In this paper, we propose an aerial reconfigurable intelligent surface (ARIS)-enhanced unmanned aerial vehicle (UAV)-to-vehicle channel model that considers a three-dimensional (3D) continuously arbitrary trajectory. The model uses an ARIS mounted on a UAV to reflect signals to the terrestrial vehicle, which facilitates and enhances signal propagation. A generalized 3D random mobility model (RMM) with smooth turns is developed to characterize ARIS kinematics, where state differential equations and Euler approximation are employed for low-complexity real-time trajectory updates. Moreover, we model both the ARIS translational jitter and attitude jitter caused by wind or air turbulence as zero-mean Gaussian random variables, investigating the sensitivity of channel characteristics to assess the system robustness under non-ideal control conditions. The resulting trajectory and jitter models capture the unique channel properties in realistic scenarios. Furthermore, key statistical properties of the proposed channel model are derived, including spatial-temporal (ST) cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency correlation functions (FCFs). We analyze the impact of ARIS trajectory and physical parameters on these statistical properties, such as velocity magnitude, phase shifts design, unit numbers, and rotation angles. Numerical simulation results demonstrate the superiority of ARIS-enhanced UAV communications using the proposed generalized 3D smooth-turn RMM with both translational jitter and attitude jitter, highlighting the significant role of ARIS in UAV communications.

Computation offloading has been extensively studied in recent years for the internet of vehicles (IoV), where roadside units (RSUs) are deployed to assist computation offloading. However, it is still challenging to decide when to offload regarding to multiple factors, such as load differences among RSUs, a vehicle’s moving speed, and a vehicle’s energy constraint. In this paper, an optimal offloading decision method is proposed based on optimal stopping theory (OST) to decide when to offload considering the aforementioned factors. Firstly, two offloading decision problems with and without energy constraint are constructed to find the optimal RSU which can minimize expected cost, where the expected cost is determined by the decision on offloading to the current RSU or continuing observing the next RSU. Then, OST is utilized to solve these two problems. Specifically, a sequence of thresholds are pre-calculated based on the OST. An offloading decision can be made by comparing the load of current RSU with the threshold. Moreover, some facts on a vehicle’s moving speed in the environment without energy constraint and the number of observations on RSUs in the environment with energy constraint are revealed. What’s more, the optimal moving speed which can minimize the expected cost is also provided. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed method. The effects of a vehicle’s moving speed and the number of observations on the performance of the proposed method are also verified. Comparing to the benchmarks, the proposed method can achieve superior performance in terms of cost and hit ratio, and has comparable performance with the best offloading method which has full RSUs’ load information. Moreover, the proposed method is robust to the estimation deviation of RSUs’ load distribution.

A covert ISAC transmission scheme based on an innovative heterogeneous sub-connected hybrid analog and digital (H2AD) multiple-input multiple-output (MIMO) transceiver is investigated in this paper. Specifically, H2AD ISAC system possesses the capability to detect the point-like target while covertly transmitting confidential information to a singleantenna legitimate user, and enabling secure transmission without detection by the warden. The objective is to maximize the covert transmission rate for legitimate users while adhering to the Cramér-Rao bound (CRB) threshold. However, due to the coupling of multiple variables under the H2AD transceiver framework, the optimization problem becomes non-convex. To tackle the challenging, an alternating optimization algorithm based on Dinkelbach’s transformation and semidefinite relaxation (DTSDR) is proposed to design the analog and digital beamforming along with the sensing signal. Then, by utilizing historical system states and optimizing for long-term returns, an improved distributional soft Actor-Critic with three refinements (DSACv2) algorithm framework based on deep reinforcement learning (DRL) is proposed. Simulation results demonstrate that incorporating the novel H2AD MIMO antenna array into ISAC system design enhances the covert performance while ensuring target sensing performance.

Low-light video enhancement is crucial for improving the visual reliability of IoT edge devices in low-light environments. However, existing methods often rely on complex network architectures, require strictly curated data, or complex preprocessing computation, resulting in poor real-time performance and limited generalization. We propose an unsupervised low-light video enhancement framework named ViD-EnlightenGAN. By incorporating temporal attention mechanisms and multi-discriminator constraints, our method achieves inter-frame consistency preservation and dynamic brightness adjustment without complex pre-processing such as optical flow or keyframe matching. Experiments demonstrate that our method achieves outstanding performance on the SDSD dataset (PSNR: 23.711 dB, SSIM: 0.695), delivering high visual quality and temporal consistency. The code will be available at https://github.com/ apperrs/ViD-EnlightenGAN.

In recent years, unmanned aerial vehicle (UAV) has swept across the communication industry for its superior mobility and flexibility. UAV-aided communicaiton has been widely concerned by the academia and industry in space-air-ground integrated network, disaster relief network, Internet of Thing (IoT) system and so on. With the development of UAV-aided communication technology, the performance of transmission has been significantly improved. Subsequently, the problem of security transmission of UAV-aided communication has become increasingly prominent. In this paper, the security capacity of a UAV-aided IoT system is studied. Firstly, a channel field multiple access (CFMA) wireless sensor data transmission framework is proposed in UAV-aided IoT system. It contains a UAV base station, multiple IoT terminals and several malicious users. Under this framework, the UAV base station regularly flies over the area where the IoT system is located. The IoT terminals transmit sensing data based on the CFMA scheme. At the same time, surrounding malicious users cooperatively eavesdrop the sensing data of IoT terminal. Secondly, the system security capacity is analyzed and given under proposed framework. Through analysis, it can be concluded that the CFMA framework inherently possesses anti-eavesdropping capabilities, and the security capacity differences among different users are not significant. This is highly compatible with the requirements of the UAV-IoT system. Finally, the simulation results prove that the CFMA framework can meet the sensing data security transmission in the UAV-aided IoT system.

This paper investigates a secure beam-scanning framework for near-field integrated sensing and communication (ISAC) systems, driven by location division multiple access (LDMA). Specifically, an ISAC base station performs full-map robust beam-scanning within each transmission cycle, aiming to simultaneously detect potential eavesdroppers (Eves) and ensure secure communication for legitimate users (Bobs). Based on the Bobs’ channel state information (CSI) obtained at the cycle’s start and the estimated CSI of Eves sensed in the previous cycle, we formulate a robust optimization problem. This problem jointly optimizes the hybrid analog-digital precoding and time allocation for beam-scanning, with the objective of maximizing the worst-case average sum secrecy rate. To simplify the solution process, we first eliminate or relax the semi-infinite constraints caused by uncertain multipath channels from two perspectives: convex hull and bounded uncertainty. Subsequently, we design a near-field LDMA codebook in both azimuth and distance domains to construct ideal radar beampatterns for covering and partitioning the spatial scanning region. We also develop efficient analog precoders to significantly reduce computational complexity. Based on the convex hull model, we develop a low-complexity alternating optimization (AO) algorithm. In addition, for the bounded uncertainty model, we propose a semidefinite relaxation-based AO algorithm without requiring a rank-one constraint. Simulation results demonstrate that the proposed framework enables effective full-map Eves sensing while guaranteeing secure communication for Bobs. Moreover, the convex hull-based algorithm exhibits superior robustness and scalability compared to conventional bounded uncertainty approaches.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance both achievable rate and energy efficiency in next-generation wireless networks. This article investigates a novel energy-efficient UAV-RIS-assisted architecture for simultaneous wireless information and power transfer (SWIPT) in integrated ground–aerial–space networks, where a satellite cooperates with multiple UAV-RISs to provide downlink wireless energy transfer (WET) and support uplink wireless information transmission (WIT) for energy-constrained IoT devices in remote environments. We formulate an alternating iterative joint optimization problem (AIJOP) that aims to maximize the system sum achievable rate while ensuring causality of device energy via jointly optimizing UAV-RIS trajectories, RIS phase shift matrices, and satellite power allocation and time allocation between WET and WIT. The problem is highly non-convex due to the strong coupling among variables. To address this challenge, we propose a trajectory–phase–power–time alternating optimization algorithm (TPPTAOA), which decomposes the original problem into four tractable subproblems and solves them iteratively. Specifically, the UAV-RIS trajectories is first optimized via using a device scheduling and TSP-based path planning approach to solve the first subproblem, followed by the proposition of a two-stage heuristic phase optimization algorithm under fixed parameters to solve the second subproblem. Subsequently, the satellite power allocation is solved using a Lagrangian dual method, while the time allocation is optimized through a two-stage strategy combining grid-based coarse search with gradient-based refinement. Simulation results under various system settings verify the fast convergence, robustness, and superior performance of the proposed TPPTAOA, showing significant improvements in both energy efficiency and uplink achievable rate compared with benchmark schemes.

In this paper, channel estimation (CE) for up-link hybrid-field communications involving multiple Internet of Things (IoT) devices assisted by an active intelligent reflecting surface (IRS) is investigated. Firstly, to reduce the complexity of near-field (NF) channel modeling and estimation between IoT devices and active IRS, a sub-blocking strategy for active IRS is proposed. Specifically, the entire active IRS is divided into multiple smaller sub-blocks, so that IoT devices are located in the far-field (FF) region of each sub-block, while also being located in the NF region of the entire active IRS. This strategy significantly simplifies the channel model and reduces the parameter estimation dimension by decoupling the high-dimensional NF channel parameter space into low dimensional FF sub channels. Subsequently, the relationship between channel approximation error and CE error with respect to the number of sub-blocks is derived, and the optimal number of sub-blocks is solved based on the criterion of minimizing the total error. In addition, considering that the amplification capability of active IRS requires power consumption, a closed-form expression for the optimal power allocation factor is derived. To further reduce the pilot overhead, a lightweight CE algorithm based on convolutional autoencoder (CAE) and multi-head attention mechanism, called CAEformer, is designed. The Cramér-Rao lower bound is derived to evaluate the proposed algorithm’s performance. Finally, simulation results demonstrate the proposed CAEformer network significantly outperforms the conventional least square and minimum mean square error scheme in terms of estimation accuracy.

Next-generation wireless network should accomplish integrated sensing, communication, and computation (ISCC) capabilities. This paper proposes a novel covert transmission scheme based on active reconfigurable intelligent surface (RIS)-enabled full-duplex (FD) unmanned aerial vehicle (UAV)-ISCC framework, where the multi-functional UAV realizes simultaneous target sensing and uplink (UL) covert communication, as well as performing edge computing (EC) for users. To maximize the minimum covert transmission rate (CTR) among all UL users, UAV transmit beamforming and trajectory, RIS weights, power allocation and signal processing in a FD UL transmission system are jointly devised. To tackle the intractable non-convex problem, we leverage second order cone programming (SOCP), penalty-dual-decomposition (PDD) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the active RIS and UAV techniques into the optimization design, the covert transmission performance of ISCC systems are improved while ensuring a certain level of target sensing and EC performance.

In this paper, we propose a quantized penalty gradient (QPG) detection algorithm for massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs). To tackle the challenges of maximum likelihood (ML) detection under discrete constraints, we reformulate the detection problem into an unconstrained optimization by introducing two customized penalty functions that promote alignment between the estimated signals and target constellation set. Based on this, the QPG algorithm is developed to efficiently solve the resulting problem, achieving competitive detection performance with only second-order computational complexity. We further provide a theoretical analysis establishing the Lipschitz continuity of the objective function, which guarantees the monotonic descent property of QPG and ensures its convergence. Moreover, we prove that QPG efficiently finds the local minima with an accessible linear convergence rate, thus leading to an explicit trade-off between detection performance and computational complexity. Finally, simulation results confirm the significant performance gains of QPG over the conventional quantized detectors across various channel conditions, while maintaining low computational complexity.

Semantic-aware Vehicular Edge Computing (VEC) has emerged as a novel paradigm to significantly reduce transmission costs and edge resource consumption by offloading extracted task-driven semantic information. However, excessive semantic extraction may impose additional computational workload. In the face of unknown environmental dynamics, the semantic extraction ratio must be jointly designed with task offloading for resource-constrained VEC. To this end, we conceive a multiple-objective (MO) semantic-aware task offloading framework for VEC by jointly optimizing semantic extraction ratio, transmit power and task scheduling strategies aimed at minimizing both long-term age-of-information (AoI) and energy consumption while guaranteeing queue stability. Subsequently, we propose a Lyapunov-guided multi-objective reinforcement learning (MORL)-based semantic-aware joint task scheduling and resource allocation (SJTSRA) solution. Specifically, Lyapunov optimization method is first leveraged to transform the original problem into a multi-objective Markov decision process (MOMDP). Then, the concave-augmented Pareto Q-learning (CAPQL) algorithm is employed to find Pareto optimal solutions through introducing uniform weight sampling and entropy regularization, where the convergence can be guaranteed theoretically. Simulation results show that the proposed solution achieves the closest approximation to the Pareto front with the highest hypervolume, and superior energy-AoI trade-offs across varying environment parameters compared to all benchmarks.

This paper investigates uplink transmission rate enhancement in space-air-ground integrated networks (SAGIN) by jointly designing precoders for a multi-antenna ground user and an unmanned aerial vehicle (UAV). Assuming a stationary relative position between the UAV and the satellite, we propose two joint precoding designs to optimize the uplink transmission rate while considering the practical finite signaling. We introduce an alternating iteration optimization approach (AIOA) when accurate channel state information (CSI) of the Rician link from the ground user and UAV is available. Additionally, we account for the statistical CSI induced by multi-path effects in various terrestrial environments, and derive a new closed-form expression for the uplink transmission rate. Building on this, a convex optimization framework is formulated by vectoring the optimization matrix and introducing auxiliary variables to tackle the non-convexity of the problem. Then, the AIOA is further adopted to jointly optimize the ground user and UAV precoders, significantly reducing computational complexity. Simulation results confirm the efficiency of the proposed AIOAs improving uplink transmission rates in SAGIN.

Pinching antennas are a promising technology for enhancing the performance of future indoor communication systems by leveraging spatial degrees of freedom. This paper pioneers the application of pinching antenna arrays in integrated sensing and communication (ISAC) systems and investigates dynamic array partitioning strategies. To maximize the secrecy sum rate (SSR), a partitioned array optimization problem under binary constraints is formulated, while satisfying sensing performance requirements and transmit power limitations. Specifically, the antenna partitioning constraints are modeled as minimum and maximum numbers of transmit antennas, along with binary constraints determining whether each antenna element functions in transmit or receive mode. To solve the non-convex optimization problem, a beamforming algorithm integrating semidefinite relaxation, generalized Rayleigh quotient, and minimum mean square error is proposed. Then, an element-wise iterative optimization method and a deep Q-network (DQN)-based partitioning approach are respectively developed to optimize the array configuration, thereby enhancing security performance under guaranteed sensing constraints. Simulation results demonstrate that the DQN-based approach outperforms the conventional iterative optimization method. In terms of security performance, the pinching antenna array can achieve a 69.70% reduction in the number of antennas and a 30.16% saving in transmit power compared to conventional fixed-position antenna (FPA) systems. Moreover, the pinching antenna system attains a 35.72% improvement in SSR performance, surpassing traditional FPA configurations.

The advancement of wireless networks has spurred an increasing demand for high-quality maritime communication services. This study presents an innovative unicast-multicast access and backhaul maritime communication network (UMABMCN), in which a high-altitude platform (HAP) provides HAP-to-vessel (H2V) unicast services to vessels and backhaul support to unmanned aerial vehicles (UAVs) through HAP-to-UAV (H2U) links. Additionally, multiple UAVs are deployed to deliver UAV-to-vessel (U2V) multicast transmission services to vessels. Specifically, we formulate a HAP-UAV-assisted unicast-multicast cooperation multi-objective optimization problem (UMCMOP) aimed at maximizing the sum achievable rate of base stations (BS)-to-vessel (B2V), maximizing the sum backhaul rate of H2U, and minimizing the energy consumption of UAVs via jointly optimizing communication connection between BSs and vessels, power allocations of UAVs, along with the placement of UAVs. The formulated UMCMOP is a mixed integer non-linear programming (MINLP) problem. To address this, we propose an enhanced multi-objective multi-verse optimization (EMOMVO-CGD) algorithm, which integrates achaos probability operator,gray wolf exploitation operator, anddiscrete update operator. To further validate the performance of EMOMVO-CGD, a joint communication connection, power allocation and placement optimization (JCCPAPO) method is proposed. Simulation results demonstrate that the two proposed algorithms outperform benchmark strategies in optimizing the aforementioned objectives.

Cooperated with simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and unmanned aerial vehicle (UAV), a non-orthogonal multiple access (NOMA) covert communication network is conceived. However, open channels are more vulnerable to eavesdropping byWardens, overlying that the channel state information (CSI) may be unstable with UAV’s time-varying deployments. In this paper, a deep reinforcement learning (DRL)-based and instantaneous channel-deducted framework is investigated for resolving the cutting-edge maximization problem of covert communication rate, subjected to the UAV flight, QoS requirement, and communication covertness. Given the channels instability caused by time-varying UAV flight, we design a channel deduction network by integrating complex-domain multi-layer perceptron (CMixer) and recurrence-based bidirectional long-short term memory (BiLSTM) to exploit the nonlinear correlations of channels in time, spatial location, and antenna domains. Relying on the states with deducted channels, the Twin Delayed Deep Deterministic policy gradient (TD3) as a proactive and policy-based DRL algorithm is used to iteratively train an agent responsible for adaptive adjusting UAV deployment and STAR-RIS beamforming. Simulation results demonstrate the effectiveness of the proposed channel deduction scheme, covert communication mechanism, and their synthesis.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance indoor wireless communication and sensing performance. However, the construction of reliable received signal strength (RSS)-based fingerprint databases for RIS-assisted indoor positioning remains an open challenge due to the lack of realistic and spatially consistent channel modeling methods. In this paper, we propose a novel method with open-source code for generating RIS-assisted RSS fingerprint databases. Our method captures the complex RIS-assisted multipath behaviors by extended cluster-based channel modeling and the physical and electromagnetic properties of RIS and transmitter (Tx). And the spatial consistency is incorporated when simulating the fingerprint data collection across neighboring positions. Moreover, an effective sorting algorithm is proposed to solve the online synchronization issue, a closed-form RIS phase configuration strategy is proposed to improve the localization accuracy, and the modeling method of mutual coupling (MC) effect is provided. Extensive simulations are conducted to evaluate the fingerprint database generated by the proposed method. And the positioning performance on the database using different algorithms is analyzed, providing valuable insights for the system design.

Future industrial 6G applications will impose stringent requirements on ultra-reliable low-latency communications (URLLC) and precision sensing enabled by integrated sensing and communication (ISAC) techniques, motivating a comprehensive study of the communication–sensing (C–S) trade-off under finite blocklength transmission. Therefore, this paper investigates the fundamental C–S performance limits in a network-assisted full-duplex (NAFD) cell-free ISAC system with URLLC users. To address the theoretical gap in the finite blocklength regime, closed-form upper-bound expressions are derived for key communication metrics, including transmission delay and decoding error probability (DEP), and a Cramér–Rao lower bound (CRLB) framework is established for multi-static sensing. Furthermore, to explicitly characterize the C–S trade-off, the ISAC network availability is evaluated and the Pareto frontier is obtained using the non-dominated sorting genetic algorithm II (NSGA-II). The results demonstrate that increasing the blocklength improves sensing accuracy at the cost of higher communication latency. To address this inherent conflict, this study proposes a DDQN-based finite blocklength optimization (FBLO) algorithm that performs blocklength selection under URLLC and sensing quality-of-service requirements to achieve a favorable C–S trade-off. Simulation results validate that the proposed algorithm achieves near-optimal performance with reduced computational overhead.

In this work, we focus on developing near-field beamforming schemes for covert wireless communications to maximize the communication quality while ensuring communication covertness, where three different antenna architectures are considered. Specifically, we first analyze the monotonicities of the throughput and the minimum detection error probability with respect to the signal-to-noise ratio (SNR) at a legitimate receiver and a warden. Then, the formulated optimization problem is transformed into a semi-definite programming (SDP) problem via vectorizing the matrix. Lastly, a maximum signal-to-leakage ratio (MSLR) scheme with a low complexity is proposed. Our results indicate that the proposed near-field beamforming schemes facilitate covert communication in both the distance and angle dimensions. The two hybrid antenna architectures lead to a slight performance loss, but significantly reduce the circuit budget. Meanwhile, the low-complexity algorithm greatly reduces the algorithm complexity, although it exhibits a noticeable disadvantage in its covert transmission performance. Furthermore, our results indicate that the designed near-field beamforming strategies are more beneficial in meeting the covertness constraints, highlighting a feasibility of covert communications in the near-field region.

This paper proposes a novel Intelligent Reflecting Surface (IRS)-assisted interweave Cognitive Internet of Vehicles (CIoV) network under malicious jamming attacks, where the IRS enhances communication performance by establishing additional links. In order to maximize the sum transmission rate of Vehicle-to-Infrastructure (V2I) links, we propose an optimization problem that jointly optimizes wireless resource allocation, such as spectrum and transmit power for Vehicle Users (VUs) and IRS phase shift. Because this problem is non-convex and complicated, we further propose a Heterogeneous Multi-agent Transformer-enhanced Dueling Double Deep Q-Network (HMA-TD3QN) based resource allocation method, where VUs and Secondary Base Station (SBS) act as distinct heterogeneous agents can independently perform resource allocation and phase shift optimization. The Transformer neural network architecture can better adapt to long sequence input states and extract relevant features from complex input states through the attention mechanism. Simulation results indicate that the proposed HMA-TD3QN method achieves improvements of 24.42%, 20.79%, and 22.25% over the basic HMA-DQN under three different jamming strategies, highlighting the effectiveness of IRS technology in enhancing the Quality of Service (QoS) and jamming resilience of CIoV network.

The Unmanned Aerial Vehicles (UAVs) communication faces challenges arising from scarce spectrum resources and malicious jamming. This paper proposes a spectrum waterfall (SW)-assisted multi-agent anti-jamming framework for UAV swarms by designing joint resource allocation and trajectory optimization (JRATO) strategies. By formulating the problem as a decentralized partially observable parameterized-action Markov Decision Process (Dec-POPAMDP), we first employ a self-attention-based convolutional neural network (CNN) to extract spatiotemporal SW knowledge, and then propose a multiagent hybrid Proximal Policy Optimization (MA-HPPO) based anti-jamming scheme to maximize the long-term utility-cost trade-off. Simulation results show that the proposed scheme outperforms the benchmarks in terms of both the convergence and the long-term utility-cost trade-off, while achieving higher success rate with lower energy consumption with varying numbers of channels.

In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom) enabled integrated sensing, communication and computation (ISCC) systems. We consider the joint design of transmit beamforming at the BS and the reflecting coefficients at the RIS as well as the computation resource allocation of each user. The optimization problem for the max computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the alternative optimization (OA) and the alternating direction method of multipliers (ADMM) algorithm is developed. Furthermore, a more computationally efficient approach is introduced, which utilizes transmit beamforming based on an accelerated primal gradient (APG) method. Furthermore, the approximation principle is proposed to transform non-convex constraints in the optimization of the reflection coefficients at RISs. Simulation results show that introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

In extremely low signal-to-noise ratio (SNR) region, the useful features of the signal are weakened by higher-power noise, making it difficult for conventional direction-of-arrival (DOA) estimation methods to adequately exploit and extract the low-SNR signal features. Thus, a generative adversarial network (GAN) is presented to learn the underlying features and complex distributions of high-SNR covariance matrices. The introduced GAN establishes a mapping between low-SNR and high-SNR covariance matrices, thereby generating first-rate high-SNR covariance matrices that closely resemble real high-SNR matrices. Also, it effectively captures signal features that are overwhelmed by excessive noise power. Additionally, to improve the performance of convolutional neural network (CNN)-based DOA estimation models in medium-to-high SNR ranges, a deep residual attention network (DRANet) is designed to significantly enhance DOA estimation accuracy in such SNR region. By integrating residual and attention modules, the network effectively filters key features. This enhances feature learning and adaptability, allowing it to capture DOA-related features more proficiently. The experimental results indicate that the developed GAN-DRANet approach can approach the CRLB in the extremely low SNR range and improves the estimation resolution limits of the other two DL-based methods, DNN and CNN, in medium to high SNR conditions.

In future networks, the integrated sensing, communication and computation (ISCC) has gradually become a research hotspot. In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom)-aided ISCC system. We consider the joint design of transmit beamforming at BS and the reflecting coefficients at RIS as well as the computation resource allocation of each user. The optimization problem for the max-min computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the block coordinate descent (BCD) algorithm is utilized to tackle the joint optimization problem. We propose the penalty function-based successive convex approximation (SCA) method to optimize the reflecting coefficients and the majorization-minimization (MM) framework to design the transmit beamforming, respectively. In addition, considering the high complexity of the proposed SCA based algorithm, we design a low-complexity beamforming and reflection coefficient scheme for a special case of single target scenario. Simulation results show that the introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

The flexibility and maneuverability of unmanned aerial vehicles (UAVs) lend themselves to tracking users and operating as an aerial base station carrying out communication enhancement functionality. A core challenge neglected by most existing works is that the true-but-unknown obstacles can jeopardize UAV flight security and shadow its communication links with users, resulting in poor achievable rate and high collision risks. In this paper, a deep-reinforcement-learning (DRL)-based UAV target tracking and digital-twin (DT)-assisted collision avoidance method is proposed to optimize UAV’s communication performance while tracking moving users. Toward this end, Twin Delayed Deep Deterministic policy gradient (TD3) as a novel and policy-based DRL algorithm is used to construct an agent responsible for adaptive deciding UAV flying control actions. To efficiently detect unknown obstacles in a flight environment, an orthogonal frequency division multiple (OFDM)-based integrated sensing and communication (ISAC) system is investigated, endowing UAV’s agent with real-time obstacle distance. Finally, we present a DT obstacle model construction mechanism and integrate it with TD3 agent training. The extensive simulations demonstrate the reward convergence of the TD3 algorithm and the communication improvement with stable user tracking and reliable collision avoidance, compared with conventional approaches.

In this paper, we propose a novel covert transmission scheme for reconfigurable holographic surface (RHS)-aided integrated sensing and communication (ISAC) system with imperfect channel state information (CSI). Considering full and partial channel uncertainty models, we jointly devise the digital and holographic beamforming along with the receive filter to maximize the worst-case and outage-constrained achievable rate (AR) of communication users while guaranteeing the sensing capability and covertness requirement. The resulting optimization problems are difficult to solve owing to the non-convexity caused by the semi-infinite constraints (SICs) and the coupled design variables. After approximating the worst-case and outage constraints by exploiting the S-procedure, successive convex approximation (SCA) and Bernstein-type inequality, we propose a secure solution that efficiently optimizes all variables by using convex optimization methods. To understand the proposed algorithm better, both the convergence and computational complexity are discussed. Simulation results show that by incorporating the RHS technique into the optimization design, the covert transmission performance of ISAC systems are improved while ensuring a certain level of sensing performance.

Cognitive Internet of Vehicles (CIoV) adds the cognitive engine based on traditional Internet of Vehicles (IoV), which can improve spectrum utilization. However, spectrum sensing data falsification (SSDF) attacks pose a threat to CIoV network security. To ensure the full utilization of spectrum resources and protect primary users transmission, this article combines blockchain with CIoV to defend against SSDF attacks in the presence of vehicle users (VUs) entering and leaving the network. Specifically, this article introduces a virtual currency called Sencoins serve as credential for VUs to purchase transmission shares. And this article proposes a reward and punishment mechanism and a hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) mining model to thwart the motivation of the VUs to launch SSDF attacks. On this basis, this article investigates the dynamics of SSDF attack strategy choice of VUs, and uses the largest Lyapunov exponent (LLE) to determine the critical value of Sencoins that avoids the system to exhibit chaotic behavior. To describe the uncertainty of the population proportion of VUs that choose different attack strategies due to high-speed movement and the VUs entering and leaving the CIoV network, this article introduces Gaussian white noise into the replication dynamics equation and builds the Itô stochastic evolutionary game model, and solves it according to the stability judgment theorem of stochastic differential equations and stochastic Taylor expansion. Finally, simulation results verify that the proposed method can quickly and effectively thwart SSDF attacks in the CIoV network. And compared with traditional methods, the proposed method can improve the efficiency of defending against SSDF attacks by 567% and the average throughput by 25%.

The high-dynamic Doppler frequency offset presents great challenges to communications in the low-Earth orbit (LEO) satellite Internet of Things (IoT) scenarios, including the high-complexity receiver synchronization algorithm, frequency tracking capability, and high-receiver sensitivity. In this article, we propose an effective communication system architecture based on chirp modulation for LEO satellite IoT with reference to LoRa system. Our approach introduces an improved chirp modulation based on low-rate optimization (LRO) with specified frequency interval and a CRC-assisted decoding scheme. Simulation results show that the proposed optimal LRO factor can improve the spectral efficiency. Furthermore, we propose a low-complexity synchronization algorithm and LRO-based frequency tracking strategy. The simulation results confirm the feasibility of the proposed communication system for LEO satellite IoT, which demonstrates robust performance under high-dynamic Doppler frequency offsets.

In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.

Nowadays, covert communication is envisioned as a promising and secure method of delivering private information. However, higher bit error rates, limited data rates, and vulnerability to advanced machine learning detection methods significantly challenge the application of covert communication. In this paper, we propose a multiple carrier index keying orthogonal frequency division multiplexing (MCIK-OFDM) based covert communication system aided by a chaotic modulation scheme to improve covert data rate and covertness. First, we propose a covert information embedding method by dynamically selecting the activation or deactivation of a subcarrier to embed covert bits according to a previously negotiated covert key between the transmitter and receiver. Then, the chaotic modulation scheme is developed to mask transmitted signals with generated chaotic signals. Moreover, we propose an adversarial machine learning-based (AML) perturbation algorithm to resist the eavesdropper’s detection of covert signals. Furthermore, the closed-form bit error rate (BER) and the achievable covert rate of the proposed covert communication system are derived. Numerical and simulation results demonstrate that the BER of the proposed MCIK-OFDM-based hybrid chaotic covert communication system is much lower than that of conventional chaotic communication systems. In addition, the proposed AML perturbation algorithm can more effectively protect covert communication from being detected by supervised and unsupervised machine learning methods compared to traditional algorithms.

Unlike the conventional fully-passive and fully-active reconfigurable intelligent surfaces (RISs), a hybrid RIS consisting of active and passive reflection units has recently been concerned, which can exploit their integrated advantages to alleviate the RIS-induced path loss. In this paper, we investigate a novel security strategy where the multiple hybrid RIS-aided integrated sensing and communication (ISAC) system communicates with downlink users and senses an extended target synchronously. Assuming imperfectly known channel state information (CSI) for the eavesdropping target, we consider the joint design of the transmit signal and receive filter bank of the base station (BS), the receive beamformers of all users and the discrete reflection coefficients (DRC) of the multiple hybrid RIS. An optimization problem is formulated for maximizing the worst-case sensing signal-to-interference-plus-noise-ratio (SINR) subject to secure communication and system power budget constraints. To address this non-convex problem, we leverage generalized fractional programming (GFP) and penalty-dual-decomposition (PDD), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the multiple hybrid RIS into the optimization design, the extended target detection and secure transmission performance of ISAC systems are improved over the state-of-the-art RIS-aided ISAC approaches.

This work addresses the design problem of the fault detection observer (FDO) based on dynamic event-triggered mechanism for Markov jump systems under denial-of-service (DoS) attacks. The concept of limited energy for attackers is employed to characterize the property of nonperiodic DoS attacks. A dynamic event-triggered mechanism is introduced to save the system's communication resources. The $H_{\infty }/H_{-}$ index is incorporated to ensure that the designed FDO possesses both robustness against disturbances and sensitivity to faults. After obtaining a set of nonlinear inequalities using Lyapunov functional techniques, a simulated annealing algorithm is employed to assist in solving, ensuring not only the discovery of global optimization solutions but also obtaining satisfactory parameters for the dynamic event-triggered mechanism. Finally, the effectiveness of the designed FDO is illustrated by an example of a vertical take-off and landing vehicle dynamical system.

In this paper, authentication for mobile radio frequency identification (RFID) systems with low-cost tags is investigated. To this end, an adaptive modulus (AM) encryption algorithm is first proposed. To further enhance security without requiring additional storage for new key matrices, a self-updating encryption order (SUEO) algorithm is designed. Furthermore, a diagonal block local transpose key matrix (DBLTKM) encryption algorithm is presented, which effectively expands the feasible domain of the key space. Building upon these three algorithms, a novel joint AM-SUEO-DBLTKM encryption algorithm is constructed. Making full use of the strengths of the proposed joint algorithm, a two-way RFID authentication protocol, named AM-SUEO-DBLTKM-RFID, is proposed specifically for mobile RFID systems. In addition, the Burrows-Abadi-Needham (BAN) logic and security analysis indicate that the proposed AM-SUEO-DBLTKM-RFID protocol can effectively combat various typical attacks. Numerical results demonstrate that the proposed AM-SUEO-DBLTKM algorithm can save 99.59% of tag storage over traditional algorithms. Finally, the proposed AM-SUEO-DBLTKM-RFID protocol achieves both low computational complexity and low storage overhead, making it well-suited for deployment in resource-constrained, low-cost RFID tags.

Decentralized federated learning (DFL) has the potential to address the issue of the aggregator’s single-point failure. However, in the absence of centralized coordination, DFL systems are vulnerable to malicious behaviors from clients. In this article, we propose a blockchain-based DFL framework to regulate the behaviors of enterprise clients in the context of cross-enterprise modeling. To be specific, we first design a novel mechanism for model plagiarism detection, wherein pseudonoise sequences are incorporated into local models, enabling to identify enterprises’ plagiarism behaviors. Then, we propose a model aggregation algorithm to improve the learning performance of the global model. Furthermore, we develop a plagiarism-aware proof-of-work consensus mechanism by adaptively adjusting enterprises’ mining difficulty based on their plagiarism records, which can efficiently demotivate them from engaging in plagiarism. The experimental results based on industrial datasets, including CWRU, PU, Milan, PV, NEU-CLS, and X-SDD, demonstrate that the proposed framework can achieve approximately 4%, 7%, and 12% of the learning accuracy improvement in the scenarios of 20%, 40%, and 60% plagiarism rates, respectively, compared to the conventional DFL system.

Vehicle-to-everything (V2X) applications are usually powered by vehicular batteries and thus are power limited in general. Reconfigurable intelligent surfaces (RISs) are capable of improving the spectral efficiency and conserving energy of the wireless communications, due to the planar array architecture of which is superior beamforming gain and energy-efficient. In this paper, we study a novel design scheme where a double-RIS-enabled integrated sensing and communication (ISAC) system in vehicular network performs both a single target sensing and multi-user communications synchronously. Specifically, two transmit power budget minimization problems are formulated based on Cramér-Rao bound (CRB)-based framework under the known target location model, and radar signal-to-noise ratio (SNR)-related framework under the uncertain target location model, respectively. For the former, we propose an efficient solver based on alternative direction method of multipliers (ADMM) technique to obtain high-quality solutions for transmit beamforming and phase shifts. For the latter, an efficient algorithm based on penalty-dual-decomposition (PDD) and second order cone programming (SOCP) approaches is proposed. Simulation results demonstrate the effectiveness of two proposed algorithms and also show the superiority of our developed schemes over state-of-the-art benchmark ISAC schemes.

Existing works mainly rely on the far-field planar-wave-based channel model to assess the performance of reconfigurable intelligent surface (RIS)-enabled wireless communication systems. However, when the transmitter and receiver are in near-field ranges, the investigation of the channel statistics based on the planar-wave-based model will result in relatively low computing accuracy. To tackle this challenge, we initially develop an analytical framework for sub-array partitioning. This framework divides the large-scale RIS array into multiple sub-arrays, effectively reducing modeling complexity while maintaining acceptable accuracy. Then, we develop a beam domain channel model based on the proposed sub-array partition framework for large-scale RIS-enabled unmanned aerial vehicle (UAV)-to-vehicle communication systems, which can be used to efficiently capture the sparse features of RIS-enabled UAV-to-vehicle channels in both near-field and far-field ranges. Furthermore, some important propagation characteristics of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (FCFs), channel capacities, and path loss statistics with respect to the different physical features of the RIS array and non-stationary properties of the channel model are derived and analyzed. Finally, simulation results are provided to demonstrate that the proposed framework is helpful to achieve a good tradeoff between the modeling complexity and accuracy for investigating the channel propagation characteristics, and therefore providing highly-efficient communications in RIS-enabled air-ground wireless networks.

In this paper, we propose a novel architecture for a multi-layer active-passive cascade transmitting reconfigurable intelligent surface (RIS)-aided receiver. We aim at enhancing the scalability of antenna dimensions and energy efficiency for user equipment (UE), as well as improving the amplitude freedom of antenna gain. This architecture integrates reflecting RIS for applications in both jamming and anti-jamming scenarios. We formulate a problem to maximize the worst-case spectral efficiency (SE) of the UE, based on imperfect channel state information (CSI) of the malicious device, thereby ensuring the SE of our UE while disrupting the signal reception of the illegal UE. To address the inherently non-convex nature of the formulated problem, we propose an alternating optimization framework, which decomposes the main problem into several subproblems. Specifically, we uniformly discretize the uncertain domains to obtain robust CSI, allowing us to determine an optimal receiver vector. To balance computational complexity and performance, we propose solutions for the subproblems of base station beamforming and coefficients with different patterns of RIS. Furthermore, to effectively disrupt malicious inter-device communication while avoiding detection and localization, we develop a collaborative interference pattern incorporating silent interference and non-interference protocols. Importantly, the pattern carefully balances performance with the overhead of CSI acquisition. Finally, simulation results validate the efficiency of the proposed algorithms, demonstrating that the proposed architecture achieves better jamming and anti-jamming performance.

Vehicular edge computing (VEC) has driven the proliferation of computation-intensive and delay-sensitive vehicular services by deploying computing and energy resources at the edge. However, the exploitation of edge resources faces challenges due to unpredictable environmental dynamics and partial observability. To this end, this paper investigates the computing energy efficiency (CEE) problem in twin-timescale VEC scenarios by dynamically adjusting the offloading policy. Based upon modeling the problem as a decentralized partially observable Markov decision process (Dec-POMDP), a pair of non-cooperative and cooperative offloading solutions are proposed relying on multi-agent reinforcement learning (MARL), respectively. Specifically, the non-cooperative solution employs multi-agent independent proximal policy optimization (IPPO) to enable vehicular user equipments (VUEs) to learn their policies in a fully distributed manner without any information sharing. By contrast, the cooperative solution combines the multi-agent shared PPO with graph attention networks (MAPPO-GAT), where the relationship among agents is learned cooperatively and the historical learning experience is shared. Additionally, we compare the computational complexity and analyze the convergence. Simulation results show that in terms of the trade-off between offloading delay and offloading energy consumption, the proposed cooperative solution is superior to the non-cooperative counterpart with the cost of moderate training overhead for cooperative learning.

In this work, we consider an intelligent reflecting surface (IRS)-assisted covert wireless communication system, in which the location of a warden Willie follows a binomial point process (BPP) distribution within a disk centered at O with a radius of R and the warden is outside a safety zone centered at C with a radius of r. The location of the safety zone can be anywhere (inside, outside, or having intersections) relative to the disk. We first analyze the detection performance of Willie for the cases with known channel state information (CSI) and unknown CSI, respectively, based on which we determine Willie's minimum detection error rate. Then, considering the geometric randomness of Willie's position inside the disk but outside the safety zone, the average minimum detection error rate is determined and adopted in the covertness constraint in the subsequent covert system design. Our examination first shows that the covert communication rate generally increases with R. However, more interestingly, whether the covert communication rate increases or decreases with r generally depends on the relative locations of the disk, the safety zone, and Alice. For example, when both the safety zone and Alice are on the same side of the disk center O, the covert rate increases with r, and vice versa.

In the face of increasingly computing-intensive and delay-sensitive vehicular applications, vehicular edge computing (VEC) has become a promising computing paradigm by deploying computing resources at the edge. This paper investigates an age of information (AoI)-aware vehicular edge offloading problem by dynamically adjusting the edge offloading ratio and selecting the VEC server, taking into account the computing energy efficiency (CEE). To adapt to the time-varying network topology of VEC, we propose a multi-agent cooperative edge offloading solution relying on actor-attention-critic framework, where each vehicular user equipment (VUE) employs an attention mechanism to regulate its attention to other VUEs, facilitating selective focus on important information to enhance policy learning. The simulation results show that the proposed solution can achieve a more compelling trade-off between AoI and CEE compared with the baseline solutions.

In this paper, to enhance the communication quality of cognitive unmanned aerial vehicle networks (CUAVNs), we investigate the intelligent reflecting surface (IRS)-assisted CUAVNs, where a leading UAV (LUAV) is deployed for sensing spectrum and a group of following UAVs (FUAVs) transmit data to LUAV with the aid of IRS. Our objective is to maximize the achievable throughput of CUAVNs by jointly optimizing sensing duration, IRS passive beamforming and LUAV’s three-dimensional (3D) location, where LUAV’s 3D location is restricted by IRS, FUAVs and primary user. For IRS-assisted single FUAV case, the intractable non-convex optimization problem is resolved into three subproblems, which are solved by utilizing the bisection search method, the closed-form expression of the optimal IRS phase shift matrix and the successive convex approximation method, respectively. Finally, an efficient alternating optimization algorithm is developed to obtain a high-quality suboptimal solution. By exploiting the solutions of single FUAV, we further propose the throughput weighted sum (TWS) algorithm to solve the intricate non-convex problem in IRS-assisted multiple FUAVs case. To further reduce the complexity of TWS, the low-complexity location weighted sum (LWS) algorithm is proposed. Numerical results show that compared to the scheme without IRS assistance, the achievable throughput increases about 102% with the proposed single FUAV scheme, and over 88% with the proposed TWS-based multiple FUAVs scheme. Moreover, the performance gap between the low-complexity LWS and the TWS is less than 7%.

Federated learning (FL) based on the centralized design faces both challenges regarding the trust issue and a single point of failure. To alleviate these issues, blockchain-aided decentralized FL (BDFL) introduces the decentralized network architecture into the FL training process, which can effectively overcome the defects of centralized architecture. However, deploying BDFL in wireless networks usually encounters challenges, such as limited bandwidth, computing power, and energy consumption. Driven by these considerations, a dynamic stochastic optimization problem is formulated to minimize the average training delay by jointly optimizing the resource allocation and client selection under the constraints of limited energy budget and client participation. We solve the long-term mixed integer nonlinear programming problem by employing the tool of Lyapunov optimization and thereby propose the dynamic resource allocation and client scheduling BDFL (DRC-BDFL) algorithm. Furthermore, we analyse the learning performance of DRC-BDFL and derive an upper bound for convergence regarding the global loss function. Extensive experiments conducted on the SVHN and CIFAR-10 data sets demonstrate that the DRC-BDFL achieves comparable accuracy to the baseline algorithms while significantly reducing the training delay by 9.24% and 12.47%, respectively.

Cognitive radio (CR) is regarded as the key technology of the 6th-Generation (6G) wireless network. Because 6G CR networks are anticipated to offer worldwide coverage, increase cost efficiency, enhance spectrum utilization, and improve device intelligence and network safety. This article studies the secrecy communication in an energy-harvesting (EH)-enabled Cognitive Internet of Things (EH-CIoT) network with a cooperative jammer. The secondary transmitters (STs) and the jammer first harvest the energy from the received radio frequency (RF) signals in the EH phase. Then, in the subsequent wireless information transfer (WIT) phase, the STs transmit secrecy information to their intended receivers in the presence of eavesdroppers while the jammer sends the jamming signal to confuse the eavesdroppers. To evaluate the system secrecy performance, we derive the instantaneous secrecy rate and the closed-form expression of secrecy outage probability (SOP). Furthermore, we propose a deep reinforcement learning (DRL)-based framework for the joint EH time and transmission power allocation problems. Specifically, a pair of ST and jammer over each time block is modeled as an agent which is dynamically interacting with the environment by the state, action, and reward mechanisms. To better find the optimal solutions to the proposed problems, the long short-term memory (LSTM) network and the generative adversarial networks (GANs) are combined with the classical DRL algorithm. The simulation results show that our proposed method is highly effective in maximizing the secrecy rate while minimizing the SOP compared with other existing schemes.

The smart grid is expected to be integrated with advanced communication and control technologies to enhance its efficiency and reliability, enabling bidirectional information and energy exchanges between power providers and consumers. Considering the substantial data generated by the smart grid, mobile-edge computing (MEC) is introduced to address the need for considerable computing capacity. Limited attention has been directed toward exploring the application of MEC in smart grid scenarios. In this article, we innovatively consider maximizing the sum of computing rate and weighted energy trading benefit for a scenario with the distributed energy resource aggregation, smart grid, and MEC. We formulate the considered utility maximization as a mixed integer nonlinear programming (MINLP) problem. In order to solve this problem, we break it down into two distinct subproblems: 1) the binary offloading decision problem and 2) the energy allocation problem. We propose a deep reinforcement learning (DRL)-based framework to determine the offloading decision and design an optimization algorithm for the energy allocation problem. The simulation results indicate that the proposed algorithm achieves approximately 98% of the traversal algorithm’s performance with only one-thousandth of its processing time.

In this article, channel estimation (CE) of an active intelligent reflecting surface (IRS) aided uplink Internet of Things (IoT) network is investigated. First, the least square (LS) estimators for the direct channel and the cascaded channel are presented, respectively. The corresponding mean-square errors (MSEs) of channel estimators are derived. Subsequently, in order to evaluate the influence of adjusting the transmit power at the IoT devices or the reflected power at the active IRS on Sum-MSE performance, two situations are considered. In the first case, under the total power sum constraint of the IoT devices and active IRS, the closed-form expression of the optimal power allocation (PA) factor is derived. In the second case, when the transmit power at the IoT devices is fixed, there exists an optimal reflective power at active IRS. To further improve the estimation performance, the convolutional neural network (CNN)-based direct CE (CDCE) algorithm and the CNN-based cascaded CE (CCCE) algorithm are designed. Finally, simulation results demonstrate the existence of an optimal PA strategy that minimizes the Sum-MSE, and further validate the superiority of the proposed CDCE/CCCE algorithms over their respective traditional LS and minimum MSE (MMSE) baselines.

This paper presents a comprehensive framework for jointly analyzing the angle estimation error and designing a three-dimensional (3D) positioning algorithm for an Internet of Things (IoT) millimeter wave (mmWave) positioning system. Initially, the azimuth and elevation angles of arrival (AoAs) at the anchors are estimated by applying the two-dimensional discrete Fourier transform (2D-DFT) algorithm. The angle estimation error is then analyzed in terms of probability density functions (PDF) by utilizing the properties of the 2D-DFT algorithm and employing challenging derivations and linear approximations. The analysis reveals that the resulting angle estimation error is non-Gaussian, distinguishing it from previous studies. Next, the complex expression of the PDF for the AoA estimation error is simplified using the first-order linear approximation of triangle functions. Subsequently, a complex expression for the variance is derived based on the obtained PDF. Specifically, the variance for the azimuth estimation error is integrated separately according to the different non-zero intervals of the obtained PDF. Additionally, the closed-form expressions of the variances are formulated using generalized hypergeometric series. Finally, the two-stage weighted least square (TSWLS) algorithm is employed to estimate the 3D position of the mobile user (MU) using the estimated AoAs and the obtained non-Gaussian variance. Extensive simulation results confirm the non-Gaussian nature of the derived angle estimation error and demonstrate the superiority of the proposed framework.

This paper develops an optimal design for device scheduling, alignment coefficient, and aggregation rounds within a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system considering a constrained sum power budget. In DP-OTA-FedAvg, gradients are aligned using an alignment coefficient and then aggregated over the air, utilizing channel noise to ensure participant privacy. This study highlights two critical tradeoffs in aligned over-the-air federated learning (OTA-FL) systems with limited power and privacy budgets. Firstly, it reveals the tradeoff between the number of scheduled devices and the alignment coefficient. Secondly, it investigates the balance between aggregation distortion and local training error while adhering to the sum power constraint. Specifically, we measure privacy using differential privacy (DP) and perform convergence analyses for both convex and non-convex loss functions. These analyses provide insights into how device scheduling, the alignment coefficient, and the number of global aggregations affect both privacy preservation and the learning process. Building on these analytical results, we formulate an optimization problem aimed at minimizing the optimality gap of DP-OTA-FedAvg under power and privacy constraints. By specifying the number of aggregation rounds, we derive a closed-form expression describing the relationship between the alignment coefficient and the number of scheduled devices. We then tackle the problem through iterative optimization of scheduling and aggregation rounds. The effectiveness of the proposed policies is verified through simulations, and the performance advantage is particularly pronounced in scenarios where devices have poor channel conditions and limited sum-power budgets.

In this work, we consider a covert communication scenario, where a transmitter Alice communicates to a receiver Bob with the aid of a probabilistic and uninformed jammer against an adversary warden’s detection. The transmission status and power of the jammer are random and follow some priori probabilities. We first analyze the warden’s detection performance as a function of the jammer’s transmission probability, transmit power distribution, and Alice’s transmit power. We then maximize the covert throughput from Alice to Bob subject to a covertness constraint, by designing the covert communication strategies from three different perspectives: Alice’s perspective, the jammer’s perspective, and the global perspective. Our analysis reveals that the minimum jamming power should not always be zero in the probabilistic jamming strategy, which is different from that in the continuous jamming strategy presented in the literature. In addition, we prove that the minimum jamming power should be the same as Alice’s covert transmit power, depending on the covertness and average jamming power constraints. Furthermore, our results show that the probabilistic jamming can outperform the continuous jamming in terms of achieving a higher covert throughput under the same covertness and average jamming power constraints.

Undesired jamming launched by malicious jammers can attack authorized communications, which is viewed as one of the critical challenges in vehicular networks. In this paper, in order to handle the problem, anti-jamming communication driven by reinforcement learning is studied in intelligent reflecting surface (IRS)-aided vehicular networks. The system sum transmission rate is optimized by joint designing the transmit beamforming at the roadside unit (RSU) and the reflection coefficients at the IRS. An anti-jamming strategy based on combining annealing bias-priority experience replay method and twin delayed deep deterministic policy gradient (TD3) technique is developed to handle the formulated challenging non-convex problem. The proposed strategy is employed to train the replay buffer in TD3, which can eliminate the deviation under the distribution change and has the advantages of fast convergence and is not easy to fall into local optima. Numerical results confirm that our proposed strategy can enhance the sum rate of multiple vehicular users and ensure radar sensing capability of RSU compared with the existing methods.

Cognitive Industrial Internet of Things (CIIoT) permits Secondary Users (SUs) to use the spectrum bands owned by Primary Users (PUs) opportunistically. However, in the absence of the PUs, the selfish SUs could mislead the normal SUs to leave the spectrum bands by initiating a Primary User Emulation Attack (PUEA). In addition, the application of Energy Harvesting (EH) technology can exacerbate the threat of security. Because the energy cost of initiating a PUEA is offset to some extent by EH technology which can proactively replenish the energy of the selfish nodes. Thus, EH technology can increase the motivation of the selfish SUs to initiate a PUEA. To address the higher motivation of the selfish SUs attacking in CIIoT scenario where the EH technology is applied, in this paper, an EH-PUEA system model is first established to study the security countermeasures in this severe scenario of PUEA problems. Next, a new reward and punishment defense management mechanism is proposed, and then the dynamics of the selfish SUs and the normal SUs in a CIIoT network are studied based on Evolutionary Game Theory (EGT), and the punishment parameter is adjusted according to the dynamics of the selfish SUs to reduce the proportion of the selfish SUs’ group choosing an attack strategy, so as to increase the throughput achieved by the normal SUs’ group. Finally, the simulation results show that the proposed mechanism is superior to the conventional mechanism in terms of throughput achieved by the normal SUs’ group in CIIoT scenario with EH technology applied.

This work proposes a covert communication scheme in a cognitive radio network where a secondary transmitter (ST) transmits confidential information to a secondary receiver under the cover of jammers with homogeneous Poisson point process. Specifically, we first analyze the detection performance of the primary transmitter (PT) and Willie under collaboration and non-collaboration modes. We then derive the covert transmission outage probability under ST’s correct and incorrect decisions for whether PT transmits or not and obtain the expression for the effective covert rate (ECR). In order to maximize the ECR, we derive the optimal value of the time allocation ratio, based on which, we also derive the optimal value of ST’s transmit power subject to the covertness constraint and some power constraints. Our examination shows the non-collaboration mode outperforms the collaboration mode in terms of achieving a higher ECR, because the uncertainty of the PT’s transmission in the former one will cause confusion at Willie and lead to an increased detection error rate. In addition, the proposed scheme effectively increases the ECR when compared with the scheme without the jammer.

In this paper, a novel system utilizing a hybrid relay-intelligent reflecting surface (HR-IRS) to boost the security performance of directional modulation (DM) is established. In particular, the malicious attacker works in full-duplex (FD) mode and it will eavesdrop on confidential message (CM) as well as send malicious jamming. To maximize the secrecy rate (SR), a joint problem of optimizing the receive beamforming, transmit beamforming, power allocation (PA) factor, and phase shift matrix (PSM) of HR-IRS is formulated. Since the optimization problem is un-convex and the variables are coupled with each other, we address this problem by iteratively optimizing these variables. First, the receive beamforming is designed based on the generalized Rayleigh-Ritz theorem. Then, the transmit beamforming and PA factor are optimized via Dinkelbach’s Transform and successive convex approximation methods. And for PSM, two strategies, called separate optimization of PSM (SO-PSM) and joint optimization of PSM (JO-PSM), are proposed. Thus, two iterative schemes are proposed accordingly, namely maximizing SR based on SO-PSM (Max-SR-SOP) and maximizing SR based on JO-PSM (Max-SR-JOP). The former has a better performance and the latter has a lower complexity. Simulation results show that given a sufficient power budget of HR-IRS, the proposed Max-SR-SOP and Max-SR-JOP can enable HR-IRS-aided DM network to obtain a higher SR than that aided by passive IRS.

As an emerging technology for wireless communication, the intelligent reconfigurable surface (IRS) is made up of numerous low-cost passive elements with reconfigurable parameters, which can reflect signals with a certain phase shift and build a programmable communication environment. To reduce the high hardware cost and energy consumption in spatial modulation (SM), an IRS-aided hybrid secure SM (SSM) system with a hybrid precoder is proposed in this paper, where an optimization problem is formulated to maximize the secrecy rate (SR) by jointly optimizing the beamforming at IRS and the hybrid precoding at transmitter. For the IRS beamforming, an alternating direction method of multipliers (IRS-ADMM) scheme is first proposed. To achieve a higher SR, an IRS beamforming scheme via semidefinite relaxation (IRS-SDR) is put forward. To reduce the high complexity of IRS-SDR, we design a block coordinate ascend-based IRS beamforming scheme (IRS-BCA) with a closed-form solution. As for the hybrid precoding, two methods, called a successive convex approximation method based on the approximate secrecy rate (ASR-SCA) and a gradient ascend method based on cut-off rate (COR-GA), are presented. Simulation results show that the proposed IRS-ADMM and IRS-SDR harvest substantial SR performance gains over IRS-BCA. In comparison to the IRS-ADMM and IRS-SDR, the proposed IRS-BCA is of the lowest complexity at the cost of performance loss. Regarding hybrid precoding, the proposed ASR-SCA outperforms COR-GA in the high transmit power region. According to the complexity and SR performance of six combination methods including each IRS beamforming method and each hybrid precoding method, we selected three combinations: IRS-BCA plus ASR-SCA, IRS-ADMM plus ASR-SCA and IRS-SDR plus COR-GA. Moreover, it is showed that the SR performance achieved by the three combination methods is significantly higher than those of IRS with random beamforming and without IRS.

In an intelligent reflecting surface (IRS)-assisted wireless communication system, obtaining the real-time channel state information (CSI) through channel estimation (CE) is crucial for achieving the IRS’s passive beamforming gain, which however shortens the effective data transmission time due to the CSI feedback overhead. It is of utmost importance to decide how often to estimate the channels in an IRS-assisted system. In this paper, we propose an integrated CE and beamforming scheme to jointly optimize the adaptive CE interval and passive beamforming strategy, based on the past observation sequences composed of imperfect CSI and data rate feedback. We formulate the two-stage optimization problem as a bilevel partially observable Markov decision process (POMDP), aiming to maximize the expectation of cumulative throughput of the system. We propose two bilevel deep reinforcement learning (DRL) algorithms, namely recurrent neural network (RNN) based proximal policy optimization (PPO) algorithm and Belief-based PPO algorithm, to solve this problem. In these two algorithms, the CSI features from the past observation sequences are implicitly extracted by the RNN network or explicitly inferred by the belief network, which then serve as the inputs for the two-stage policy networks to determine the necessity of CE and the IRS beamforming vector based on the PPO algorithm. Simulation results demonstrate the superiority of the proposed adaptive CE scheme over the periodic counterpart in terms of throughput. Moreover, the results show that it is profitable to estimate the channels less frequently if the channels exhibit a higher correlation across time.

We consider a covert communication system from a transmitter Alice to a receiver Bob with the aid of a proba-bilistic and uninformed jammer against an adversary warden's detection, where the jammer's transmission status and power are random with priori probabilities. We first analyze the warden's detection performance as a function of the jammer's transmission probability, transmit power distribution (e.g., minimum and maximum transmit power, average power), and Alice's transmit power, based on which we optimize these parameters to maximize the communication throughput from Alice to Bob subject to a covertness constraint. Our analysis reveals that the jammer's minimum transmit power is not always zero in the probabilistic jamming strategy, which is different from that in the continuous jamming strategy presented in the literature. Instead, our analysis proves that the jammer's minimum jamming power is the same as Alice's covert transmit power, which depends on the required covertness level and the available average jamming power. Furthermore, our results show that the probabilistic jamming can outperform the continuous jamming in terms of achieving a higher covert communication throughput.

In this paper, we consider optimizing a multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) transceiver at the roadside unit (RSU) to detect a potential eavesdropping target and transmit the private information securely to the legitimate vehicular users. An optimization problem is formulated by optimizing the sum secrecy throughput of vehicle-to-infrastructure (V2I) links under requirements of waveform similarity and target return signal-to-interference-plus-noise ratio (SINR) threshold. To handle the challenging issue, we first cast the resulting non-convex problem into an equivalent optimization problem relying on the mean-square error (MSE) technique specified resource budget, and then develop an alternating procedure to decouple two optimization variables and decompose the resulting problem as two subproblems. To deal with the subproblem, we propose a dual ascent approach (DAA) based on the Limited-memory Broyden Fletcher Goldfarb and Shanno (LBFGS). Simulation results confirm the efficiency of the devised optimization method.

In this work, we consider the optimality of signalling for covert sensing, where a legitimate receiver intends to estimate unknown variables based on the received signals from a transmitter, while ensuring that the probability of these signals being detected by a warden Willie is negligible. Specifically, we consider additive white Gaussian noise (AWGN) for both the estimation channel and detection channel, based on which we first reveal that Gaussian signalling is not optimal in terms of maximizing the estimation accuracy (e.g., maximizing the Fisher information) subject to a covertness constraint, e.g., guaranteeing a Kullback-Leibler divergence being no large than a specific value. To this end, we explicitly show that a skew normal distribution with an optimized skew parameter can achieve a higher estimation accuracy than a normal distribution subject to the same covertness constraint. Furthermore, we develop a framework based on calculus of variations and the Runge-Kutta method to identify the optimal signalling for covert sensing. As expected and explicitly shown in our numerical results, the identified optimal signalling outperforms both the normal and skew normal distributed signalling, which demonstrates the necessity of optimizing signalling in the context of covert sensing.

Abstract Based on the successful application of generative adversarial network (GAN) models in the field of image generation, this article introduces GANs into the field of deep learning for communication systems and surveys its application in modulation classification. To solve the difficulties in feature extraction, to address the low recognition accuracy of existing radio signal modulation‐type recognition methods, and to adapt to complex electromagnetic environments with high noise interference intensity, this article presents a modulation recognition model for high‐order digital signals. This model uses the Morlet wavelet transform to analyse time‐frequency signals, uses the excellent image generation performance of a GAN model to extract and reconstruct the features of noise‐contaminated time‐frequency images, and designs an integrated classification network architecture to classify and predict reconstructed images. The experimental results show that the algorithm model proposed in this article can significantly improve the recognition accuracy of high‐order digital modulated signals under low signal‐to‐noise ratio conditions and can achieve 90% recognition accuracy at a signal‐to‐noise ratio of 1 dB.

The covert communication technology has emerged as a novel method for network authentication, copyright protection, and providing the evidence of cybercrimes. However, how to design the covert communication scheme in the physical layer of wireless networks and how to optimize the data rate for the covert communication channels are very challenging. In this article, we propose a wireless covert communication system (CCS), where the transmit antennas are selected and coded to generate a covert codebook. According to the covert codebook, the antennas can be dynamically combined to transmit different covert messages. In addition, we adopt a modulation scheme, named covert quadrature amplitude modulation (QAM), to modulate the covert messages, where the precoding method is designed to deviate the constellations for covert information bits from those for the public information bits. Furthermore, we derive the closed-form expressions of capacity and bit error ratio (BER) for the proposed CCS. To maximize the covert data rate of the CCS, we formulate an optimization problem of the covert data rate and solve the problem to find the optimal precoding matrix. To reduce the covert information leakage, artificial noise is introduced to the system to jam the communication between the transmitting and watching nodes. We design a beamforming scheme to maximize the secure rate for the CCS, where the leakage of covert information can be minimized while the covert communication is not influenced. Simulation results show that the proposed CCS can significantly improve the covert data rate and reduce the covert BER in comparison with the traditional CCSs.

The use of a reconfigurable intelligent surface (RIS) in the enhancement of the rate performance is considered to involve the limitation of the RIS being a passive reflector. To address this issue, we propose a RIS-aided amplify-and-forward (AF) relay network in this paper. By jointly optimizing the beamforming matrix at AF relay and the phase-shift matrices at RIS, two schemes are put forward to address a maximizing signal-to-noise ratio (SNR) problem. First, aiming at achieving a high rate, a high-performance alternating optimization (AO) method based on Charnes–Cooper transformation and semidefinite programming (CCT-SDP) is proposed, where the optimization problem is decomposed into three subproblems solved using CCT-SDP, and rank-one solutions can be recovered using Gaussian randomization. However, the optimization variables in the CCT-SDP method are matrices, leading to extremely high complexity. To reduce the complexity, a low-complexity AO scheme based on Dinkelbachs transformation and successive convex approximation (DT-SCA) is proposed, where the variables are represented in vector form, and the three decoupling subproblems are solved using DT-SCA. Simulation results verify that compared to three benchmarks (i.e., a RIS-assisted AF relay network with random phase, an AF relay network without RIS, and a RIS-aided network without AF relay), the proposed CCT-SDP and DT-SCA schemes can harvest better rate performance. Furthermore, it is revealed that the rate of the low-complexity DT-SCA method is close to that of the CCT-SDP method.

Extremely large-scale reconfigurable intelligent surface (XL-RIS) has recently been proposed and is recognized as a promising technology that can further enhance the capacity of communication systems and compensate for severe path loss. However, the pilot overhead of beam training in XL-RIS-assisted wireless communication systems is enormous because the near-field channel model needs to be taken into account, and the number of candidate codewords in the codebook increases dramatically. To tackle this problem, we propose two deep learning-based near-field beam training schemes in XL-RIS-assisted communication systems, where deep residual networks are employed to determine the optimal near-field RIS codeword. Specifically, we first propose a far-field beam-based beam training (FBT) scheme in which the received signals of all far-field RIS codewords are fed into the neural network to estimate the optimal near-field RIS codeword. In order to further reduce the pilot overhead, a partial near-field beam-based beam training (PNBT) scheme is proposed, where only the received signals corresponding to the partial near-field XL-RIS codewords are input to the neural network. Moreover, we further propose an improved PNBT scheme to enhance the performance of beam training by fully exploring the neural network’s output. Finally, simulation results show that the proposed schemes outperform the existing beam training schemes and can reduce the beam sweeping overhead by approximately 95%.

A main challenge for radar-aided millimeter wave (mmWave) vehicle-to-infrastructure (V2I) communication application, is that it requires mitigating the mutual interference between vehicular radar and communication operating at same frequency bands. This paper considers the joint design of the multiple-input multiple-output (MIMO) transmit waveform and receive filter bank for road side unit (RSU)-mounted radar in a spectrally crowded V2I communication environment. With the criterion of maximizing the average signal-to-interference-plus-noise ratio (SINR), a non-convex problem, which involves the weighted-sum waveform energy over the overlayed space-frequency bands, energy and similarity constraints, is formulated. An iterative algorithm is proposed to solve the joint optimization problem. At each iteration, the transmit waveform is optimized based on the alternating direction method of multipliers (ADMM) method with a significantly lower computational complexity. As a consequence, accurate location information derived from the optimized radar is used to reduce the beam training overhead of V2I communication links. Finally, simulation results display the effectiveness of the devised method in finding feasible and enhanced solutions, importantly outperforming several counterparts.

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, potential eavesdropping is one of the main threats to V2V communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multiple armed bandit problem is designed to implement the secret key switching procedure when it is needed. Through constant perception-execution loops (PELs), the security and confidentiality is improved for the authorized vehicles in their behavioral interactions with the illegal eavesdropper. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

In this paper, we investigate the energy-efficiency (EE) problem in edge association for heterogeneous Internet of Vehicles (IoV), when the dynamic environmental information can not be known in advance. Aiming to maximize the long-term tradeoff between EE and handover (HO) overhead, we propose a cooperative multi-agent edge association solution, where vehicular user equipments (VUEs) make decisions cooperatively relying on their local observations under centralized training. Specifically, we first construct a multi-agent partially observable Markov decision process (MA-POMDP) problem and decompose the system value function into the local value functions for implicit individual learning. Next, through sharing learning experience and approximating the global state, each VUE is able to obtain its own optimal/suboptimal policy given its local observations and historical information. Simulation results show that the proposed solution outperforms the non-cooperative counterpart and other baselines in terms of improving EE with the most appropriate number of HOs.

In this article, an effective energy-efficiency optimization problem is investigated in the cognitive unmanned aerial vehicle (UAV) communication system, where the moving following UAV (FUAV) transmits collected data to the leading UAV (LUAV) by reusing the spectrum of the ground primary user. For this scenario, a novel joint UAV trajectory and resource allocation optimization algorithm is proposed. We aim to maximize the energy efficiency of the cognitive UAV communication systems under interference, shortest step size, collision prevention, and speed constraints. However, this optimization problem is difficult to be solved, as it is nonconvex and involves strong relations among many variables. To address this issue, we first decompose the original optimization problem into four subproblems: 1) spectrum sensing duration subproblem; 2) spectrum sensing threshold subproblem; 3) transmitted power subproblem; and 4) FUAV trajectory optimization subproblem. For spectrum sensing duration subproblem and spectrum sensing threshold subproblem, their optimal solutions can be efficiently solved via a golden section search method. For the transmitted power subproblem, the closed-form expression of the optimal transmitted power is deduced. For the nonconvex FUAV trajectory optimization subproblem, we consider the shortest step size design under known starting and destination positions and then obtain an approximate solution of FUAV trajectory via the successive convex optimization (SCO) technique. Finally, an alternate iterative framework with fast convergence is designed to solve the original optimization problem. The simulation results show that the proposed algorithm has 18 times improvement in energy efficiency compared with the straight flight scheme with lower energy consumption.

Vehicular edge computing (VEC) has become a promising enabler for ultrareliable and low-latency communications (URLLC) vehicular networks by providing computational resources for task offloading. In this article, we investigate an online task offloading problem for heterogeneous VEC (HVEC) network in the face of unknown environment dynamics. To overcome the unavailability of state information, we aim for minimizing the expectation of total offloading energy consumption while satisfying stringent delay requirements by learning the relationship between historical observations and rewards. Hence, this problem constitutes a contextual multiarmed bandit (MAB) problem. By grouping users according to their task preferences, we propose a contextual clustering of bandits-based online vehicular task offloading (CBTO) solution, which is aware of the task popularity. Simulation results reveal that the proposed solution outperforms other contextual and context-free benchmarkers in terms of both offloading energy consumption and delay performance.

In this article, we investigate the anti-jamming problem with joint channel and power allocation for unmanned aerial vehicle (UAV) networks. In particular, we focus on avoiding both mutual interference among UAVs and external malicious jamming to maximize the system Quality of Experience (QoE) relevant to the power consumption. To simultaneously capture the competition and coordination among UAVs, we first model the problem as a local interaction Markov game and then prove it as an exact potential game with at least one Nash equilibrium. Next, we propose a collaborative multiagent layered Q learning (MALQL)-based anti-jamming communication algorithm to reduce the high dimensionality of the action space and analyze the asymptotic convergence of the proposed algorithm. Simulation results show the effectiveness of the proposed algorithm, which outperforms the traditional multiagent$Q$learning algorithm when suffering from different jamming strategies.

In this paper, the joint design of power allocation (PA), beamforming (BF) and positioning is studied for unmanned-aerial-vehicle (UAV) aided millimeter-Wave (UAV-mmWave) systems, with the objective of maximizing the energy efficiency (EE), under the constraints of maximum transmitting power, minimum data rate from the ground users and positioning range of the UAV. To address the above problem, we first obtain the positioning of the UAV, with the help of approximate beam pattern. Then, near-optimal BF and closed-form PA are derived given the obtained position, with the help of block coordinate descent method. To reduce the complexity, two suboptimal BF schemes with one-loop iteration and closed-form solutions are respectively derived. Furthermore, we propose the simplified algorithms for two special cases, i.e., only line-of-sight (LoS) path and Non-LoS (NLoS) path exist between the users and the UAV. Simulation results verify the effectiveness of the developed joint schemes and show the superior EE performance. Moreover, they can obtain almost the same performance as the existing benchmark schemes but with lower complexity.

Reconfigurable intelligent surfaces (RISs) are envisioned to be a disruptive wireless communication technique that is capable of reconfiguring the wireless propagation environment. In this paper, we study a free-space RIS-assisted multiple-input single-output (MISO) communication system in far-field operation. To maximize the received power from the physical and electromagnetic nature point of view, a comprehensive optimization, including beamforming of the transmitter, phase shifts of the RIS, orientation and position of the RIS is formulated and addressed. After exploiting the property of line-of-sight (LoS) links, we derive closed-form solutions of beamforming and phase shifts. For the non-trivial RIS position optimization problem in arbitrary three-dimensional space, a dimensional-reducing theory is proved. The simulation results show that the proposed closed-form beamforming and phase shifts approach the upper bound of the received power. The robustness of our proposed solutions in terms of the perturbation is also verified. Moreover, the RIS significantly enhances the performance of the mmWave/THz communication system.

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS.

This paper studies a convincingly secure transmission framework under an allowable outage probability for practical finite-alphabet inputs, where a full-duplex receiver (Bob) is taken into account to emit the artificial noise for deteriorating the eavesdropper’s decoding performance. We develop a secure-reliable mechanism to take the place of prior secrecy rate (SR) maximization strategy, where a closed form expression is invoked for substituting the non-closed SR expression upon exploiting the multi-exponential decay fitting approach. Hence, the intractable expectation operation over a large number of noise samples is circumvented. Moreover, a pair of critical probabilities of the reliable transmission and secure outage are first analyzed, and then a low-complexity optimization scheme is formulated. Apart from designing the transmission scheme for classically secure networks consisting of a transmitter, Bob and an eavesdropper, we further carry out an investigation on conceiving a secure-reliable strategy against multiple Eves for improving the system’s extensibility. To this end, analytical expressions of both the reliability outage and secrecy outage probabilities for multiple-Eve scenarios are also derived. Furthermore, a pragmatic iterative solution is conceived for addressing the corresponding max-min optimization problem. Finally, the simulation results validate the significance of our considered secure-reliable transmission in terms of the average SR performance attained.

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces.

Mobile edge computing (MEC) enables computing services at the network edge closer to mobile users (MUs) to reduce network transmission latency and energy consumption. Deploying edge computing servers in small base stations (SBSs), operators make profit by offering MUs with computing services, while MUs purchase services to solve their own computation tasks quickly and energy-efficiently. In this context, it is of particular importance to optimize computing resource allocation and computing service pricing in each SBS, subject to its limited computing and communication resources. To address this issue, we formulate an optimization problem of computing resource management and trading in small-cell networks and tackle this problem using a two-tier matching. Specifically, the first tier targets at the association algorithm between MUs and SBSs to achieve maximum social welfare, and the second tier focuses on the collaboration algorithm among SBSs to make efficient usage of limited computing resources. We further show that the two proposed algorithms contribute to stable matchings and achieve weak Pareto optimality. In particular, we verify that the first algorithm arrives at a competitive equilibrium. Simulation results demonstrate that our proposed algorithms can achieve a better network social welfare than baseline algorithms while retaining a close-optimal performance.

Because of discrepant deteriorations of the intended receiver and the unintended receiver, artificial noise (AN) can be invoked in conjunction with the precoder to wireless transmissions for enhancing the secrecy rate (SR) performance as long as we elaborately frame them. This paper studies a secure transmission strategy by jointly designing the precoder and AN beamformer at the relay network, where a passive eavesdropper and finite-alphabet inputs are taken into account. We propose a pair of solutions for low-order modulation and high-order modulation, respectively. To solve the first optimization problem, we propose a low-complexity algorithm with the aid of the invoked cut-off rate. Interestingly, we find that the phase of an optimum precoder for maximizing the SR has a correlation to both the channels spanning from the transmitter to the relays and spanning from the relays to the legitimate receiver. Furthermore, we reveal that the AN beamformer vector has at most one non-zero component, which only locates at the position corresponding to the minimum element of the channel between the relays and the intended receiver. According to these findings, the SR maximization problem over the two vectors is simplified as one only related to a pair of scalars. As for the high-order modulation, a new solution is further proposed for circumventing the predicament that the computational complexity exponentially increases as the size of the adopted modulation increases, where we not only eliminate two-layer summation over the legitimate symbols but also conceive a concave maximization SR problem. Finally, simulation results demonstrate the efficiency of the proposed algorithms in terms of the SR performance.

This work examines the performance gain achieved by deploying an intelligent reflecting surface (IRS) in covert communications. To this end, we formulate the joint design of the transmit power and the IRS reflection coefficients by taking into account the communication covertness for the cases with global channel state information (CSI) and without a warden’s instantaneous CSI. For the case of global CSI, we first prove that perfect covertness is achievable with the aid of the IRS even for a single-antenna transmitter, which is impossible without an IRS. Then, we develop a penalty successive convex approximation (PSCA) algorithm to tackle the design problem. Considering the high complexity of the PSCA algorithm, we further propose a low-complexity two-stage algorithm, where analytical expressions for the transmit power and the IRS’s reflection coefficients are derived. For the case without the warden’s instantaneous CSI, we first derive the covertness constraint analytically facilitating the optimal phase shift design. Then, we consider three hardware-related constraints on the IRS’s reflection amplitudes and determine their optimal designs together with the optimal transmit power. Our examination shows that significant performance gain can be achieved by deploying an IRS into covert communications.

A two‐stream remote sensing image fusion network (RCAMTFNet) based on the residual channel attention mechanism is proposed by introducing the residual channel attention mechanism (RCAM) in this paper. In the RCAMTFNet, the spatial features of PAN and the spectral features of MS are extracted, respectively, by a two‐channel feature extraction layer. Multiresidual connections allow the network to adapt to a deeper network structure without the degradation. The residual channel attention mechanism is introduced to learn the interdependence between channels, and then the correlation features among channels are adapted on the basis of the dependency. In this way, image spatial information and spectral information are extracted exclusively. What is more, pansharpening images are reconstructed across the board. Experiments are conducted on two satellite datasets, GaoFen‐2 and WorldView‐2. The experimental results show that the proposed algorithm is superior to the algorithms to some existing literature in the comparison of the values of reference evaluation indicators and nonreference evaluation indicators.

This work examines the performance gain achieved by deploying an intelligent reflecting surface (IRS) for delay-constrained covert communications. To this end, we formulate the joint design of the transmit power and the IRS reflection coefficients, including its phase shifts and reflection amplitudes, to maximize the communication quality subject to a covertness constraint. We first prove that perfect covertness is achievable with the aid of the IRS even for a single-antenna transmitter, which is impossible without the IRS. Then, we develop a penalty-based successive convex approximation (PSCA) algorithm to tackle the design optimization problem. Considering the high complexity of the PSCA algorithm, we further propose a low-complexity two-stage algorithm, where closed-form expressions for the transmit power and the IRS's reflection coefficients are derived. Our examination shows that significant performance gain can be achieved by deploying an IRS into covert communications.

This work considers unmanned aerial vehicle (UAV) networks for collecting data covertly from ground users. The full-duplex (FD) UAV intends to gather critical information from a scheduled user (SU) through wireless communication and generate artificial noise (AN) with random transmit power in order to ensure a negligible probability of the SU’s transmission being detected by the unscheduled users (USUs). To enhance the system performance, we jointly design the UAV’s trajectory and its maximum AN transmit power together with the user scheduling strategy subject to practical constraints, e.g., a covertness constraint, which is explicitly determined by analyzing each USU’s detection performance, and a binary constraint induced by user scheduling. The formulated design problem is a mixed-integer non-convex optimization problem, which is challenging to solve directly, but tackled by our developed penalty successive convex approximation (P-SCA) scheme. An efficient UAV trajectory initialization is also presented based on the successive hover-and-fly (SHAF) trajectory, which also serves as a benchmark scheme. Our examination shows the developed P-SCA scheme significantly outperforms the benchmark scheme in terms of achieving a higher max-min average transmission rate (ATR) from all the SUs to the UAV.

Intelligent reflecting surface (IRS) is of low-cost and energy-efficiency and will be a promising technology for the future wireless communications like sixth generation. To address the problem of conventional directional modulation (DM) that Alice only transmits single confidential bit stream (CBS) to Bob with multiple antennas in a line-of-sight channel, IRS is proposed to create friendly multipaths for DM such that two CBSs can be transmitted from Alice to Bob. This will significantly enhance the secrecy rate (SR) of DM. To maximize the SR (Max-SR), a general non-convex optimization problem is formulated with the unit-modulus constraint of IRS phase-shift matrix (PSM), and the general alternating iterative (GAI) algorithm is proposed to jointly obtain the transmit beamforming vectors (TBVs) and PSM by alternately optimizing one and fixing another. To reduce its high complexity, a low-complexity iterative algorithm for Max-SR is proposed by placing the constraint of null-space (NS) on the TBVs, called NS projection (NSP). Here, each CBS is transmitted separately in the NSs of other CBS and AN channels. Simulation results show that the SRs of the proposed GAI and NSP can approximately double that of IRS-based DM with single CBS for massive IRS in the high signal-to-noise ratio region.

Technically, the security performance of generalized spatial modulation (GenSM) networks can be enhanced by dynamically adjusting the precoder allocated to the legitimate signal as communication channel varies. For this purpose, our paper proposes a secure transmission strategy upon designing both digital and analog precoders for hybrid GenSM systems, where an eavesdropper is taken into account. The concept of the hybrid GenSM system has arose to improve the spatial multiplexing (SMX) gain for remedying the shortcoming of the limited number of radio frequency chains in traditional GenSM systems. However, this may lead to a great deal of security degradation since the SMX gain of the unintended receiver will be also improved. To this end, we develop a secrecy enhancement scheme by devising both analog and digital precoders for hybrid GenSM networks. Specifically, we derive an efficiently closed-form alternative to the original secrecy rate (SR) expression for reducing the excessive computational complexity of the joint optimization problem over the hybrid precoder. Then, by using this alternative as our cost function, an iterative algorithm is proposed. In particular, we elaborately conceive a pair of concave maximization problems in order to optimize the digital and analog precoders, respectively. Our proposed strategy not only utilizes semi-positive definite relaxing technique over the analog precoder but also invokes a lower bound of the alternative to further simplify the optimization over the vectored digital precoder. Subsequently, both the convergence and computational complexity of the proposed alternating iteration algorithm are analyzed. Compared to existing designs, our proposed strategy strikes a compelling role in balancing the SR performance and complexity. Finally, our simulation results confirm the efficiency of the proposed algorithm in terms of the SR performance achieved.

Wireless caching technologies have been proposed to relieve the transmission pressures, especially, the transmission redundancy on back-haul channels. In this paper, we consider a two-layer caching network, consisting of traditional macro-cell base station (MBS) aided back-haul channels and small-cell base stations (SBSs) aided local links. The network service provider (NSP), who is in charge of the two layers, leases its resources of the secondary layer, i.e., coverage of the SBSs, to content providers (CPs) for making extra profits and releasing pressures on the back-haul channels. At the same time, CPs will evaluate whether they are provided with proper incentives to pre-cache their files in the SBSs. Considering different quality of services (QoS) provided by the two layers as well as the economical impact of the traditional layer on the secondary layer, the NSP designs the optimal incentive mechanisms within the framework of contract theory for maximizing its own profits. First, we formulate the utility of the NSP and CPs. Then, the minimum transmission requirement, reserve price and limited resources are considered as constraints in designing the optimal contract. Also, some important properties of these constraints are analyzed to facilitate the optimal contract determination process. At last, an optimal contract determination scheme is proposed, based on which the optimal coverage set is determined first, and then the corresponding optimal prices are derived with the aid of equal cost line. Numerical results are provided to demonstrate the effectiveness of the proposed optimal contract in increasing the NSP's profits and incentivizing CPs to transmit on the secondary layer.

Spatial modulation (SM) is a particularly important form of multiple-input multiple-output (MIMO), which uses both modulation symbols and antenna indices to carry information. In this paper, to avoid the high cost and circuit complexity of SM, we consider the hybrid SM system with a hybrid precoding transmitter architecture, combining a digital precoder and an analog precoder. In such a system, we carried out secure hybrid precoding and transmit antenna subarray selection (TASS) methods. Two hybrid precoding methods, called maximizing approximate secrecy rate (SR) via gradient ascent (Max-ASR-GA) and maximizing approximate SR via alternating direction method of multipliers (Max-ASR-ADMM), are proposed to improve the SR performance. As for TASS, a high-performance method of maximizing the approximate SR (Max-ASR) is first presented. To reduce its high complexity, two low-complexity TASS methods, namely maximizing eigenvalue (Max-EV) and maximizing product of signal-to-interference-plus-noise ratio and artificial noise-to-signal-plus-noise ratio (Max-P-SINR-ANSNR), are proposed. Simulation results demonstrate that the proposed Max-ASR-GA and Max-ASR-ADMM hybrid precoders harvest substantial SR performance gains over existing method. For TASS, the proposed three methods Max-ASR, Max-EV, and Max-P-SINR-ANSNR perform better than existing leakage method. Particularly, the proposed Max-EV and Max-P-SINR-ANSNR are of low-complexity at the expense of a little performance loss compared with Max-ASR.

Due to robustness against multi-path effect, channel state information (CSI) of Orthogonal Frequency Division Multiplexing (OFDM) systems is supposed to provide accurate distance measurement for indoor localization. However, we find that the original CSI ranging model is biased, so the model cannot be used to directly derive Cramer-Rao lower bound (CRLB) of positioning error for CSI-ranging based localization scheme. In this paper we first analyze the estimation bias of the original CSI ranging model according to indoor wireless channel model. Then we propose a negative power summation ranging model which can be used as an unbiased ranging model for both Line-Of-Sight (LOS) and Non-LOS scenarios. Subsequently, based on the proposed model, we derive both the CRLB of ranging error and the CRLB of positioning error for CSI-ranging localization scheme. Through simulation we validate the bias of the original ranging model and the approximately zero bias of our proposed ranging model. Through comprehensive experiments in different indoor scenarios, localization errors by different ranging models are compared to the CRLB, meanwhile our proposed ranging model is demonstrated to have better ranging and localization accuracy than the original ranging model.

Covert communications can hide the very existence of wireless transmissions and thus are able to address privacy issues in numerous applications of the emerging Internet of Things (IoT). In this article, we adopt channel inversion power control (CIPC) to achieve covert communications in Rayleigh fading wireless networks, where a transmitter can possibly hide itself from a warden while transmitting information to a receiver. The CIPC can guarantee a constant signal power at the receiver, which removes the requirement that the receiver has to know the channel state information in order to coherently decode the transmitter's signal. Specifically, we examine the performance of the achieved covert communications in terms of the effective covert rate (ECR), which quantifies the amount of information that the transmitter can reliably convey to the receiver subject to the warden's total error probability being no less than some specific value. The noise uncertainty at the warden serves as the enabler of covert communications. For fairness, we also consider noise uncertainty at the legitimate receiver. Our examination shows that increasing the noise uncertainty at the warden and the receiver simultaneously may not continuously improve the ECR achieved in the considered system model.

This work, for the first time, tackles channel estimation design with pilots in the context of covert wireless communication. We consider Rayleigh fading for the communication channel from a transmitter to a receiver, both additive white Gaussian noise (AWGN) and Rayleigh fading for the detection channel from the transmitter to a warden. Before transmitting information signals, the transmitter has to send pilots to enable channel estimation at the receiver. For the case with AWGN detection channel, we first analytically prove that transmitting pilot and information signals with equal power minimizes the warden's detection performance. This motivates us to consider the equal transmit power and then optimize channel use allocation between pilot and information signals under this case. Our analysis shows that the optimal number of the channel uses allocated to pilots increases as the covertness constraint becomes tighter. For the case with Rayleigh fading detection channel, we present a general framework to optimally allocate transmit power and channel uses between pilot and information signals. Our examination shows that the covert communication performance gain achieved by this general framework is not remarkable relative to the scheme with equal transmit power and this gain diminishes as the covertness constraint becomes stricter.

To enhance transmission performance, privacy level, and energy manipulating efficiency of wireless networks, this article initiates a novel simultaneous wireless information and power transfer (SWIPT) full-duplex (FD) relaying protocol, named harvest-and-opportunistically-relay (HOR). Due to the FD characteristics, the dynamic fluctuation of relay's residual energy is difficult to quantify and track. To solve this problem, the Markov Chain (MC) theory is invoked. Furthermore, to improve the privacy level of the proposed HOR relaying system, covert transmission performance analysis is performed, where closed-form expressions of the optimal detection threshold and minimum detection error probability are derived. Last but not least, with the aid of stationary distribution of the MC, closed-form expression of transmission outage probability is calculated, based on which transmission outage performance is analyzed. Numerical results have validated the correctness of analyses on transmission outage and covertness. The impacts of key system parameters on the performance of transmission outage and covertness are given and discussed. Based on mathematical analysis and numerical results, we showcase that the proposed HOR model can not only reliably enhance the transmission performance via smartly managing residual energy but also efficiently improve the privacy level of the legitimate transmission party via dynamically adjusting the optimal detection threshold.

The security of spatial modulation (SM) aided networks can always be improved by reducing the desired link's power at the cost of degrading its bit error ratio performance and assuming the power consumed to artificial noise (AN) projection (ANP). We formulate the joint optimization problem of maximizing the secrecy rate (Max-SR) over the transmit antenna selection and ANP in the context of secure SM-aided networks. In order to solve this problem, we provide a pair of solutions, namely joint and separate solutions. Specifically, an accurate approximation of the SR is used for reducing the computational complexity, and the optimal AN covariance matrix (ANCM) is found by convex optimization for any given active antenna group (AAG). Then, given a large set of AAGs, simulated annealing mechanism is invoked for optimizing the choice of AAG, where the corresponding ANCM is recomputed by this optimization method as well when the AAG changes. To further reduce the complexity of the above-mentioned joint optimization, a low-complexity two-stage separate optimization method is also proposed. Moreover, when the number of transmit antennas tends to infinity, the Max-SR problem becomes equivalent to that of maximizing the ratio of the desired user's signal-to-interference-plus-noise ratio to the eavesdropper's. Thus, our original problem reduces to a fractional programming problem and a significant computational complexity reduction can be achieved. Finally, our simulation results verify the efficiency of the proposed methods in terms of the SR performance attained.

Unmanned aerial vehicles (UAVs) are envisaged to play a new and important role in future cellular networks. In this paper, we analyze the uplink performance of heterogeneous networks with UAVs in terms of coverage probability and area spectral efficiency (ASE). To be more specific, we first investigate the system performance under a general channel model, with practical considerations such as (1) line-of-sight and non-line-of-sight components, (2) antenna height difference L between the UAVs and the base stations (BSs), and (3) idle mode capabilities (IMCs) at the BSs to mitigate inter-cell interference. Thereafter, we study the system performance under the latest UAV path loss model defined by the 3rd Generation Partnership Project. Under this special case, we provide a more detailed analysis of the coverage probability as well as the ASE, and explore the impacts of difference system parameters on the system performance. Numerical results validate the analytical expressions and show that (1) the IMC can improve the coverage probability and the ASE, especially when the network is dense, (2) the overall system performance degrades when L increases, and (3) the fractional power control factor has a negligible impact on the UAVs performance when L is large enough.

In this paper, the problem of content caching in 5G cellular networks relying on social-aware device-to-device communications (DTD) is investigated. Our focus is on how to efficiently select important users (IUs) and how to allocate content files to the storage of these selected IUs to form a distributed caching system. We aim at proposing a novel approach for minimizing the downloading latency and maximizing the social welfare simultaneously. In particular, we first model the problem of maximizing the social welfare as a many-to-one matching game based on the social property of mobile users. We study this game by exploiting users' social properties to generate the utility functions of the two-side players, i.e., content providers (CPs) and IUs. Then we model the problem of minimizing the downloading latency as a many-to-many matching problem. For solving these games, we design a many-to-one IU selection (MOIS) matching algorithm and a many-to-many file allocation (MMFA) matching algorithm, respectively. Simulation and analytical results show that the proposed mechanisms are stable, and are capable of offering a better performance than other benchmarks in terms of social welfare and network downloading latency.

Secure wireless information and power transfer based on directional modulation is conceived for amplify-and-forward relaying networks. Explicitly, we first formulate a secrecy rate maximization (SRM) problem, which can be decomposed into a twin-level optimization problem and solved by a one-dimensional (1D) search and semidefinite relaxation (SDR) technique. Subsequently, in order to reduce the search complexity, we formulate an optimization problem based on maximizing the signal-to-leakage-AN-noise-ratio (Max-SLANR) criterion, and transform it into a SDR problem. In addition, the relaxation is proved to be tight according to the classic Karush-Kuhn-Tucker (KKT) conditions. Finally, to reduce the computational complexity, a successive convex approximation (SCA) scheme is proposed to find a near-optimal solution. The complexity of the SCA scheme is much lower than that of the SRM and the Max-SLANR schemes. Simulation results demonstrate that the performance of the SCA scheme is very close to that of the SRM scheme in terms of its secrecy rate and bit error rate, but much better than that of the zero forcing scheme.

This paper considers a multiple-access system with multiple-packet reception (MPR) capability γ, i.e., a packet can be successfully received as long as it overlaps with γ -1 or fewer other packets at any instant during its lifetime. To efficiently utilize the MPR capability, this paper generalizes p-persistent carrier-sense multiple access (CSMA) to consider that a user with carrier sensing capability c adopts the transmission probability p, if this user has sensed n ongoing transmissions for n = 0, 1,⋯, c - 1. This paper aims to model the characteristics of such CSMA and to design transmission probabilities for achieving maximum saturation throughput. To this end, we first formulate such CSMA as a parameterized Markov decision process (MDP) and use the long-run average performance to evaluate the saturation throughput. Second, by observing that the exact values of optimal transmission probabilities are in general infeasible to find, we modify this MDP to establish an upper bound on the maximum throughput, and modify this MDP again to propose a heuristic design with near-optimal performance. Simulations with respect to a wide range of configurations are provided to validate our study. The throughput performance under more general models and the robustness of our design are also investigated.

Evidence indicates that demands from mobile users (MU) on popular cloud content, e.g., video clips, account for a dramatic increase in data traffic over cellular networks. The repetitive downloading of hot content from cloud servers will inevitably bring a vast quantity of redundant data transmissions to networks. A strategy of distributively pre-storing popular cloud content in the memories of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the communication latency whilst mitigating the redundant data streaming substantially. In this paper, we establish a commercialized small-cell caching system consisting of a network service provider (NSP), several video providers (VP), and randomly distributed MUs. We conceive this system in the context of 5G cellular networks, where the SBSs are ultra-densely deployed with the intensity much higher than that of the MUs. In such a system, the NSP, in charge of the SBSs, wishes to lease these SBSs to the VPs for the purpose of making profits, whilst the VPs, after pushing popular videos into the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. Specifically, we first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, with the help of the probability derived, we formulate the profits of both the NSP and the VPs. Next, we solve the profit maximization problem based on the framework of contract theory, where the NSP acts as a monopolist setting up the optimal contract according to the statistical information of the VPs. Incentive mechanisms are also designed to motivate each VP to choose a proper resource-price item offered by the NSP. Numerical results validate the effectiveness of our proposed contract framework for the commercial caching system.

This paper examines the possibility, performance limits, and associated costs for a self-sustained relay to transmit its own covert information to a destination on top of forwarding the source's information. Since the source provides energy to the relay for forwarding its information, the source does not allow the relay's covert transmission and to detect it. Considering the time switching (TS) and power splitting (PS) schemes for energy harvesting, where all the harvested energy is used for transmission at the self-sustained relay, we derive the minimum detection error probability ξ* at the source based on which we determine the maximum effective covert rate ψ* subject to a given covertness constraint on ξ*. Our analysis shows that ξ* is the same for the TS and PS schemes, which leads to the fact that the cost of achieving ψ* in both the two schemes in terms of the required increase in the energy conversion efficiency at the relay is the same, although the values of ψ* in these two schemes can be different in specific scenarios. For example, the TS scheme outperforms the PS scheme in terms of achieving a higher ψ* when the transmit power at the source is relatively low. If the covertness constraint is tighter than a specific value, it is the covertness constraint that limits ψ*, and otherwise, it is upper bound on the energy conversion efficiency that limits ψ*.

One of the key metrics in measuring system quality of service (QoS) is the delay performance. Most existing papers have focused on the studies of decreasing transmission delay. However, as the wireless communication traffic increasing dramatically, queueing delay in the wireless networks becomes a non-negligible issue. Martingale theory, which fits any arrival and service process, providing a much tighter delay bound compared to the effective bandwidth theory, has been proposed to analyze the system queueing delay bound, especially in a bursty traffic scenario. In this paper, we propose to study the resource allocation problem based on the delay bounds derived from martingale theory. In specific, we first revisit some basic knowledge about stochastic network calculus, and present the delay bounds derived from martingale theory in certain typical bursty service models. Then, we setup a resource allocation problem in a computation offloading scenario, where multiple computation nodes with distinct computation capacities are considered. User's computation tasks are usually bursty, and are required to be executed within a limited time. We propose to minimize the system delay violation probability by properly allocating the computation tasks to different computation nodes. A closed-form solution is derived for the computation offloading problem, using a special kind of water-filling policy. Moreover, we discuss two potential models of martingale-based resource allocation, and provide the corresponding system architectures. Finally, numerical results are presented to demonstrate the performances of the proposed scheme. The proposed water-filling scheme achieves a smaller system delay violation probability compared to the benchmark.

In this work, we propose a covert communication scheme where the transmitter attempts to hide its transmission to a full-duplex receiver, from a warden that is to detect this covert transmission using a radiometer. Specifically, we first derive the detection error rate at the warden, based on which the optimal detection threshold for its radiometer is analytically determined and its expected detection error rate over wireless fading channels is achieved in a closed-form expression. Our analysis indicates that the artificial noise deliberately produced by the receiver with a random transmit power, although causes self-interference, offers the capability of achieving a positive effective covert rate for any transmit power (can be infinity) subject to any given covertness requirement on the expected detection error rate. This work is the first study on the use of the full- duplex receiver with controlled artificial noise for achieving covert communications and invites further investigation in this regard.

A large number of computation-hungry mobile applications have led to an ever-increasing computation demands. Mobile edge computing (MEC) has been considered as an emerging paradigm to alleviate the demand effectively by offloading the computationally intensive tasks from mobile devices (MD) to the adjacent MEC servers. It is expected that the quality of computation experience, e.g., the computing energy and the execution latency, can be greatly improved by the MEC. In this paper, we will investigate the computing task offloading problem via the MEC in the context of small-cell base-station (SBS) networks, where each SBS is equipped with an MEC server. Specifically, we first formulate the optimization problem to minimize the objective, i.e., the weighted sum of energy consumption and execution duration. The parameters to be optimized are the allocations of the MD's tasks to be offloaded to the MEC servers. Then we propose a novel belief propagation (BP) algorithm to optimize the task allocation in a distributed manner. Next, we develop the factor graph according to the network topology and decompose the object function into multiple local utilities to fit the factor graph. Finally, we transform local utilities into the estimations of marginal distributions and propose a distributed BP algorithm to solve the estimations. Simulations demonstrate that our BP algorithm can effectively approach the optimal solutions via exhaustive search.

With the burst of mobile data, it is necessary to make use of idle mobile equipment for caching space. Caching in the femto-cells is proposed for reducing transmission latency between the WiFi points and its mobile users (MU) and better user service. In this paper, we firstly take the copyright of the files as the allocation resource and the WiFi points with caching space want to rent these copyrights. We propose a multi-rounds double auction mechanism for this problem and take the popularity parameter of the files as the quality weight. This game can help multiple content providers (CP) lease copyrights of the files to multiple WiFi points effectively. Different from traditional double auctions, it will take the failed buyers and sellers into consideration and they are allowed to change their requests in the next auction process. This mechanism can largely improve efficiency of the game and is budget balanced. We also prove that the allocation process is monotone and with the set of the critical payment rule, we prove the truthfulness of the mechanism. Additionally, we prove that the mechanism can form a conditional equilibrium. Simulation results verify the effectiveness of the proposed mechanism and compare with the traditional one-round double auction.

In this paper, we propose an effective read-voltage optimization method for multi-level-cell (MLC) NAND flash memory to improve the performance of error correcting codes (ECCs) with finite blocklength. Specifically, we first obtain the maximal channel coding rate achievable at a given blocklength and error probability of quantized channel. Based on this finite-blocklength channel-coding rate (FCR), we convert the optimization problem into minimizing the error probability instead of the channel coding rate. Then, we develop a cross iterative search (CIS) method and the genetic algorithm to solve this optimization problem. In our simulations, for a well-designed LDPC code, our read-voltage optimization method improves program-and-erase (PE) endurance up to about 900 and 600 cycles against the maximizing the mutual information (MMI) and entropy-based optimization methods, respectively, at a frame-error-rate (FER) of 2×10-4.

In this paper, a practical wireless transmission scheme is proposed to transmit confidential messages to the desired user securely and precisely by the joint use of multiple techniques, including artificial noise (AN) projection, phase alignment/beamforming, and random subcarrier selection (RSCS) based on orthogonal frequency division multiplexing (OFDM), and directional modulation (DM), namely RSCS-OFDM-DM. This RSCS-OFDM-DM scheme provides an extremely low-complexity structure for the desired receiver and makes the secure and precise wireless transmission realizable in practice. For illegal eavesdroppers, the receive power of confidential messages is so weak that their receivers cannot intercept these confidential messages successfully once it is corrupted by AN. In such a scheme, the design of phase alignment/beamforming vector and AN projection matrix depends intimately on the desired direction angle and distance. It is particularly noted that the use of RSCS leads to a significant outcome that the receive power of confidential messages mainly concentrates on the small neighboring region around the desired receiver and only small fraction of its power leaks out to the remaining large broad regions. This concept is called secure precise transmission. The probability density function of real-time receive signal-to-interference-and-noise ratio (SINR) is derived. Also, the average SINR and its tight upper bound are attained. The approximate closed-form expression for average secrecy rate is derived by analyzing the first-null positions of the SINR and clarifying the wiretap region. Simulation and analysis show that the proposed scheme actually can achieve a secure and precise wireless transmission of confidential messages in line-of-propagation channel, and the derived theoretical formula of average secrecy rate is verified to coincide with the exact results well for medium and large scale transmit antenna array or in the low and medium SNR regions.

A large-scale fully digital receive antenna array can provide very high-resolution direction of arrival (DOA) estimation, but resulting in a significantly high RF-chain circuit cost. Thus, a hybrid analog and digital (HAD) structure is preferred. Two phase alignment (PA) methods, HAD PA (HADPA) and hybrid digital and analog PA (HDAPA), are proposed to estimate DOA based on the parametric method. Compared to analog PA (APA), they can significantly reduce the complexity in the PA phases. Subsequently, a fast root multiple signal classification HDAPA (root-MUSIC-HDAPA) method is proposed specially for this hybrid structure to implement an approximately analytical solution. Due to the HAD structure, there exists the effect of direction-finding ambiguity. A smart strategy of maximizing the average receive power is adopted to delete those spurious solutions and preserve the true optimal solution by linear searching over a set of limited finite candidate directions. This results in a significant reduction in computational complexity. Eventually, the Cramer-Rao lower bound (CRLB) of finding emitter direction using the HAD structure is derived. Simulation results show that our proposed methods, root-MUSIC-HDAPA and HDAPA, can achieve the hybrid CRLB with their complexities being significantly lower than those of pure linear searching-based methods, such as APA.

Covert wireless communication aims to hide the very existence of wireless transmissions in order to guarantee a strong security in wireless networks. In this paper, we examine the possibility and achievable performance of covert communication in amplify-and-forward one-way relay networks. Specifically, the relay is greedy and opportunistically transmits its own information to the destination covertly on top of forwarding the source's message, while the source tries to detect this covert transmission to discover the illegitimate usage of the resource (e.g., power and spectrum) allocated only for the purpose of forwarding the source's information. We propose two strategies for the relay to transmit its covert information, namely rate-control and power-control transmission schemes, for which the source's detection limits are analyzed in terms of detection error probability and the achievable effective covert rates from the relay to destination are derived. Our examination determines the conditions under which the rate-control transmission scheme outperforms the power-control transmission scheme, and vice versa, which enables the relay to achieve the maximum effective covert rate. Our analysis indicates that the relay has to forward the source's message to shield its covert transmission and the effective covert rate increases with its forwarding ability (e.g., its maximum transmits power).

Covert communications hide the transmission of a message from a watchful adversary while ensuring a certain decoding performance at the receiver. In this paper, a wireless communication system under fading channels is considered where covertness is achieved by using a full-duplex receiver. More precisely, the receiver of covert information generates artificial noise with a varying power causing uncertainty at the adversary, Willie, regarding the statistics of the received signals. Given that Willie's optimal detector is a threshold test on the received power, we derive a closed-form expression for the optimal detection performance of Willie averaged over the fading channel realizations. Furthermore, we provide guidelines for the optimal choice of artificial noise power range, and the optimal transmission probability of covert information to maximize the detection errors at Willie. Our analysis shows that the transmission of artificial noise, although causing self-interference, provides the opportunity of achieving covertness but its transmit power levels need to be managed carefully. We also demonstrate that the prior transmission probability of 0.5 is not always the best choice for achieving the maximum possible covertness when the covert transmission probability and artificial noise power can be jointly optimized.

Covert communication aims to shield the very existence of wireless transmissions in order to guarantee a strong security in wireless networks. In this work, for the first time we examine the possibility and achievable performance of covert communication in one- way relay networks. Specifically, the relay opportunistically transmits its own information to the destination covertly on top of forwarding the source's message, while the source tries to detect this covert transmission to discover the illegitimate usage of the recourse (e.g., power, spectrum) allocated only for the purpose of forwarding source's information. The necessary condition that the relay can transmit covertly without being detected is identified and the source's detection limit is derived in terms of the false alarm and miss detection rates. Our analysis indicates that boosting the forwarding ability of the relay (e.g., increasing its maximum transmit power) also increases its capacity to perform the covert communication in terms of achieving a higher effective covert rate subject to some specific requirement on the source's detection performance.

Wireless caching is an efficient mechanism for reducing downloading delay and reducing the traffic pressure over backhual channels by caching some popular content, e.g., video clips, in small base stations (SBSs). In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs) and mobile users (MUs). The NSP leases its SBSs to VRs in order to earn profits, while the VRs store popular videos into the lent SBSs, thereby gaining profits from providing better services to the MUs. We conceive the system within the framework of contract theory by designing the optimal quality-price contract. We establish the profit function of NSP and VRs and solve the profit maximization problem through contract theory. Numerical results validate the effectiveness of our incentive mechanism for the system.

Evidences indicate that wireless video traffic has played an important role in cellular networks. Caching mechanisms which store popular contents into local small-cell base stations (SBSs) in cellular networks are proposed to further reduce transmission delay and release the traffic pressure over backhaul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs) and multiple mobile users (MUs). The NSP as a network facility monopoly releases its resources to the VRs in order to maximize its own profits. The distribution of VR's type is known to the NSP, while the actual type of a given VR is not known. We research on such an information asymmetric market within the framework of contract theory, formulated as an adverse selection problem. The MUs and SBSs are modeled as two independent Poisson point processes, and the directly downloading probability from the adjacent SBS is derived via stohastic geometry theory. Based on the probability, we formulate the utility functions of the NSP and the VRs. Then, the optimal contract problem is constructed. Also, we provide the feasibility of the contract, and the optimal contract is proposed when VR's popularity parameter γ takes different values. Numerical results are provided to show the optimal quality and the optimal price designed for each VR.

To offer communication capability in an easy and simple deployment, power line communications (PLCs) have recently attracted interest from the smart grid. The effective sub‐channel assignment can increase throughput of In‐Home (IH) PLC networks with orthogonal frequency division multiplexing scheme distributed over low‐voltage areas. Given a logical topology of an IH PLC network, the authors present a formulation to optimise the sub‐channel assignment problem as a linear programming with an objective function of maximising the network throughput. It takes into account constraints of the network topology, interference and traffic fairness. According to the solution of the optimising sub‐channel assignment problem, the scheduling algorithm of links to obtain channels is developed for every time slot. Evaluation demonstrates that the proposed approach performs much better in improving overall network throughput and ensuring the traffic fairness of network users than conventional ones.

Recently, content-aware-enabled distributed caching relying on local small-cell base stations (SBSs), namely, smallcell caching, has been intensively studied for reducing transmission latency as well as alleviating the traffic load over backhaul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several content providers (CPs), and multiple mobile users (MUs). The NSP, as a network facility monopolist in charge of the SBSs, leases its resources to the CPs for gaining profits. At the same time, the CPs are intended to rent the SBSs for providing better downloading services to the MUs. We focus on solving the profit maximization problem for the NSP within the framework of contract theory. To be specific, we first formulate the utility functions of the NSP and the CPs by modeling the MUs and SBSs as two independent Poisson point processes. Then, we develop the optimal contract problem for an information asymmetric scenario, where the NSP only knows the distribution of CPs' popularity among the MUs. Also, we derive the necessary and sufficient conditions of feasible contracts. Lastly, the optimal contract solutions are proposed with different CPs' popularity parameter γ. Numerical results are provided to show the optimal quality and the optimal price designed for each CP. In addition, we find that the proposed contract-based mechanism is superior to the benchmarks from the perspective of maximizing the NSP's profit.

In this paper, spatial channel pairing (SCP) is introduced to coherent combining at the relay in relay networks. Closed-form solution to optimal coherent combining is derived. Given coherent combining, the approximate SCP solution is presented. Finally, an alternating iterative structure is developed. Simulation results and analysis show that, given the symbol error rate and data rate, the proposed alternating iterative structure achieves signal-to-noise ratio gains over existing schemes in maximum ratio combining (MRC) plus matched filter, MRC plus antenna selection, and distributed space-time block coding due to the use of SCP and iterative structure.

In wideband multi-pair two-way relay networks, the performance of beamforming at a relay station (RS) is intimately related to the accuracy of the channel state information (CSI) available. The accuracy of CSI is determined by Doppler spread, delay between beamforming and channel estimation, and density of pilot symbols, including transmit power of pilot symbols. The coefficient of the Gaussian-Markov CSI error model is modeled as a function of CSI delay, Doppler spread, and signal-to-noise ratio, and can be estimated in real time. In accordance with the real-time estimated coefficients of the error model, an adaptive robust maximum signal-to-interferenceand- noise ratio (Max-SINR) plus maximum signal-to-leakage-and-noise ratio (Max-SLNR) beamformer at an RS is proposed to track the variation of the CSI error. From simulation results and analysis, it is shown that: compared to existing non-adaptive beamformers, the proposed adaptive beamformer is more robust and performs much better in the sense of bit error rate (BER); with increase in the density of transmit pilot symbols, its BER and sum-rate performances tend to those of the beamformer of Max-SINR plus Max-SLNR with ideal CSI.

Consider a time-slotted communication channel that is shared by K active users transmitting to a single receiver. It is assumed that the receiver has the ability of the multiple-packet reception to correctly receive up to γ (1 ≤ γ <; K) simultaneously transmitted packets. Each user accesses the channel following a deterministic binary sequence, called the protocol sequence, and transmits a packet within a channel slot if the sequence value is equal to one. If the users are not time synchronized, the relative shifts among them can cause significant fluctuation in throughput. If the throughput of each user is independent of relative shifts, then the adopted protocol sequence set is said to be throughput-invariant (TI). If we define worst-case system throughput as the minimal system throughput that can be guaranteed for any set of relative shifts, then TI sequences maximize it and hence are of fundamental interest. This paper investigates TI sequences for γ ≥ 1. Several new results are obtained including throughput value as a function of the duty factors, a lower bound on the sequence period, a construction that achieves the lower bound on the sequence period, and theorems on the intrinsic structure that establish connections with some other families of binary sequences.

In this paper, we investigate a two-way relaying power line communication (PLC) network with analog network coding. We focus on the analysis of the system outage probability, symbol error rate, and average capacity. Specifically, we first derive the probability density function (PDF) of the received signal-to-noise ratio (SNR) with a closed form, by exploiting the statistical properties of the PLC channel. Then with the help of this PDF, we develop the outage probability, symbol error rate, and average capacity with closed forms, based on the Hermite polynomial. Simulations show that the derived analytical results are consistent with those by Monte Carlo simulation.

We consider the slot-synchronized collision channel without feedback, in which K active users all transmit their packets to one sink. It is assumed that the channel has the ability of the multiple-packet reception (MPR), i.e., can accommodate at most γ (1 ≤ γ1. For both design objective, we establish a lower bound on sequence period and prove the lower bound can be achieved by some construction.

In this paper we investigate the identification and synchronization problems on a multiple access collision channel. Following Massey's lead, solutions to these problems are addressed by protocol sequences. This paper considers two different levels of user synchroneity: frame-synchronous access and slot-synchronous access. For the identification problem, we study user-detectable sequences. These are sequences with the cross-correlation property that allows each active user be detected within a bounded delay basing only on the channel activity information observed. Furthermore, we investigate the synchronization problem for delay-detectable sequences under the slot-synchronous access assumption. The goal of the synchronization problem is to determine the offset relations among all the active users. Sequences that allow such determination can be viewed as a special subset of user-detectable sequences. For both of these sequence families, it is desirable that the sequence length should be as short as possible. Hence, it is important to derive the minimum sequence lengths for these respective families. This is an extremely difficult open problem. Nevertheless, lower and upper bounds on these minimum lengths are presented in this paper under different levels of synchroneity assumptions. In addition, the performance of these sequences is demonstrated via numerical simulation.

Zadoff-Chu (ZC) sequences have been used as random access sequences in modern wireless communication systems, replacing the conventional pseudo-random-noise (PN) sequences due to their superior autocorrelation properties. An analytical framework quantifying the ZC sequence's performance and its fundamental limitation as a random access sequencein the presence of frequency offset between the transmitter and the receiver is introduced. We show that a ZC sequence's perfect autocorrelation properties can be severely impaired by the frequency offset thereby limiting the overall performance of the random access signals formed from these sequences. First, we derive the autocorrelation function of these random access sequences as a function of the frequency offset. Next, we introduce the concept of critical frequency offsets and the spectrum associated with a ZC sequence set to characterize the frequency offset properties of the random access signals. Finally, we demonstrate that the frequency offset immunity of a ZC sequence set can be controlled by shaping the spectrum of the ZC sequence set.

This paper proposes a unique discovery signal as an enabler of peer-to-peer (P2P) communication which overlays a cellular network and shares its resources. Applying P2P communication to cellular network has two key issues: 1. Conventional ad hoc P2P connections may be unstable since stringent resource and interference coordination is usually difficult to achieve for ad hoc P2P communications; 2. The large overhead required by P2P communication may offset its gain. We solve these two issues by using a special discovery signal to aid cellular network-supervised resource sharing and interference management between cellular and P2P connections. The discovery signal, which facilitates efficient neighbor discovery in a cellular system, consists of un-modulated tones transmitted on a sequence of OFDM symbols. This discovery signal not only possesses the properties of high power efficiency, high interference tolerance, and freedom from near-far effects, but also has minimal overhead. A practical discovery-signal-based P2P in an OFDMA cellular system is also proposed. Numerical results are presented which show the potential of improving local service and edge device performance in a cellular network.

Accurate channel information at the transmitter is crucial to multi-user MIMO performance. Unfortunately, the bandwidth of the control channel by which the feedback is conveyed is often limited. An important issue is how to improve multi-user MIMO performance with minimal feedback. Conventional feedback techniques focus on improving the quantized codebook performance using various quantization criteria. In this paper, instead of trying to optimize a single codebook, we apply multiple alternating codebooks to effectively reduce the multi-user MIMO quantization error in addition to the reduction provided by the single codebook optimization techniques. That is, we use an existing quantization codebook design methodology to create not one but multiple such similar codebooks. The codebooks are alternated at each feedback instance creating a larger virtual codebook with the same number of feedback bits as the single smaller codebook. Simulation results show that significant performance gain in multi-user MIMO systems is obtained via the alternating codebook scheme.

We investigate two different relay selection (RS) methods for precoded decode-and-forward (DF) cooperative OFDM systems: one scheme is to select the best relays for individual precoding groups (IPG-RS), and the other is to choose a single relay for the entire precoding groups (EPG-RS). We derive their diversity performances from the pairwise error probability (PEP) analysis, indicating that both schemes can achieve the full frequency and cooperative diversity with a proper choice of precoding size. Numerical results also show that IPG-RS can either reduce the decoding complexity or improve the error performance significantly as compared to existing solutions whereas EPG-RS has some performance loss as a result of less communication overhead.

Two fast image super-resolution reconstruction algorithms are proposed based on multigrid (MG) and Krylov subspace accelerative algorithms. After briefly introduction of image super-resolution reconstruction model, MG and Krylov subspace accelerative algorithms, two accelerative MG algorithm named MG-CG and MG-GMRES are proposed to solve the sparse symmetric positive definite and non-symmetric linear equation, which are often occurred in image super-resolution reconstruction. The restriction, prolongation and smoothing operators of each algorithm are thoroughly studied and the convergence is analyzed respectively. Experimental results demonstrate that the proposed algorithms can greatly improve the convergence rate compared with MG, two Krylov accelerative algorithms and Richardson iteration.