase-shift keying signaling scheme. We observe that the fraction of times the relay is in use can be significantly reduced compared to the raditional DF strategy. It is also observed that at high transmit power, the spectral efficiency increases while the average BER decreases with increase in the required rate.

In this paper, we investigate the impacts of unreliable backhaul connections on cognitive heterogeneous networks with best relay selection. Since spectrum sharing is employed, the transmit powers of the small-cell transmitter and relays are constrained by the peak interference at the primary user, as well as their maximum transmit powers. The closed-form expressions of the outage probability, ergodic capacity and symbol error rate are derived along with the asymptotic performance to get full insights. Our results show that the backhaul reliability is a limiting factor of the system performance.

In this paper, spatial modulation (SM) is used in a cooperative decode-and-forward (DF) cognitive radio system in order to enhance the overall spectral efficiency. In particular, a multi- antenna secondary transmitter communicates with a single antenna secondary receiver with the help of DF secondary relays in the presence of multiple primary users (PUs). To study the secondary system performance, we derive a closed-form expression for the average pairwise error probability (PEP) over Rayleigh fading channels assuming limited feedback from the PUs. A tight upper bounded average bit error rate is obtained using the PEP expression. Moreover, simple approximate expressions are obtained to get insights on the system diversity and estimation errors effects. Numerical results, which match simulations, show the effectiveness of SM in improving the overall secondary performance in the presence of channel estimation errors.

In this paper, the performance of multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is investigated, when multiple users are grouped into a cluster. The superiority of MIMO-NOMA over MIMO-OMA in terms of both sum channel capacity and ergodic sum capacity is proved analytically. Furthermore, it is demonstrated that the more users are admitted to a cluster, the lower is the achieved sum rate, which illustrates the tradeoff between the sum rate and maximum number of admitted users. On this basis, a user admission scheme is proposed, which is optimal in terms of both sum rate and the number of admitted users when the signal-to-interference-plus-noise ratio thresholds of the users are equal. When these thresholds are different, the proposed scheme still achieves good performance in balancing both criteria. Moreover, under certain conditions, it maximizes the number of admitted users. In addition, the complexity of the proposed scheme is linear in the number of users per cluster. Simulation results verify the superiority of MIMO-NOMA over MIMO-OMA in terms of both sum rate and user fairness, as well as the effectiveness of the proposed user admission scheme.

With the proliferation of Internet-of-Things (IoT), the chirp spread spectrum (CSS) technique is re-emerging for communications. Although CSS can offer high processing gain, its poor spectral efficiency and the lack of orthogonality among different chirps tend to limit its potential. In this paper, we derive the condition to orthogonally multiplex an arbitrary number of linear chirps. For the first time in the literature, we show that the maximum modulation rate of the linear continuous-time chirps satisfying the orthogonality condition can approach the Nyquist signaling rate, the same as single-carrier waveforms with Nyquist signaling or orthogonal frequency-division multiplexing signals. The performance of the proposed orthogonal CSS is analyzed in comparison to the emerging LoRa systems for IoT applications with power constraint, and its capability for high-speed communications is also demonstrated in the sense of Nyquist signaling.

In this paper, we propose three automatic modulation classification classifiers based on order-statistics and reduced order-statistics, where the order-statistics are the random variables sorted by ascending order and the reduced order-statistics represent a subset of the original order-statistics. Specifically, the linear support vector machine classifier applies the linear combination of the order-statistics of the received signals, while the approximate maximum likelihood and the backpropagation neural networks (BPNNs) classifier resort to the reduced order-statistics to decrease the computational complexity. Moreover, BPNN is applicable for modulation classification both in known and unknown channel scenarios. It is shown that in the known channel scenario, the proposed classifiers provide a good tradeoff between performance and computational complexity, while in the unknown channel scenario, the proposed BPNN classifier outperforms the expectation maximization classifier in terms of both classification performance and computational complexity. Simulations results are provided to evaluate the proposed classifiers.

Signal identification has emerged as an enabling technology for intelligent wireless communication systems with applications in military and commercial fields. One of recent trends in this research topic is to propose identification algorithms for multiple antenna (MA) systems with multi-carrier (MC) transmissions. The previously reported investigations are limited to space-time block code (STBC) systems with MC transmissions. However, practical systems include also space-frequency block code (SFBC) schemes with MC transmissions. In this paper, we develop and analyze an SFBC identification algorithm for MA orthogonal frequency-division multiplexing (OFDM) transmission for the first time in the literature. Analytical expressions for the time-domain properties of the Alamouti and spatial multiplexing SFBC-OFDM signals are derived as the basis of the identification process. The proposed algorithm is divided into two steps. The first step estimates the cross-correlation function of pairs of signals received from different antennas, while the second step employs a false-alarm based test for decision making. The proposed algorithm avoids the need for a priori knowledge of the modulation format, channel coefficients, signal-to-noise ratio (SNR) value, and the starting time of OFDM symbols. Simulation results show the ability of the proposed algorithm to provide an acceptable identification performance in the presence of transmission impairments, even at relatively low SNR values. These favorable results are achieved with acceptable computational cost.

In this paper, we propose a novel joint routing and medium access control (MAC) protocol with traffic differentiation, based on quality of service (QoS) for wireless sensor networks (WSNs). This is referred to as joint routing and MAC (JRM) protocol. By leveraging the classical layered approach and combining routing and MAC layer functions, the proposed JRM protocol achieves a solution for energy efficiency in WSNs. JRM also ensures low latency for prioritized traffic. There are three major advantages of the proposed protocol. Firstly, the instantaneous network information (e.g., estimated time to destination, and node's unavailability to forward additional packet) is piggy-backed with the control packets acknowledgement and clear- to-send of the MAC frame. Based on the updated network knowledge and the objective of the required performance metrics (e.g., energy and latency), the next hop neighbor is chosen dynamically with reduced control overhead. Secondly, the JRM protocol introduces an approach for finding the constrained shortest path for forwarding packets, which results in load balancing in WSNs. Finally, routers (nodes) in JRM require very little forwarding and routing table information, which is compatible with the resource constraints in WSNs. The efficiency of the proposed protocol is shown through simulation results.

In recent years, a significant amount of research has been conducted to explore the benefits of network coding in different scenarios, from both theoretical and simulation perspectives. In this paper, we utilize queueing theory to propose an analytical framework for bidirectional unicast flows in multi-hop wireless mesh networks, and study throughput of inter-flow network coding. We analytically determine performance metrics such as the probability of successful transmission in terms of collision probability, and feedback mechanism and retransmission. Regarding the coding process, our model uses a multi-class queueing network where coded packets are separated from native packets and have a non-preemptive higher priority over native packets, and both queues are in a stable state. Finally, we use simulations to verify the accuracy of our analytical model.

In this paper, relay selection is considered to enhance security of a cooperative system with multiple threshold-selection decode-and-forward (DF) relays. Threshold-selection DF relays are the relays in which a predefined signal-to-noise ratio is set for the condition of successful decoding of the source message. We focus on the practical and general scenario where the channels suffer from independent non-identical Rayleigh fading and where the direct links between the source and destination and source and eavesdropper are available. Based on channel state information knowledge, three relay selection strategies, namely traditional, improved traditional, and optimal, are studied. In particular, the secrecy outage probability of all three strategies are obtained in closed-form. It is found that the diversity of secrecy outage probability of all strategies can improve with increasing the number of relays. It is also observed that the secrecy outage probability is limited by either the source to relay or relay to destination channel quality.

Two new non-data-aided (NDA) signal-to-noise ratio (SNR) estimators for multiple antenna systems are presented. The proposed estimators rely on higher-order moments of the received signal and employ the time-diversity of the fading channel. While one requires a priori knowledge about the number of transmit antennas, the other estimates the SNR without such knowledge. The performance of the proposed estimators is evaluated for different numbers of transmit antennas and over a wide SNR range. Experimental results show that the proposed estimators exhibit a good performance, over a wide range of SNRs, in terms of normalized-root-mean-square-error (NRMSE) with small bias.

In this paper, the power allocation (PA) problem is investigated in cognitive radio networks (CRNs), employing non-orthogonal multiple access (NOMA) technique. In such a framework, the quality of service (QoS) requirements for both the primary users (PUs) and secondary users (SUs) should be met. We propose a novel PA algorithm, which fully exploits the characteristics of NOMA-based system. The QoS requirements for PUs are guaranteed through the setup of the overall power for SUs. In addition, it provides PA in a descending order, according to SUs' channel gains. It is validated that the proposed algorithm is optimal. To show its effectiveness, it is compared with one of the most efficient PA algorithms, namely fractional transmit power control (FTPC). The superiority of the proposed algorithm is thoroughly verified by simulation results. Additionally, the computational complexity of our proposed algorithm is only linear, i.e., O(N).

In this paper, we consider beamforming design for an underlay cognitive radio multiple-input single-output broadcast channel, where a pair of secondary users coexists with multiple primary receivers. There exist multiple malicious eavesdroppers who attempt to overhear the confidential messages from the secondary system. When the channel state information of the eavesdroppers can only be obtained in the statistical sense, we transform the constraint which results from the statistical information of the passive eavesdroppers into a linear matrix inequality and convex constraint. To improve the secrecy rate of the secondary system, we aim to design a jamming noise to degrade the eavesdroppers. The main objective of the design is to maximize the secrecy rate of the secondary system while satisfying all the interference power constraints at the primary users and per-antenna power constraint at the secondary transmitter. The original problem is a nonconvex program, which can be reformulated to a convex program by applying the rank relaxation method. To this end, we prove that the rank relaxation is tight and it can be efficiently solved. Moreover, to develop an efficient resource allocation scheme we transform the relaxed problem into an equivalent problem based on a duality result.

In this paper, the resource allocation problem is considered in the context of spectrum underlay in cognitive radio (CR) networks. In such a framework, the quality of service (QoS) requirements for both primary users (PUs) and secondary users (SUs) need to be satisfied. Admission control based on removal algorithms is used jointly with power control such that the QoS requirements of all admitted SUs are satisfied, while no excessive interference is caused to PUs. For the first time in the literature, we introduce the min-weight load-balancing problem based on weighted bipartite graphs, while combining it with power control and user removal. Simulation results show that the combined algorithm achieves improved results when compared with merely using power control and user removal algorithms.

In this paper, the secrecy of a cooperative system consisting of a single source, relay, destination and eavesdropper is analyzed. The threshold-selection decode-and-forward relay is considered, where the relay can correctly decode and forward only if it satisfies a threshold signal-to-noise ratio (SNR). Both destination and eavesdropper take advantage of the direct and relayed transmissions through maximal ratio diversity combining. The secrecy outage probability (SOP) and ergodic secrecy rate are derived in closed-form for different channel state information (CSI) availability. It was observed that when the required rate is low, having CSI knowledge is more advantageous than in the case of higher rate. An increase in the required threshold SNR at the relay can increase the SOP if the relayed link SNR is relatively higher than the direct link SNR. It was also shown that SOP cannot be improved beyond a certain value when keeping either dual-hop link average SNR fixed and increasing the other link SNR, whereas the ergodic secrecy rate can be increased by keeping the source to destination average SNR fixed.

Modulation classification is crucial in applications such as electronic warfare and interference cancellation. In this letter, a novel feature-based Kolmogorov-Smirnov classifier is proposed for the identification of the modulation formats. The received signal is first preprocessed with a folding operation that helps identify the modulation formats based on their different axes of symmetry. Simulation results show that the performance of the proposed classifier is close to that of the optimal likelihood-based classifier, while its robustness to noise uncertainty is improved and its computational complexity is reduced compared to that of the optimal likelihood-based classifier.

In this paper, we consider the secure beamforming design for an underlay cognitive radio multiple-input single-output broadcast channel in the presence of multiple passive eavesdroppers. Our goal is to design a jamming noise (JN) transmit strategy to maximize the secrecy rate of the secondary system. By utilizing the zero-forcing method to eliminate the interference caused by JN to the secondary user, we study the joint optimization of the information and JN beamforming for secrecy rate maximization of the secondary system while satisfying all the interference power constraints at the primary users, as well as the per-antenna power constraint at the secondary transmitter. For an optimal beamforming design, the original problem is a nonconvex program, which can be reformulated as a convex program by applying the rank relaxation method. To this end, we prove that the rank relaxation is tight and propose a barrier interior-point method to solve the resulting saddle point problem based on a duality result. To find the global optimal solution, we transform the considered problem into an unconstrained optimization problem. We then employ Broyden-Fletcher-Goldfarb-Shanno method to solve the resulting unconstrained problem, which helps reduce the complexity significantly, compared with the conventional methods. Simulation results show the fast convergence of the proposed algorithm and substantial performance improvements over the existing approaches.

In this paper, we investigate the specific emitter identification (SEI) problem, which distinguishes different emitters using features generated by the nonlinearity of the power amplifiers of emitters. SEI is performed by measuring the features representing the individual specifications of emitters and making a decision based on their differences. In this paper, the SEI problem is considered in both single-hop and relaying scenarios, and three algorithms based on the Hilbert spectrum are proposed. The first employs the entropy and the first- and second-order moments as identification features, which describe the uniformity of the Hilbert spectrum. The second uses the correlation coefficient as an identification feature, by evaluating the similarity between different Hilbert spectra. The third exploits Fisher's discriminant ratio to obtain the identification features by selecting the Hilbert spectrum elements with strong class separability. When compared with the existing literature, we further consider the identification problem in a relaying scenario, in which the fingerprint of different emitters is contaminated by the relay's fingerprints. Moreover, we explore the identification performance under various channel conditions, such as additive white Gaussian noise, non-Gaussian noise, and fading. Extensive simulation experiments are performed to evaluate the identification performance of the proposed algorithms, and results show their effectiveness in both single-hop and relaying scenarios, as well as under different channel conditions.

In this paper, we adopt a multiobjective optimization for the bit and power allocation problem in order to meet the requirements of emerging wireless systems, i.e., achieving higher throughput without considerably increasing the transmit power. More specifically, we propose to simultaneously maximize the throughput and minimize the transmit power of an OFDM system subject to average bit error rate (BER), power budget, and maximum allocated number of bits per subcarrier constraints. The formulated optimization problem is not convex and we use an evolutionary algorithm, i.e., genetic algorithm, in order to obtain the solution. We study the structure of the problem and the obtained solution and notice that the constraint on the average BER can be replaced by a BER per subcarrier constraint. As such, we propose an approximate non-convex optimization problem. We further exploit the structure of the approximate optimization problem and notice that the BER constraint per subcarrier (i.e., the source of the non-convexity) must be satisfied with an equality sign and can be substituted; this leads to an equivalent convex optimization problem where the global optimality of the Pareto solutions is guaranteed. Closed-form expressions are obtained for the bit and power allocations with reduced complexity. Simulation results show that the proposed multiobjective optimization approach provides significant performance improvements over single objective optimization techniques presented in the literature, without incurring additional complexity.

This paper presents a novel recovery algorithm based on sparsity adaptive matching pursuit (SaMP) and a new near-optimal pilot placement scheme, for compressed sensing (CS)-based sparse channel estimation in orthogonal frequency division multiplexing (OFDM) communication systems. Compared with other state-of-the-art recovery algorithms, the proposed algorithm possesses the feature of SaMP of not requiring a priori knowledge of the sparsity level, and moreover, adjusts the step size adaptively to approach the true sparsity level. Furthermore, we focus on the pilot pattern design in sparse channel estimation. Although a brute-force search guarantees the optimal pilot pattern, it is prohibitive to examine all possibilities due to high computational complexity. It is known that by minimizing the mutual coherence of the measurement matrix when the signal is sparse on the unitary discrete Fourier transform (DFT) matrix, the optimal set of pilot locations is a cyclic difference set (CDS). Based on this, we propose an efficient near-optimal pilot placement scheme in cases where CDS does not exist. Simulation results show that the proposed channel estimation algorithm, with the new pilot placement scheme, offers a better tradeoff between the performance-in terms of mean-squared-error (MSE) and bit-error-rate (BER)-and complexity, when compared to other estimation algorithms.

In this paper, spatial modulation (SM) is used in multiple-input multiple-output (MIMO) spectrum sharing systems in order to enhance the overall spectral efficiency. In particular, a multi-antenna secondary transmitter, employing SM as a modulator, communicates with a multi-antenna secondary receiver in the presence of multiple primary users. To study the effect of estimation errors on the secondary system performance, we derive a closed-form expression for the average pairwise error probability (PEP) over Rayleigh fading channels assuming limited feedback from the PUs. A tight upper bounded average bit error rate is obtained using the PEP expression. Moreover, simple approximate expressions are obtained to get insights on the system diversity and estimation errors' effects. Numerical results, which match with simulations, show the efficacy of SM in improving the overall secondary performance in the presence of estimation errors.

This paper studies the use of spatial modulation (SM) in multiple-input multiple-output (MIMO) cognitive radio networks considering the primary receiver interference constraint and the maximum transmit power of the secondary transmitter. In particular, we investigate the effect of estimation errors on the secondary system performance, where a closed-form expression is derived for the average pairwise error probability (PEP) in Rayleigh fading environments. Based on this PEP expression, a tight upper bounded average bit error probability is obtained using the union bound formula. In addition, an asymptotic analysis is conducted and simple approximate expressions are derived to get useful insights on the system diversity and estimation errors' effects. Numerical results, which are validated through simulations, show that the SM is robust against estimation errors.

A novel approach for specific emitter identification using Hilbert spectrum is proposed for both single-hop and relaying scenarios. In particular, two features, i.e., the energy entropy and color moments, are extracted from the Hilbert spectrum of the signal of interest as identification features. The spectrum is obtained through the Hilbert-Huang transform, which is a powerful tool for the analysis of non-linear and nonstationary signals by decomposing them into a set of intrinsic mode functions. The identification task is solved by applying the support vector machine. We further extend the identification problem to a relaying scenario, in which the fingerprint of different emitters may be contaminated by the relay's fingerprints. To the best of our knowledge, this case has not been investigated so far in the literature. At last, simulation results validate that the proposed approach can effectively cope with the specific emitter identification problems in both single-hop and relaying scenarios.

An efficient algorithm for identifying space-time block-coded orthogonal frequency division multiplexing (STBC-OFDM) signals is introduced in this paper. The inherited signal redundancy is exploited for identification, with the second-order correlations between pairs of signals received from diverse antennas used as the identification feature. The decision on the STBC signal is made by employing the statistical properties of the feature estimate. The proposed algorithm does not require STBC or OFDM block synchronization, channel or noise power estimation, and knowledge of the signal constellation. The performance of the proposed algorithm is evaluated through extensive simulation experiments. The results show the superiority of the proposed algorithm over the previously reported algorithms, with a reduced observation time and at lower signal-to-noise ratio.

In recent years, network coding has emerged as an innovative method that helps wireless network approaches its maximum capacity, by combining multiple unicasts in one broadcast. However, the majority of research conducted in this area is yet to fully utilize the broadcasting nature of wireless networks, and still assumes fixed route between the source and destination that every packet should travel through. This assumption not only limits coding opportunities, but can also cause buffer overflow in some specific intermediate nodes. Although some studies considered scattering of the flows dynamically in the network, they still face some limitations. This paper explains pros and cons of some prominent research in network coding and proposes FlexONC (Flexible and Opportunistic Network Coding) as a solution to such issues. The performance results show that FlexONC outperforms previous methods especially in worse quality networks, by better utilizing redundant packets spread in the network.

Underwater acoustic (UWA) channels are often characterized as time-varying systems which result in intercarrier interference (ICI) in the reception of orthogonal frequency division multiplexing (OFDM) signals. Recently, compressed sensing (CS) has gained a fast-growing interest by exploiting the sparse nature of UWA channels in OFDM communication networks. This paper studies selected characterizations of the UWA channels, and reviews several mathematical UWA channel models in the literature. Moreover, we present a CS-based sparse channel estimation based on a recently-established statistical channel model, which incorporates acoustic signal propagation laws and random local displacements. The sparse coefficients can be estimated using CS-based reconstruction algorithms.

This paper examines the problem of compressed sensing-based sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. In particular, we present an improved estimation algorithm based on the sparsity adaptive matching pursuit (SAMP), which is referred to as the adaptive step size SAMP (AS-SAMP), and compare it with the existing algorithms. Without requiring a priori knowledge of the sparsity, the proposed algorithm adjusts the step size adaptively to approach the true sparsity, thus improving the estimation accuracy. Simulation results show that the proposed algorithm provides a better trade-off between the mean squared error (MSE) performance and complexity when compared with conventional methods.

This paper proposes an efficient identification algorithm for spatial multiplexing (SM) and Alamouti (AL) coded orthogonal frequency-division multiplexing (OFDM) signals. The cross correlation between the received signals from different antennas is exploited to provide a discriminating feature to identify SM-OFDM and AL-OFDM signals. The proposed algorithm requires neither estimation of the channel coefficients and noise power, nor the modulation of the transmitted signal. Moreover, it does not need space-time block code or OFDM block synchronization. The effectiveness of the proposed algorithm is demonstrated through extensive simulation experiments in the presence of diverse transmission impairments, such as time and frequency offsets, Doppler frequency, and spatially correlated fading.

In this paper, we study joint beamforming and power control for downlink multiple-input multiple-output systems with multiple users and target values for signal-to-interference plus noise ratios SINRs. We formulate this as a constrained optimization problem of minimizing total interference subject to constraints on the beamforming vector norms, target SINRs, and total transmit power. Necessary and sufficient conditions satisfied by the optimal beamformer and power allocation are presented, and a new algorithm for joint beamforming and power control is proposed. This adapts the beamforming vectors and transmit powers incrementally, and it stops when the specified SINR targets are achieved with minimum powers. The proposed algorithm is illustrated with numerical results obtained from simulations, which study its convergence and compare it with other similar algorithms. Copyright © 2013 John Wiley & Sons, Ltd.

In cognitive radio (CR) networks, secondary users (SUs) are allowed to opportunistically access the primary users (PUs) spectrum to improve the spectrum utilization; however, this increases the interference levels at the PUs. In this paper, we consider an orthogonal frequency division multiplexing OFDM-based CR network and investigate the tradeoff between increasing the SU transmission rate (hence improving the spectrum utilization) and reducing the interference levels at the PUs. We formulate a new multiobjective optimization (MOOP) problem that jointly maximizes the SU transmission rate and minimizes its transmit power, while imposing interference thresholds to the PUs. Further, we propose an algorithm to strike a balance between the SU transmission rate and the interference levels to the PUs. The proposed algorithm considers the practical scenario of knowing partial channel state information (CSI) of the links between the SU transmitter and the PUs receivers. Simulation results illustrate the performance of the proposed algorithm and its superiority when compared to the work in the literature.

In this paper, we propose physical layer security for cooperative cognitive radio networks (CCRNs) with relay selection in the presence of multiple primary users and multiple eavesdroppers. To be specific, we propose three relay selection schemes, namely, opportunistic relay selection (ORS), suboptimal relay selection (SoRS), and partial relay selection (PRS) for secured CCRNs, which are based on the availability of channel state information (CSI) at the receivers. For each approach, we derive exact and asymptotic expressions for the secrecy outage probability. Results show that under the assumption of perfect CSI, ORS outperforms both SoRS and PRS.

In this paper, an algorithm for the detection of the Alamouti-coded orthogonal frequency division multiplexing (AL-OFDM) signals is proposed. To the best of our knowledge, this is the first time in the literature when the detection of AL-OFDM signals used in recent WiMAX and LTE standards is investigated. The cross-correlation between the signals received with two antennas is studied as a detection feature, and its analytical closed-form expression obtained. These findings are further employed to develop the signal detection algorithm. The algorithm performance is investigated based on simulated standard signals. A good performance is achieved with a short sensing time and at low signal-to-noise ratios (SNRs). Additionally, the proposed algorithm requires neither information about the channel, modulation type, and noise power, nor timing synchronization.

In this paper, a joint resource allocation problem in amplify and forward (AF) OFDM-based two-way multiple-relay cognitive radio network is considered, where two transceiver nodes exchange information via a relay node. The full transmission happens in two phases: multiple access (MA) phase and broadcast (BC) phase. Considering individual power and interference constraints, the power allocation, subcarrier pairing and relay selection are jointly optimized in order to maximize the sum-rate. The dual decomposition technique is applied to obtain the optimal solution. Additionally, an efficient suboptimal algorithm which drastically reduces the computational complexity of the optimal solution with a small performance degradation is proposed. Finally, simulation results are shown to demonstrate the performance gain of the proposed algorithms.

A joint channel assignment and power allocation algorithm is presented for cognitive wireless networks, where primary and secondary users operate over the same frequency band at the same time. In this study, we take into consideration a constraint on the interference from secondary users towards primary users, as well as a constraint on the QoS of secondary users. The problem is formulated as a constrained utility maximization problem, for which we prove the existence and the uniqueness of the global optimum solution. Since the problem is NP-hard, we provide a novel heuristic algorithm that operates in two phases. Firstly, it admits all the incoming secondary users and allocates the available channels through a distributed and dynamic procedure, taking into account the interference constraint. Secondly, an iterative power control, which considers both constraints, is applied. In each iteration, a user removal algorithm is employed to reduce the number of admitted secondary users until a steady state is reached. The performance of the joint resource allocation algorithm is investigated in terms of the total number of admitted secondary users for several network parameters, such as the minimum QoS requirements and the interference constraint.

In this paper, the power loading problem for orthogonal frequency division multiplexing (OFDM) with imperfect channel estimation is investigated considering the trade-off between energy efficiency (EE) and spectral efficiency (SE). Unlike traditional research that uses the EE as an objective function and imposes constraints either on the SE or the achievable rate, we propound a multiobjective optimization approach that can flexibly switch between the EE and the SE functions or change the priority level of each function using a trade-off parameter. Our dynamic approach is more tractable than conventional approaches and more convenient to realistic communication applications and scenarios. The system model considers the path loss and shadowing effect in modeling the EE and SE metrics, in addition to taking the channel estimation error into account. We first solve the marginal problems of maximizing the EE and the SE individuality, and then prove that the multiobjective optimization of the EE and the SE is equivalent to a simple problem that maximizes the capacity and minimizes the total power consumption. Finally, we use numerical results to discuss the choice of the trade-off parameter and study the effect of the estimation error, transmission power budget and channel-to-noise ratio on the multiobjective optimization.

This letter proposes a blind modulation classification (MC) algorithm applicable to single and multiple-antenna systems operating over frequency-selective channels. We show that the correlation functions of the received signals for certain modulation formats exhibit peaks at a particular set of time lags, a result which can be exploited as a discriminating feature. We also develop a new hypothesis test in order to detect the correlation-induced peaks. The proposed algorithm is general in the sense that it accommodates any number of transmit- and receive-antennas, without prior information about channel statistics. The classification performance of the proposed algorithm is assessed through Monte Carlo simulations.

This paper addresses the problem of space-time block code (STBC) identification for multiple-antenna (MA) orthogonal frequency-division multiplexing (OFDM) systems operating over frequency-selective channels for the first time in literature. Previous investigations published on the topic of STBC identification were restricted to single-carrier systems operating over frequency-flat channels. OFDM systems make this topic more challenging to handle since the identifiers work in frequency-selective channels with little or no knowledge of the beginning of the OFDM blocks, OFDM parameters, and frequency-selective channel coefficients. We show that, by taking advantage of the space-time redundancy, STBC identification can be performed by exploiting the cross-correlation of the signals received from different antennas as a discriminating feature. Using this feature, we develop a binary hypothesis test for decision making. The proposed method does not require information about the channel coefficients, modulation format, noise power, or timing of the OFDM and STBC blocks. Moreover, it does not need accurate knowledge of either clock-timing information or OFDM parameters, including the number of sub-carriers and cyclic prefix length. Extensive simulation experiments have verified the effectiveness of the proposed method.

This paper adopts a multiobjective optimization (MOOP) approach to investigate the optimal link adaptation problem of orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, where secondary users (SUs) can opportunistically access the spectrum of primary users (PUs). For such a scenario, we solve the problem of jointly maximizing the CR system throughput and minimizing its transmit power, subject to constraints on both SU and PUs. The optimization problem imposes predefined interference thresholds for the PUs, guarantees the SU quality of service in terms of a maximum bit-error-rate (BER), and satisfies a transmit power budget and a maximum number of allocated bits per subcarrier. Unlike most of the work in the literature that considers perfect SU spectrum sensing capabilities, the problem formulation takes into account errors due to imperfect sensing of the PUs bands. Closed-form expressions are obtained for the optimal bit and power allocations per SU subcarrier. Simulation results illustrate the performance of the proposed algorithm and demonstrate the superiority of the MOOP approach when compared to single optimization approaches presented in the literature, without additional complexity. Furthermore, results show that the interference thresholds at the PUs receivers can be severely exceeded due to the perfect spectrum sensing assumption or due to partial channel information on links between the SU and the PUs receivers. Additionally, the results show that the performance of the proposed algorithm approaches that of an exhaustive search for the discrete optimal allocations with a significantly reduced computational effort.

In this paper, we adopt a multiobjective optimization approach to jointly optimize the rate and power in OFDM-based cognitive radio (CR) systems. We propose a novel algorithm that jointly maximizes the OFDM-based CR system throughput and minimizes its transmit power, while guaranteeing a target bit error rate per subcarrier and a total transmit power threshold for the secondary user (SU), and restricting both co-channel and adjacent channel interferences to existing primary users (PUs) in a statistical manner. Since the interference constraints are met statistically, the SU transmitter does not require perfect channel-state-information (CSI) feedback from the PUs receivers. Closed-form expressions are derived for bit and power allocations per subcarrier. Simulation results illustrate the performance of the proposed algorithm and compare it to the case of perfect CSI. Further, the results show that the performance of the proposed algorithm approaches that of an exhaustive search for the discrete global optimal allocations with significantly reduced computational complexity.

This paper proposes a novel and efficient algorithm for space-time block code (STBC) classification, when a single antenna is employed at the receiver. The algorithm exploits the discriminating features provided by the discrete Fourier transform (DFT) of the fourth-order lag products (FOLPs) of the received signal. It does not require estimation of the channel, signal-to-noise ratio (SNR), and modulation of the transmitted signal. Computer simulations are conducted to evaluate the performance of the proposed algorithm. The results show the validity of the algorithm, its robustness to carrier frequency offset, and low sensitivity to timing offset.

This paper discusses the receiver design of a destination node supporting multiple antennas for an alternate-relaying decode-and-forward (DF) cooperative communication system. We exploit the structure of the received signal to develop an optimal data detection algorithm at the destination. It is shown that the optimal algorithm can be implemented by parallel detectors, each based on a family of Bahl, Cocke, Jelinek and Raviv (BCJR) algorithms. The proposed optimal algorithm requires to receive and store all packets before performing data detection. To avoid this, a sub-optimal algorithm is also proposed. Unlike the optimal algorithm, the sub-optimal one exploits two consecutive received packets to detect one packet. It turns out that the sub-optimal algorithm has less reduced delay, complexity, memory size and bandwidth loss with a slight increase of the bit-error-rate. The detection performance of the proposed algorithms is evaluated via Monte Carlo simulations, and the results demonstrate their effectiveness.

In this paper, we investigate the resource allocation problem (joint bit and power loading) of secondary users sharing the radio spectrum with primary users in cognitive radio networks. We consider the co-existence scenario where a secondary user is allowed to access the shared spectrum while guaranteeing tolerable interference to primary users. For such a scenario, we formulate and solve an optimization problem that jointly maximizes the secondary user throughput and minimizes its transmit power while satisfying target bit error rate per subcarrier and certain limits of co-channel and adjacent channel interferences to existing primary users. Simulation results are described that illustrate the performance of the proposed algorithm, and show its closeness to that of an exhaustive search for the equivalent discrete formulation.

This paper studies the joint rate and power allocation problem for OFDM-based cognitive radio systems where secondary users (SUs) can opportunistically access the spectrum of primary users (PUs). We propose a novel algorithm that jointly maximizes the OFDM-based cognitive radio system throughput and minimizes its transmit power, while guaranteeing a target bit error rate per subcarrier and restricting both co-channel interference (CCI) and adjacent channel interference (ACI) to existing primary users. Since estimating the instantaneous channel gains on the links between the SU transmitter and the PUs receivers is impractical, we assume only knowledge of the path loss on these links. Closed-form expressions are derived for the close-to-optimal bit and power distributions. Simulation results are described that illustrate the performance of the proposed scheme and show its closeness to that of an exhaustive search for the discrete optimal allocations. Further, the results quantify the violation ratio of both the CCI and ACI constraints at the PUs receivers due to the partial channel information. The effect of adding a fading margin to reduce the violation ratio is also studied.

Blind signal identification is an important topic of research for both commercial and military communications. A novel identification algorithm for spatial multiplexing (SM) and Alamouti space-time block code (AL-STBC) signals is proposed in this paper, when the receiver is equipped with a single antenna. The proposed algorithm exploits a discriminating feature provided by the discrete Fourier transform (DFT) of the fourth-order lag product (FOLP) of the received signal. The proposed algorithm requires neither estimation of the channel, noise power, nor modulation of the transmitted signal. Computer simulations are conducted to evaluate the algorithm performance; these show the validity of the proposed algorithm with low sensitivity to timing offset.

This paper develops a space-time block code (STBC) identification algorithm for multi-antenna (MA) orthogonal frequency-division multiplexing (OFDM) transmission over frequency-selective channels. We show that, by taking advantage of the space-time redundancy, signal identification can be performed by exploiting the cross-correlation of the received signals from two antennas as a discriminating feature. With this feature, we develop a binary hypothesis test for decision making. The proposed algorithm does not require information about the channel coefficients, modulation, noise power, or start times of the OFDM and STBC blocks. Moreover, it does not need accurate clock timing information. Simulation experiments have verified the accuracy and feasibility of the proposed algorithm.

A key issue in the full‐rate alternate‐relaying cooperative communication systems is the interference which is caused by the simultaneous transmission of the source and one of the relays. In this study, the authors propose maximum‐likelihood (ML) detectors to mitigate the interference in such systems. Unlike previous work in which interference cancellation is required at the destination, the authors exploit the interference signal as a beneficial resource to develop an optimal detector. It is shown that the optimal detector can be implemented by parallel Viterbi algorithms. The major drawback of the proposed optimal detector is the delay because the destination has to receive and store the entire frame before performing data detection. Owing to the inevitable delay restriction, a sub‐optimal detector is developed. In contrast with the optimal detector, the sub‐optimal detector exploits two consecutive received packets to decode one packet. It turns out that the sub‐optimal detector significantly reduces the required delay, memory size and bandwidth loss, with a slight increase of the bit‐error‐rate and the computational complexity. Extensive simulation results have been presented to demonstrate the effectiveness of the proposed detectors.

Blind signal classification, a major task of intelligent receivers, has important civilian and military applications. This problem becomes more challenging in multi-antenna scenarios due to the diverse transmission schemes that can be employed, e.g., spatial multiplexing (SM) and space-time block codes (STBCs). This paper presents a class of novel algorithms for blind classification of SM and Alamouti STBC (AL-STBC) transmissions. Unlike the prior art, we show that signal classification can be performed using a single receive antenna by taking advantage of the space-time redundancy. The first proposed algorithm relies on the fourth-order moment as a discriminating feature and employs the likelihood ratio test for achieving maximum average probability of correct classification. This requires knowledge of the channel coefficients, modulation type, and noise power. To avoid this drawback, three algorithms have been further developed. Their common idea is that the discrete Fourier transform of the fourth-order lag product exhibits peaks at certain frequencies for the AL-STBC signals, but not for the SM signals, and thus, provides the basis of a useful discriminating feature for signal classification. The effectiveness of these algorithms has been demonstrated in extensive simulation experiments, where a Nakagami-m fading channel and the presence of timing and frequency offsets are assumed.

This paper investigates the second-order cyclostationarity of block transmitted-single carrier linearly digitally modulated (BT-SCLD) signals, and its applications to signal classification and blind (non-data aided) parameter estimation. Analytical closed-form expressions are derived for the cyclic autocorrelation function (CAF), cyclic spectrum (CS), complementary CAF (CCAF), complementary CS (CCS), and corresponding cycle frequencies (CFs). Furthermore, the conditions for avoiding aliasing in the cycle and spectral frequency domains are obtained. Based on these findings, we propose algorithms for classifying BTSCLD, orthogonal frequency division multiplexing (OFDM), and SCLD signals, and for the blind estimation of the BT-SCLD block transmission parameters. Simulation and laboratory experiments demonstrate the effectiveness of the proposed algorithms under low signal-to-noise ratios (SNRs), short sensing times, and various channel conditions. Furthermore, these algorithms have the advantage of not requiring the recovery of carrier, waveform, and symbol timing information, or the estimation of signal and noise powers.

This paper proposes a novel low complexity joint bit and power suboptimal allocation algorithm for multicarrier systems operating in fading environments. The algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target bit error rate (BER) per subcarrier and meeting a constraint on the total transmit power. Simulation results are described that illustrate the performance of the proposed scheme and demonstrate its superiority when compared to the algorithm in [4] with similar or reduced computational complexity. Furthermore, the results show that the performance of the proposed suboptimal algorithm approaches that of an optimal exhaustive search with significantly lower computational complexity.

In this paper, a novel joint bit and power loading algorithm is proposed for orthogonal frequency division multiplexing (OFDM) systems operating in fading environments. The algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target average bit error rate (BER) and meeting a constraint on the total transmit power. Simulation results are described that illustrate the performance of the proposed scheme and demonstrate its superiority when compared to the algorithm in [1].

In this paper, a novel low complexity bit and power loading algorithm is formulated for orthogonal frequency division multiplexing (OFDM) systems operating in fading environments and in the presence of unknown interference. The proposed non-iterative algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target bit error rate (BER) per subcarrier. Closed-form expressions are derived for the optimal bit and power distributions per subcarrier. The performance of the proposed algorithm is investigated through extensive simulations. A performance comparison with the algorithm shows the superiority of the proposed algorithm with reduced computational effort.

Cognitive radio (CR) systems require knowledge of the signal environment if they are to coexist with the primary (incumbent) users of the radio spectrum. Consequently, signal identification is a major task of a CR. This paper proposes a novel identification algorithm for spatial multiplexing (SM) and Alamouti space-time block code (AL STBC) transmissions, when the CR employs a single receive antenna. This algorithm relies on the fourth-order moment as a discriminating signal feature and uses a maximum likelihood (ML) criterion for decision making. Its performance is investigated through theoretical analysis and simulation experiments.

Signal classification is a major task of a cognitive radio. This paper proposes a novel cyclostationarity-based algorithm for the blind classification of space time block codes (STBCs) and derives analytical expressions for the second-order cyclic statistics used as the basis of the algorithm. Monte Carlo simulation results demonstrate a good classification performance with low sensitivity to phase noise and the channel Doppler shift. The proposed approach avoids the need for a priori knowledge of the channel coefficients, carrier phase, and timing offsets. Moreover, it does not need accurate information about the transmission data rate and carrier frequency offset.

In this paper, we propose the use of bit-interleaved coded modulation iterative decoding (BICM-ID) in the full-rate decode and forward cooperative communication systems. At the destination, we exploit the interference signal to develop the optimal detector. It is shown that the proposed detector is implemented by parallel concatenating maximum a posteriori (MAP) algorithms and dampers to the decoders. The detector exchanges soft information between decoders and MAP algorithms in an iterative way, thus, improving communication reliability. Due to the inevitable delay restriction of the optimal detector, a sub-optimal detector is developed. Extensive simulation results are presented to demonstrate the effectiveness of proposed detectors. Results indicate that the proposed detectors outperform conventional relaying detectors in terms of their bit error rate.

Spectrum sensing and awareness constitute key functionalities of a cognitive radio (CR), and encompass signal detection, classification, and blind parameter estimation. This paper proposes a novel algorithm for the tone frequency spacing estimation and joint classification of M-ary frequency shift keying (M-FSK) signals in a fading environment. The proposed algorithm relies on the number and position of the first-order cycle frequencies (CFs), and requires neither recovery of the carrier and symbol timing, nor the estimation of channel parameters or signal and noise powers. Receive spatial diversity is exploited to enhance the classification and estimation performance of the algorithm. The simulation results for the algorithm performance confirm its effectiveness.

This paper proposes a novel joint bit and power allocation algorithm for multicarrier systems operating in fading environments. The algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target average bit error rate (BER). Simulation results are described and they illustrate the performance of the proposed scheme and demonstrate its superiority with respect to existing schemes.

Automatic classification of the modulation type of an unknown communication signal is a challenging task, with applications in both commercial and military contexts, such as spectrum surveillance, cognitive radio, and electronic warfare systems. Most of the automatic modulation classification (AMC) algorithms found in the literature assume that the signal of interest has been transmitted using a single antenna. In this paper, a novel AMC algorithm for multiple input multiple output (MIMO) signals is proposed, which employs fourth-order cumulants as features for classification. First, perfect channel state information (CSI) is assumed. Subsequently, a case of more practical relevance is considered, where the channel matrix is unknown and has to be estimated blindly by employing independent component analysis (ICA). The performance of the proposed classification algorithm is investigated through simulations and compared with an average likelihood ratio test (ALRT) which can be considered as optimum in the Bayesian sense, but has a very high computational complexity.

In this paper, we consider a network-coded cooperative wireless network, where users mutually pair among themselves to realize network coding. We consider a multi-user environment, where users transmit to a common destination in the absence of dedicated relays. Two nodes constituting a pair periodically swap the roles of the source and relay to mutually achieve spatial diversity. As such, conditioned on the successful detection of the source''s packet, a networkcoded packet is formed at the relay by a linear combination of its own packet and the source''s packet. A single transmission of this network-coded packet therefore helps both nodes to achieve diversity gain. In this work, we address the important problem of the mutual pairing of users, which directly governs the overall network performance. We first propose an optimal user pairing algorithm in order to maximize the total network capacity. To simplify the pairing process, we subsequently propose computationally simpler, heuristic user pairing schemes. In particular, we propose max-max pairing to maximize the network capacity, and max-min pairing to minimize the outage probability. Performance analysis of the proposed optimal and heuristic user pairing schemes is performed in terms of the average capacity, average outage probability, and user-fairness.

In this paper we consider orthogonal frequency division multiplexing (OFDM) based cognitive radio systems that operate as secondary users (SUs) in a spectrum overlay approach and study joint spectral shaping and power control subject to specified target signal-to-interference+noise ratio (SINR) as a constrained optimization problem. We discuss necessary and sufficient conditions for the optimal solution of this problem where the spectrum overlay constraints are satisfied and the specified target SINR value is achieved with minimum transmit power. We also present an algorithm that incrementally adapts the OFDM transmitter to reach the optimal solution and study the convergence speed of the proposed algorithm through simulations.

Signal classification is important in various commercial and military applications. Multiple antenna systems complicate the signal classification problem since there is now the issue of estimating the number and configuration of transmit antennas. The novel blind classification algorithm proposed in this paper exploits the cyclostationarity property of space-time block codes (STBCs) for the classification of multiple antenna systems in the presence of possible transmission impairments. Analytical expressions for the second-order cyclic statistics used as the basis of the algorithm are derived, and the computational cost of the proposed algorithm is considered. This algorithm avoids the need for a priori knowledge of the channel coefficients, modulation, carrier phase, and timing offsets. Moreover, it does not need accurate information about the transmission data rate and carrier frequency offset. Monte Carlo simulation results demonstrate a good classification performance with low sensitivity to phase noise and channel effects, including frequency-selective fading and Doppler shift.

Cognitive radio (CR) technology represents a promising solution for solving the problem of spectrum scarcity and underutilization in wireless communications. While orthogonal frequency division multiplexing (OFDM) is regarded as a suitable technology for the CR physical layer, it can suffer from out-of-band radiation, which hinders its spectrum sharing capabilities. In this paper we consider a turbo-coded OFDM based CR system, and propose three methods for shaping the spectrum of the CR signal to reduce interference affecting primary users (PUs) that operate in adjacent bands. The proposed methods use multiple choice sequences (MCS) generated at the bit level by employing different pseudo random bit sequences, by using bit interleavers with different depths, or by a combination of both, respectively. In order to reduce interference to adjacent bands, the sequence with the best spectral shape is transmitted along with its index. Performance of the proposed methods is illustrated with numerical results obtained from simulations, and the trade off between a better spectral shape and the reduction in the spectral efficiency of the systems is discussed.

In this paper, we propose maximum likelihood (ML) detectors to mitigate the influence of the interference signal for the two-relays full- rate cooperative systems. At the relays, the proposed ML detector is employed by averaging out the interference signal. Furthermore, at the destination, we exploit the interference signal to develop the ML detector. It is shown that the optimal detector is implemented by parallel Viterbi algorithms. The major drawback of the proposed optimal detector is the delay, i.e a destination has to receive and store the whole received packets before performing data detection. Due to the inevitable delay restriction, sub-optimal detector is developed. In contrast with the optimal detector, the sub-optimal detector exploits two consecutive received packets to decode one packet. It turns out that the sub-optimal detector outperforms the optimal detector in terms of the required delay, memory size, bandwidth loss, and computational complexity. Extensive simulation results have been presented to demonstrate the effectiveness of the proposed detectors. Results indicate that the proposed detectors outperform conventional relaying detectors in terms of their bit error rate and packet error rate.

Spectrum awareness is one of the most challenging requirements in cognitive radio (CR). To adequately adapt to the changing radio environment, it is necessary for the CR to be able to perform joint detection and classification of low signal-to-noise ratio (SNR) signals. In this paper we propose a joint detection and classification algorithm for the mobile Worldwide Interoperability for Microwave Access (WiMAX) and Long Term Evolution (LTE) signals, which exploits the second-order signal cyclostationarity. Simulation results are presented, which show the efficiency of the proposed algorithm under diverse scenarios. Furthermore, we provide new analytical findings related to the second-order cyclostationarity of the signals of interest.

A key issue in the full-rate two-relay cooperative communication systems is the interference which is caused by the simultaneous transmission of the source and one of the relays at any time. In this paper, we exploit the interference signal at the destination to develop a maximum likelihood (ML) detector for decode and forward full-rate cooperative systems. It is shown that the Viterbi algorithm can be employed to find the ML solution. To reduce the complexity of the proposed ML detector, a sub-optimal detector is also introduced. Further, we propose a ML interference cancellation scheme at the relays. The performance of the proposed schemes is evaluated through Monte Carlo simulations. Results indicate that the proposed schemes outperform direct transmission and conventional relaying schemes in terms of their bit error rate.

Cyclostationary characteristics of communication signals can be exploited for performing various signal processing tasks. Receiver impairments that affect the cyclic statistics of signals may lead to a degradation in the performance of cyclostationarity-based signal processing algorithms. Inphase/ quadrature (I/Q) imbalance, oscillator phase noise, and random sampling jitter can be counted amongst the typical receiver impairments encountered in wireless communication systems. In this work, we investigate the effects of these nonidealities on the second-order cyclic statistics of baseband communication signals. General results are derived for arbitrary complex-valued (and possibly improper) signals, and examples are provided for cases of practical interest.

In this paper we present a new algorithm for detecting mobile Worldwide Interoperability for Microwave Access (WiMAX) signals in cognitive radio systems, which is based on preamble-induced second-order cyclostationarity. We derive closed form expressions for the cyclic autocorrelation function (CAF) and cyclic frequencies (CFs) due to the preamble, and use these results in the proposed algorithm for signal detection. The algorithm is illustrated with numerical results obtained from simulations, which demonstrate its efficiency under diverse scenarios.

In this paper, a distinctive cyclostationarity-based feature of the Long Term Evolution (LTE) Orthogonal Frequency Division Multiplexing (OFDM) signals used in the Frequency Division Duplex (FDD) downlink transmission is proved, and further employed for their detection. This relates to the existence of the reference signals (RSs) used for channel estimation and cell search/acquisition purposes. The analytical closed form expressions for the RS-induced cyclic autocorrelation function (CAF) and cyclic frequencies (CFs) are derived. Based on these findings, a signal detection algorithm is then developed. Simulation results show that the proposed algorithm achieves a good detection performance for low signal-to-noise ratios (SNRs), short sensing times, and under diverse channel conditions.

Cognitive radio is regarded as a novel approach for improving the utilization of precious radio spectrum resource. The detection of very low signal-to-noise ratio (SNR) signals with relaxed a priori information on the signal parameters is of high importance to such radios. This paper proposes a detection algorithm based on the first-order cyclostationarity for amplitude modulated (AM) signals that only requires rough information on the signal bandwidth and carrier frequency. A theoretical asymptotic analysis is performed. Simulation results show that the proposed algorithm performs well at low SNRs.

Cognitive radio (CR) represents a possible solution to the paradoxical problem of simultaneous scarcity and underutilization of the electromagnetic spectrum. Spectrum awareness, a key task of such a radio, encompasses the recognition of the received signal type and parameters. This paper investigates the cyclostationarity approach for the recognition of cyclically prefixed single carrier linearly digitally modulated (CP-SCLD) signals versus SCLD and orthogonal frequency division multiplexing (OFDM) signals under practical conditions, including time-dispersive channels, additive Gaussian noise, and phase, frequency and timing offsets. Analytical closed-form expressions are derived for the cyclic autocorrelation function (CAF) and the set of cycle frequencies (CFs) of CP-SCLD signals. These results are the basis of the proposed signal recognition algorithm. This algorithm has the advantage of avoiding requirements for the recovery of carrier, waveform, and symbol timing information, and the estimation of signal and noise powers.

This paper analyzes the performance of Proportional Fair (PF) scheduling in Orthogonal Frequency Division Multiple Access (OFDMA) wireless systems. OFDMA represents a promising multiple access scheme for high-data-rate transmission over wireless channels as it combines the Orthogonal Frequency Division Multiplexing (OFDM) modulation and subcarrier allocation. On the other hand, the PF scheduling is an efficient resource allocation scheme with good fairness characteristics. Consequently, OFDMA with PF scheduling represents an attractive solution to deliver high data rate services to multiple users simultaneously with a high degree of fairness. We investigate a two dimensional (time slot and frequency subcarrier) PF scheduling algorithm for OFDMA systems, and evaluate its performance analytically and by simulation. We derive closed-form expressions for the average throughput and throughput fairness index. Computer simulations are used for verification. The analytical results agree well with the results from simulations, which verifies the correctness and accuracy of the analytical expressions.

In this paper, we develop a new algorithm to jointly estimate the channel impulse response and inphase-quadrature (IQ) imbalances for OFDM systems. The estimation algorithm is based on the expectation maximization(EM) algorithm, exploiting information from the pilot symbols and detected data symbols in a systematic fashion. To reduce the complexity of the estimation algorithm, a suboptimal scheme is also introduced. The results indicate that the proposed algorithms achieve a significant improvement in the bit error rate (BER) performance when compared with conventional data-aided algorithms.

Previous studies on the cyclostationarity aspect of orthogonal frequency division multiplexing (OFDM) and single carrier linearly digitally modulated (SCLD) signals assumed simplified signal and channel models or considered only second-order cyclostationarity. This paper presents new results concerning the cyclostationarity of these signals under more general conditions, including time dispersive channels, additive Gaussian noise, and carrier phase, frequency, and timing offsets. Analytical closed-form expressions are derived for time- and frequency-domain parameters of the cyclostationarity of OFDM and SCLD signals. In addition, a condition to eliminate aliasing in the cycle and spectral frequency domains is derived. Based on these results, an algorithm is developed for recognizing OFDM versus SCLD signals. This algorithm obviates the need for commonly required signal preprocessing tasks, such as signal and noise power estimation and the recovery of symbol timing and carrier information.

In this paper, likelihood-based algorithms are explored for linear digital modulation classification. Hybrid likelihood ratio test (HLRT)- and quasi HLRT (QHLRT)- based algorithms are examined, with signal amplitude, phase, and noise power as unknown parameters. The algorithm complexity is first investigated, and findings show that the HLRT suffers from very high complexity, whereas the QHLRT provides a reasonable solution. An upper bound on the performance of QHLRT-based algorithms, which employ unbiased and normally distributed non-data aided estimates of the unknown parameters, is proposed. This is referred to as the QHLRT-Upper Bound (QHLRT-UB). Classification of binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals is presented as a case study. The Cramer-Rao Lower Bounds (CRBs) of non-data aided joint estimates of signal amplitude and phase, and noise power are derived for BPSK and QPSK signals, and further employed to obtain the QHLRT-UB. An upper bound on classification performance of any likelihood-based algorithms is also introduced. Method-of-moments (MoM) estimates of the unknown parameters are investigated and used to develop the QHLRT-based algorithm. Classification performance of this algorithm is compared with the upper bounds, as well as with the quasi Log-Likelihood Ratio (qLLR) and fourth-order cumulant based algorithms.

The second-order cyclostationarity of cyclically prefixed single carrier linear digital (CP-tSCLD) modulated signals is investigated with emphasis on its applicability to signal recognition. Analytical closed-form expressions for the second-order (one-conjugate) cyclic cumulants (CCs) and the set of cycle frequencies (CFs) for CP-SCLD modulated signals are derived. Based on these results, an algorithm is proposed for the recognition of CP-tSCLD against SCLD and orthogonal frequency division multiplexing (OFDM) signals. This algorithm obviates the need for signal pre-processing tasks, such as symbol timing, carrier and waveform recovery and estimation of signal and noise powers.

The sensing of the radio frequency (RF) environment is a fundamental concept of cognitive radio (CR). It follows that the detection and classification of very low signal-to-noise ratio signals with relaxed a priori information on signal parameters is a problem of considerable relevance to CR. This paper proposes an algorithm based on first-order cyclostationarity for joint detection and classification of frequency-shift-keying (FSK) signals. This algorithm does not require timing and frequency recovery, and estimation of signal and noise powers. The theoretical analysis of the algorithm performance is validated by simulation results.

This paper studies the nth-order cyclostationarity of orthogonal frequency division multiplexing (OFDM) and single carrier linearly digitally modulated (SCLD) signals affected by a time dispersive channel, additive Gaussian noise, carrier phase, and frequency and timing offsets. The analytical closed-form expressions for the nth-order cyclic cumulants (CCs) and cycle frequencies (CFs) of OFDM and SCLD signals are derived. Furthermore, a second-order CC-based algorithm is developed to recognize OFDM against SCLD signals under the aforementioned conditions. This algorithm obviates the need for signal preprocessing tasks, such as symbol timing estimation, carrier and waveform recovery, and signal and noise power estimation. Simulation experiments confirm the theoretical analysis.

The paper studies the cyclostationarity of an orthogonal frequency division multiplexing (OFDM) with a view to recognizing OFDM against single carrier linear digital (SCLD) modulations. The analytical expressions for the nth-order cyclic cumulants (CCs) and cycle frequencies of an OFDM signal embedded in additive white Gaussian noise and subject to phase, frequency and timing offsets are derived An algorithm based on a second-order CC is proposed to recognize OFDM against SCLD modulations. The recognition algorithm of the authors obviates the need for preprocessing tasks, such as symbol timing estimation, carrier and waveform recovery, and signal and noise power estimation. The results of simulation experiments confirm the theoretical analysis.

In this paper, first-order cyclostationarity of M-ary frequency shift keying (M-FSK) signals affected by additive Gaussian noise, phase, frequency offset and timing errors is investigated, and applied for modulation order recognition. A novel recognition algorithm is proposed, which employs the number of first-order cycle frequencies (CFs) of the received signal as a discriminating feature. The algorithm has the advantage that it requires neither symbol timing and carrier recovery, nor estimation of signal and noise powers as preprocessing tasks. Simulations are carried out to confirm theoretical developments.

The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information and so on, blind identification of the modulation is a difficult task. This becomes even more challenging in real-world scenarios with multipath fading, frequency-selective and time-varying channels. With this in mind, the authors provide a comprehensive survey of different modulation recognition techniques in a systematic way. A unified notation is used to bring in together, under the same umbrella, the vast amount of results and classifiers, developed for different modulations. The two general classes of automatic modulation identification algorithms are discussed in detail, which rely on the likelihood function and features of the received signal, respectively. The contributions of numerous articles are summarised in compact forms. This helps the reader to see the main characteristics of each technique. However, in many cases, the results reported in the literature have been obtained under different conditions. So, we have also simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies. Furthermore, new problems that have appeared as a result of emerging wireless technologies are outlined. Finally, open problems and possible directions for future research are briefly discussed.

In this paper we develop an algorithm based on higher-order cyclic cumulants for the automatic recognition of QAM signals. The method is robust to the presence of carrier phase and frequency offsets. Theoretical arguments are verified with simulations performed for 4-QAM and 16-QAM signals.