Offloading computationally intensive tasks to edge nodes reduces latency and improves user experience. Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) can effectively address the limitations of the fixed deployment of traditional edge nodes, but the dynamic nature of UAVs brings challenges to the optimization of computation offloading. Methods based on deep reinforcement learning (DRL) can efficiently learn edge network dynamics to optimize computation offloading in UAV-assisted systems. In the system model, different edge nodes such as UAVs, roadside units, and user equipments (UEs) are treated as agents in the reinforcement learning network, so that the optimization of the edge computation offloading strategy can be transformed into a multi-agent optimization problem. In addition, meta-reinforcement learning is introduced into the multi-agent deep deterministic policy gradient (MADDPG) algorithm to cope with the dynamics and uncertainty of the UAV-assisted edge computing network environment. Simulation results show that the computation offloading strategy based on meta-reinforcement learning can not only quickly adapt to the dynamic changes of the network environment but also outperform other benchmark algorithms in terms of network energy efficiency.

With the rapid development of the internet-of-things (IoT), the application of IoT terminals (ITs) has been growing exponentially. To address this challenge, ultra-dense networks have been widely regarded as an effective solution. However, under the constraints of task latency and resource limitations, how to achieve the joint offloading and caching of energy efficiency and security remains a critical issue. To address it, we first propose two types of secure collaborative offloading modes for this network framework, i.e., secure collaborative computation offloading with caching and non-caching. Under these two modes, we then strive to minimize the overall local energy consumption (EC) of all ITs, subject to the constraints of computational resources, latency, security cost, and caching capacity. This is achieved by jointly optimizing device association, cache decision-making, channel selection, executing decision-making, power control, secure service allocation, and multi-step task offloading. To solve the formulated nonlinear fractional problem efficiently, we put forward an improved football team training algorithm (IFTTA), which integrates a diversity-guided mutation strategy into the original football team training algorithm (FTTA). Furthermore, we conduct an in-depth analysis of the convergence properties and computational complexity of the proposed algorithm. Simulation results demonstrate that the IFTTA achieves lower total local EC and task-processing delay compared to the FTTA, while satisfying system constraints, and generally outperforms existing state-of-the-art methods.

This paper proposes a unified sparse transmission framework for coded modulation-based unsourced random access (URA) systems, referred to as sparse linear precoded URA (SLP-URA). The proposed design generalizes and unifies a variety of existing URA schemes including random spreading, sparse interleave division multiple access (IDMA), and on-off division multiple access (ODMA) as special cases of a broader design space characterized by tunable sparsity structures. To support this flexible framework, we derive a consistent generalized log-likelihood ratio (LLR)-based active user detection (AUD) method, and further propose message passing (MP)-based AUD and multi-user detection (MUD) algorithms that accommodate arbitrary sparsity patterns. An approximate low-complexity implementation is also introduced to reduce computational burden. Through rigorous symbol-level signal-to-interference-plus-noise ratio (SINR) analysis, we establish that increasing the column weight of the SLP matrix reduces SINR variance and outage probability, providing rigorous theoretical grounding for the performance gains. Simulations demonstrate that the proposed SLP-URA framework consistently achieves improved performance over conventional ODMA schemes, even when the latter are equipped with enhanced detection algorithms. These results suggest that SLP-URA provides a robust foundation for future URA system design and optimization.

Reconfigurable intelligent surface (RIS) plays an essential role in alleviating severe path loss in millimeter wave communication systems. Its performance hinges on the precise modeling of high-dimensional cascaded channels. However, traditional modeling approaches require extensive experience in radio propagation, resulting in complex and inefficient processes. To overcome these limitations, we transform the RIS channel modeling into a channel distribution transport mapping problem and introduce a generative model based on rectified flow. Our approach integrates distance information into a diffusion transformer (DiT) architecture through cross-attention mechanisms, resulting in a conditional DiT capable of synthesizing target channels from distance inputs. We further optimize the rectified flow into a single-step generator via reflow techniques. Building on this framework, we design a generative digital twin (DT) channel model that serves as a high fidelity data generator for downstream tasks. The proposed model acts as a virtual replica of the propagation environment, enabling efficient channel data synthesis for training communication algorithms such as channel state information feedback and channel estimation. Simulation results show that our approach generates channels with minimal distribution discrepancy compared to real channels (a maximum mean discrepancy < 0.01), outperforming existing generative methods. Furthermore, the reflow-driven DT channel model achieves the shortest generation time among all evaluated benchmarks.

Cell-free massive multi-input-multi-output (CF-mMIMO) provides significant improvement owing to the distributed architecture. However, it suffers from the constraints including information constraints, and computation resources constraints. In this paper, we propose 4 ranks of available information in CF-mMIMO and aim to find a distributed precoding exploiting randomly accessible side information, which is one-step without iterations and robust against the unreliable fronthaul between distributed central processing units. Quadratic team learning (QTL) is devised which is derived from team theory to handle the distributed underdetermined quadratic programming. The extensive 1440 experiments validate the superiority of QTL and we believe QTL is a definitely excellent choice for CF-mMIMO distributed precoding. To the best of our knowledge, this is the first work utilizing team theory to help the design of artificial intelligence architecture for wireless communications. To prompt the development of QTL, we have open-sourced the implementation code on https://github.com/hzy238221seu/QTL4CF-Precoding.git.

Cell-free massive MIMO (CF-mMIMO) represents the pinnacle of distributed antenna systems, offering superior service to all users. However, the distributed nature of access points leads to fragmented information processing, limiting performance in practical deployments. Additionally, existing studies often overlook the impact of limited fronthaul capacity, which introduces noise and degrades the reliability of shared information. In this work, we implement a practical CF-mMIMO prototype under fifth generation new radio standards and propose a diffusion-based uplink scheme that outperforms conventional distributed cell-free systems without cooperation. Our approach adapts to arbitrary fronthaul topologies by leveraging the law of large numbers. We further analyze the linear effects of fronthaul noise and the correlation of uploaded data, demonstrating that the diffusion uplink excels in Rician fading environments while maintaining robust performance in Rayleigh fading. To the best of our knowledge, this is the first work to employ a diffusion model for mitigating fronthaul non-idealities, enabling distributed cooperative uplink in a real-world CF-mMIMO system.

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 studies the physical-layer security (PLS) for an unmanned aerial vehicle (UAV)-aided non-orthogonal multiple access (NOMA) network, where transmissions from a base station (BS) to a trusted near user (TNU) and an untrusted far user (UFU) are assisted by an UAV relay in the face of multiple non-colluding external eavesdroppers (Es). The traditional strategies such as artificial noise-aided friendly jammer (TAN-FJ), artificial noise with non-friendly jammer (TAN-NFJ), and reconfigurable intelligent surface (RIS)-aided schemes fail to simultaneously guarantee the secrecy for both TNU and UFU, thus how to guarantee the PLS for NOMA users becomes an urgent issue to be solved. In this context, we propose an artificial noise (PAN) scheme aided with digital network coding (DNC). Specifically, the artificial noise signals generated by UAV are resorted to encrypt confidential signals with the assistance of DNC, which realizes one-time pad, thus the PLS of TNU and UFU can be ensured. We resort security-reliability tradeoff (SRT) as a metric to evaluate the PLS for our proposed PAN scheme. Hence, the exact and asymptotic expressions of outage probability (OP) and intercept probability (IP) are derived to quantify the reliability and security. Moreover, numerical simulations validate the correctness of our theoretical results and reveal that: 1) the PAN scheme enhances the SRT performance of near user compared to the TAN-FJ, TAN-NFJ, and RIS-aided schemes, while far user by the PAN scheme achieves almost the same SRT performance as the TAN-FJ scheme and significantly better performance than the TAN-NFJ and RIS-aided schemes, which indicates that the PAN scheme realizes the fairness of secrecy transmissions for NOMA users; 2) the SRT performance of TNU benefits from multiple antennas at BS and UAV, which has ignorable effect on the SRT performance of UFU; 3) the antenna number at UAV has more obvious impact on the SRT of TUN than the antenna number at BS.

Movable antenna (MA) technology enables flexible beamforming through adaptive adjustment of antenna positions, thereby enhancing communication performance. This paper investigates the MA-aided air-to-ground (A2G) system, where an unmanned aerial vehicle (UAV) equipped with MAs serves multiple ground users. The primary objective is to jointly optimize UAV deployment, antenna positions, beamforming, and power control to maximize the downlink communication sum rate. A key challenge lies in the difficulty of acquiring complete instantaneous channel state information (I-CSI), as the antennas cannot traverse all possible positions to obtain such full-scale I-CSI, which impedes the efficient optimization of system parameters. To address this challenge, we propose a two-stage transmission scheme: in the first stage, location-aware statistical CSI (S-CSI) is leveraged to optimize UAV deployment, power control, and MA positions for maximizing long-term system performance, while the second stage estimates I-CSI based on the optimized UAV and antenna positions to refine short-term transmit beamforming after long-term variables are determined, thereby substantially reducing CSI acquisition overhead. For long-term policy optimization, we introduce a novel dual-spatial-scale hierarchical deep reinforcement learning (DSSH-DRL) framework, which preserves flexibility in global policy formulation while enhancing training efficiency via effective decoupling of control variables. Simulation results demonstrate the effectiveness of the proposed algorithm and validate the promising application potential of MA technology in A2G communication networks.

We investigate the demand-private coded caching problem, in whichKusers, each equipped with a cache of sizeM, access a library ofNfiles under a privacy constraint. This constraint requires that no user obtain any information about the demands of others. We first present a new virtual-user-based achievable scheme for arbitrary numbers of users and files, which yields tighter order-optimal guarantees whenN≤KandM≤ 1. Next, we further focus on the caseN≤K. On the achievability side, for cache sizeM∈ [0,N/(K+1)(N−1)], we propose a novel demand-private scheme based on the idea that each user’s decoding process should depend only on their own demand. In terms of converse, we derive a new converse bound that is applicable forN≤Kand arbitraryM. Comparing the proposed achievability and converse, we find the optimal memory-rate tradeoff of the demand-private coded caching problem forM∈ [0,N/(K+1)(N−1)] whereN≤K≤ 2N−2, and the optimal memory-rate tradeoff forM∈ [0,1/K+1] whereK> 2N− 2. Moreover, for the case of 2 files and arbitrary number of users, by deriving another new converse bound, the optimal memory-rate tradeoff is characterized forM∈ [0,2/K] ∪ [2(K-1)/K+1,2]. Finally, we provide the optimal memory-rate tradeoff of the demand-private coded caching problem for 2 files and 3 users under arbitrary cache sizeM.

Cell-free is considered a promising technology for the future network, which adopts a large number of distributed antennas to provide a uniformly good service. However, current researches under the long-term evolution standard mostly ignore the problem of asynchronous transmission brought by the different transmission delays due to the geographical distance differences, and assume that the system is perfectly synchronized. On the other, these works often do not consider a distributed method with controllable fronthaul overhead compatible with cell-free. To enable an extensive cell-free in the sixth generation, we derive an asynchronous analysis framework and propose a centralized and a distributed downlink precoding method respectively. What is more important, we have verified that cell-free suffers from inter-carrier-interference and inter-symbol-interference under the 5th generation new radio standard. To the best of our knowledge, this is the first work implementing a distributed asynchronous precoding method in an extensive cell-free, and simulation results demonstrate the effectiveness of the proposed two precoding methods, compared to naive precoding ignoring the asynchronous impact.

Cell-free is seen as one of the most important technology for the future wireless communications. In this paper, we adopt a wideband cell-free to implement low-altitude coverage to serve multiple unmanned aerial vehicles (UAVs) in the city playing the core role of low-altitude economy. For practice, distributed computation, asynchronous effects, beam split and imperfect channel state information are considered. We mainly rely on per-beam synchronization (PBS) and discuss different architecture implementations. A wideband asynchronous architecture that reuses the time delay modules exploited in wideband beam split calibration is proposed. In addition, a semi-synchronized path set (SSP-Set) is derived to eliminate asynchronous interference and a geometric scattering graphic convolutional network is used to acquire the (sub)-optimal SSP-Set. Based on these two technologies, a beam-delay alignment transmission (BDAT) scheme is obtained and we implement it with a distributed paradigm. The numerical results demonstrate the proposed BDAT can benefit from the cooperative downlink beamforming and provide a uniformly good service for UAVs.

Combining integrated sensing and communication (ISAC) and an unmanned aerial vehicle (UAV) can not only save the wireless resource but also enhance the air-ground coverage. However, the high-quality air-ground link of ISAC network is more prone to exposure, and its security is challenging. In this paper, we design a covert air-ground transmission scheme for ISAC, where the sensing signal can be utilized as a mask to disrupt the detection of communication by Willie. Since it is difficult to obtain the accurate knowledge about Willie’s location, we employ the norm-bounded model to describe the uncertainty of location at Willie. To further enhance the covertness, a full-duplex (FD) UAV user is considered to receive the covert signal while transmitting the artificial jamming to confuse Willie. We first calculate the minimum detection error probability (MDEP) by deriving the optimal detection threshold, and we obtain the analytic expression of average MDEP. Then, the covert transmission rate is maximized by controlling beamforming vectors and the UAV trajactory while satisfying the target detection constraint, the covertness constraint as well as the transmit power constraint, which can be resolved by an alternating optimization algorithm. Finally, we present simulation results to verify that the proposed scheme with the FD jamming can better guarantee the covertness of air-ground ISAC.

Cell-free massive multiple-input multiple-output (MIMO) systems have emerged as a transformative architecture for sixth generation (6G) communication networks, where distributed access points (APs) collaborate to simultaneously serve all user equipments (UEs). However, in time division duplex (TDD) systems, the reciprocity of uplink channel and downlink channel is disrupted by hardware imperfections in radio frequency (RF) chains, leading to significant degradation in system performance. This paper begins with a theoretical analysis of the downlink performance under a conjugate beamforming scheme, considering scenarios with and without channel calibration. A key theoretical insight highlights the limitation of conventional least squares (LS) calibration method, which fails to achieve high calibration accuracy even with an unlimited number of pilot observations. To overcome this limitation, we propose a novel channel calibration approach based on a diffusion model, designed to successively refine the calibration vector obtained from the LS calibration method. Furthermore, to address the shortcomings of conventional denoising diffusion probabilistic model (DDPM) training architectures, we introduce an innovative bridge-based diffusion model that maps the distribution of LS calibration vectors to their perfect counterparts. The proposed diffusion neural network architecture employs a conditional generative process, integrating a message passing neural network (MPNN) to incorporate domain-specific calibration insights. Numerical results demonstrate the superior performance of our proposed calibration method compared to existing methods, with supplementary experiments and in-depth analyses confirming the efficacy of the proposed successive refinement design.

Beamforming enhances signal strength and quality by focusing energy in specific directions. This capability is particularly crucial in cell-free integrated sensing and communication (ISAC) systems, where multiple distributed access points (APs) collaborate to provide both communication and sensing services. In this work, we first derive the distribution of joint target detection probabilities across multiple receiving APs under false alarm rate constraints, and then formulate the beam selection procedure as a Markov decision process (MDP). We establish a deep reinforcement learning (DRL) framework, in which reward shaping and sinusoidal embedding are introduced to facilitate agent learning. To eliminate the high costs and associated risks of real-time agent-environment interactions, we further propose a novel digital twin (DT)-assisted offline DRL approach. Different from traditional online DRL, a conditional generative adversarial network (cGAN)-based DT module, operating as a replica of the real world, is meticulously designed to generate virtual state-action transition pairs and enrich data diversity, enabling offline adjustment of the agent’s policy. Additionally, we address the out-of-distribution issue by incorporating an extra penalty term into the loss function design. The convergency of agent-DT interaction and the upper bound of the Q-error function are theoretically derived. Numerical results demonstrate the remarkable performance of our proposed approach, which significantly reduces online interaction overhead while maintaining effective beam selection across diverse conditions including strict false alarm control, low signal-to-noise ratios, and high target velocities.

Accurate sensing channel estimation is fundamental to high-performance integrated sensing and communication (ISAC), as it supplies critical information for target detection and localization. Despite extensive research, most existing approaches rely on the unrealistic assumption of ideal hardware conditions. However, hardware impairments are often inevitable due to the use of cost-efficient circuit components. This highlights the necessity for robust estimation techniques that remain reliable under imperfect conditions. To this end, we propose a self-supervised model-fusion network (SMF-Net) tailored for sensing channel estimation in hardware-impaired ISAC systems. To suppress distortions induced by hardware non-idealities, we design a two-stage cascaded convolutional neural network that leverages the spectral bias of neural networks, i.e., their tendency to learn high response frequency details in shallow layers and low frequency information in deep layers, to better separate different types of distortions present in corrupted channel estimates. By analyzing domain-specific features of the distorted channel components, we introduce a hybrid-domain denoising strategy that effectively exploits spatial correlations and angular sparsity inherent in the channel model. Furthermore, the framework is trained in a self-supervised manner, obviating the need for clean channel labels. Theoretical analysis validates the effectiveness of the proposed method and demonstrates that the self-supervised training strategy can match the performance of its supervised counterpart given a sufficiently large training set. Numerical results confirm the superiority of the proposed SMF-Net across various challenging scenarios, including severe nonlinear distortions, low transmission power, and limited training data.

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 propose a covert air-ground transmission scheme for integrated sensing and communication (ISAC), where the sensing signal can be utilized as a mask to disrupt the detection of communication by Willie. To further enhance the covertness, a full-duplex (FD) unmanned aerial vehicle (UAV) user is deployed to receive the covert signal while transmitting the artificial jamming to confuse Willie. The minimum detection error probability (MDEP) is first calculated by deriving the optimal detection threshold, and the analytic expression of average MDEP is obtained. Then, the covert transmission rate is maximized while satisfying the target detection constraint, the covertness constraint as well as the transmit power constraint, which can be resolved by an alternating optimization algorithm. Finally, simulation results are presented to demonstrate that the proposed scheme with the FD jamming can better guarantee the covertness of air-ground ISAC.

Although neural network-based speech enhancement models perform much better than their traditional counterparts, their substantial computational demands make it challenging for real-time applications on edge devices. Moreover, compact models often exhibit weak generalization in complex and out-of-domain scenarios. In this paper, we propose an efficient model based on our previous work Split-and-Merge Recurrent-based UNet (SMRU). The proposed model achieves a significant reduction in computational load through the incorporation of Skip-RNN layers and an attention-based sub-band compression module. Moreover, the employment of a two-stage uncertainty-driven loss function for aleatoric uncertainty capture leads to enhanced generalization and denoising performance without increasing the computational complexity during inference. Experimental results demonstrate that our model not only surpasses the original SMRU but also outperforms recently proposed lightweight models with similar computational cost (approximately 200M MACs). Furthermore, our model exhibits strong generalization in cross-corpus test sets, making it a promising solution for real-time speech enhancement applications.

A two-terminal lossy compression problem motivated by semantic communication is investigated, in which the semantic source is invisible at the encoders, two syntactic sources correlated with the semantic source are observed and compressed separately by two encoders, and a central decoder expects to reconstruct the semantic source and these two syntactic sources. The rate region of this semantic-aware quadratic Gaussian two-terminal source coding problem is characterized under the μ-sum assumption. This rate region is achieved by the Gaussian Berger-Tung coding scheme. The converse is proved by splitting the weighted-sum-rate optimization problem into sum-rate problem, CEO problem, and one-help-one problem to demonstrate the Gaussian optimality of weighted sum rate. This splitting method reveals the connection between the characterization of the whole rate region and the characterization of partial bounds, such as the sum-rate bound and the one-help-one bound.

This paper addresses the compressive sensing (CS)-based frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) downlink channel estimation problem in dynamic scenarios. We propose a multipath angle of arrival (AoA) and angle of departure (AoD)-based shared dictionary learning (MASDL) algorithm, where the discriminative and shared features in the angular domain are exploited via supervised dictionary learning, enhancing the generalization ability of the model. Simulation results show that the proposed algorithm achieves better normalized mean square error (NMSE) performance and substantially reduces the pilot overhead compared with other channel estimation schemes.

The quality of channel state information (CSI) feedback is critical for maximizing the spectral efficiency of massive multiple-input multiple-output systems. With multiple antenna arrays, the overhead of direct CSI feedback in frequency division duplex mode is usually large, and many CSI compression techniques have been proposed to alleviate this problem. Deep learning (DL) has achieved tremendous strides in CSI feedback. However, most current DL-based CSI compression methods utilize fully connected layers to achieve dimensionality reduction, which may be suboptimal for network optimization and result in noteworthy information loss and reduced CSI reconstruction accuracy. In this paper, we propose a novel recursive discretization compression framework with a selective state space model for CSI feedback, namely CsiMamba-RDC. The framework employs improved residual vector quantization to recursively refine CSI representation, reducing information loss and storage overhead. Additionally, we present an encoder-decoder model leveraging a selective state space model to extract diverse channel features.

Accurate channel state information (CSI) is critical for signal detection and precoding design in massive multiple-input multiple-output (MIMO) systems. However, traditional channel attention mechanisms for CSI feedback heavily rely on global pooling methods, which overlook finer-grained statistical patterns. In this paper, we propose a novel CSI feedback scheme for massive MIMO systems by utilizing a differentiable histogram attention mechanism, named DHANet, to boosts channel feature extraction and improve system performance. Specifically, DHANet replaces the traditional global pooling operations in the Squeeze-and-Excitation Block with kernel density estimation (KDE)-based differentiable histogram feature extraction, thereby enabling the capture of detailed channel-specific statistical information for more efficient CSI feedback. Moreover, the proposed mechanism can be seamlessly integrated into the existing CSI feedback architectures. Simulation results demonstrate that the DHANet achieves superior performance compared to the traditional pooling-based methods, particularly under 1/8 compression rates, highlighting its significant potential for CSI feedback in massive MIMO systems.

Offloading computing tasks to edge servers can provide better user experience. However, the deployment of a large number of distributed edge nodes brings challenges to the optimal management of network energy consumption. In this paper, deep reinforcement learning (DRL) is used to optimize unmanned aerial vehicle (UAV)-assisted edge computing task offloading to improve network energy efficiency. First, different edge nodes are treated as agents in the DRL network, so that the optimization of edge computing task offloading strategy is transformed into a multi-agent optimization problem. In addition, meta-learning is introduced into the multi-agent deep deterministic policy gradient algorithm to cope with the dynamics and uncertainty of the edge computing network environment. The simulation results show that the task offloading strategy based on meta-learning can not only quickly adapts to the dynamic changes of the environment and tasks, but also outperforms the benchmark algorithms in network energy efficiency.

Weakly supervised video anomaly detection (WSVAD) often relies on Multiple Instance Learning (MIL). However, selecting only the most discriminative segments for training limits the model's ability to comprehensively detect anomalous events, particularly hard anomalies. To overcome this limitation, we propose the Discriminative Score Suppression (DSS) module. This module suppresses the discriminative scores of the most prominent anomalies, shifting the model's attention to less obvious but important hard anomalies. This approach guides the model to learn the critical features of hard anomalies, enabling a more comprehensive detection of anomalous events. Additionally, the Anomaly Score Refinement (ASR) module constructs a dissimilarity-based classifier by storing normal patterns as prototypes, and integrates this with a neural network classifier. By combining the anomaly scores from both classifiers, more accurate detection of true hard anomalies is achieved. A score-sensitive inner-bag loss function not only adjusts penalties based on anomaly scores but also ensures that the model avoids erroneous selections. Our method accurately detects various anomalies, including challenging and multi-segment anomalies, while minimizing false positives for normal events. Extensive experiments show that the proposed framework outperforms state-of-the-art methods on the UCF-Crime and XD-Violence datasets.

With the help of unmanned aerial vehicles (UAVs), mobile ultrahigh definition (UHD) live broadcast systems can not only provide UHD images from multiple angles, but also monitor situations in live broadcast scenes in real time, thus providing a better user experience. However, owing to the restricted flight and edge computing capabilities of UAVs, how to optimise the assignment strategy when multiple UAVs perform tasks collaboratively becomes pivotal for system performance. In this paper, we optimise the multi-UAV collaborative task assignment strategy by improving the wolf pack algorithm (WPA). The strategy incorporates the ideas of crossover, replication, and mutation in genetic algorithms in the process of position updating, and improves the update mode of new population individuals. In addition, the strategy introduces the idea of auction algorithm to correct the infeasible solution that violates the constraints to obtain the optimal strategy. The simulation results show that the proposed algorithm can effectively solve the collaborative task assignment problem with better stability and convergence.

Cell-free is seen as an important implementation for future wireless networks, which eliminates the conventional ‘cell’ concept and enables wide deployment. However, previous works mostly ignore the asynchronous effects in such a large distributed antenna system and assume perfect synchronization which is not practical. In this paper, we proposed a user-centric alignment transmission (UCAT) to settle this problem, which has the analytical beamforming vectors in each access point (AP) being computed locally and fits user-centric cell-free well. With cooperative center processing unit power optimization and AP beamforming computation, an asynchronous downlink method is obtained, and finally, numerical results demonstrate the effectiveness of UCAT.

This article studies the secrecy performance for a dual-hop nonorthogonal multiple access (NOMA)-aided unmanned aerial vehicle (UAV) network in the face of a passive untrusted far user (UFU). A new artificial noise (AN) scheme is proposed, where AN generated in the first hop can be used to encrypt confidential signals by XOR operation in the second hop. Based on the proposed AN (PAN) scheme, we analyze the exact and asymptotic outage probabilities (OPs) for both NOMA users and intercept probability (IP) for trusted near user (TNU). Simulation results verify the correctness of our theoretical analysis and demonstrate that the PAN scheme significantly improves the security for TNU at the cost of negligible reliability of UFU compared to the benchmark schemes.

Internet of Vehicles (IoV) has become the key technology to improve road safety and traffic efficiency. However, with the explosion of the number of vehicles and more frequent authentication, computing and communication costs increase. Nevertheless, most of the traditional IoV authentication protocols lack scalability and are vulnerable to physical attacks and internal attacks, so they are difficult to meet the needs of modern IoV environment. To address these issues, this paper proposes a hierarchical mutual authentication protocol for the IoV based on physical unclonable function (PUF) and fog computing. In this protocol, we design a three-layer architecture for IoV supported by fog computing, where the fog node (FN) acts as an intermediate authentication layer, managing a group of roadside units (RSUs) and sharing the computational tasks of the trusted authority (TA) to alleviate its burden. Moreover, PUFs are embedded in entity devices to encrypt sensitive parameters, preventing internal data leakage. Based on this architecture, our protocol implements both vehicle-to-infrastructure (V2I) authentication and group authentication. In the V2I authentication, the FN distributes session keys in bulk to a group of RSUs and multiple vehicles, significantly reducing the repetitive authentication overhead between vehicles and RSUs. In the group authentication, the RSU authenticates vehicles and distributes group key, thereby avoiding the need for frequent authentication. We have also implemented fast updates of session keys and group keys, independently completed by the FN and RSU, reducing reliance on the TA and enhancing key security. We have conducted both formal and informal security analyses of the proposed protocol and used the ProVerif tool to verify its security. The results demonstrate that the protocol meets the security requirements needed for IoV. The evaluation results shows that the proposed protocol can significantly reduce the computation and communication overhead, and improve the overall performance of the system.

In the dynamic moving process of vehicle clusters, there are several challenges, including inefficiencies, cross-domain trust issues and privacy leakage. To address these issues, we propose a blockchain-assisted group and cross-domain authentication key agreement, which implements distributed key management based on a threshold key sharing scheme, and realizes group authentication and group key distribution for vehicle clusters through the collaboration of roadside units (RSUs) and Key Generation Center (KGC). Meanwhile, a cross-domain trust chain is constructed based on blockchain to accomplish secure cross-domain authentication and key agreement without the participation of the original KGC, which solves the problem of trust deficiency and single-point vulnerability in the process of cross-domain communication. Finally, we employed Real-or-Random (ROR) formal security analysis and the ProVerif tool to verify that the proposed authentication scheme, the results show that the proposed scheme is secure and superior to existing schemes in terms of communication and computational overhead.

The Internet of Vehicle (IoV) is a core component of smart city development. However, data interactions between IoV entities involve personal privacy, and once maliciously attacked, they may threaten the stable operation of the entire transportation system. Traditional authentication protocols suffer from high computational and communication overheads and are vulnerable to various threats, including physical attacks, entity impersonation, and replay attacks. Moreover, due to their reliance on centralized trusted authority(TA), traditional protocols are prone to single-point failures, especially when handling large-scale vehicle access. To address these challenges, this paper proposes a lightweight mutual authentication protocol based on physical unclonable function(PUF), which not only ensures vehicle anonymity and traceability but also supports a pseudonym update function after authentication. The protocol employs an architecture in which the main TA(MTA) is responsible for registration and data storage, while the sub-TA(STA) handles authentication, thereby effectively mitigating the risk of a single-point failure. Additionally, to counter the exposure of a large number of challenge-response pairs in traditional PUF-based authentication—making them susceptible to machine learning(ML)-based modeling attacks—this paper introduces a CUBE-PUF scheme based on digital Rubik’s Cube and random numbers. This approach enhances response unpredictability and randomness. We conduct both formal and informal security analyses of the proposed protocol and rigorously verify its security using the ProVerif verification tool. Furthermore, comparative evaluations with existing protocols demonstrate that our approach significantly reduces communication and computational overhead while offering enhanced security.

Frequency-hopping enhanced repetition (FHER) scheme, which provides time diversity and frequency diversity, has been considered a promising approach to meet the ultra-high reliability requirement of ultra-reliable and low latency communications (URLLC) in Grant-free (GF) random access networks. However, the effects of slow fading may significantly reduce the diversity gains offered by an FHER scheme, and such effects so far have not been considered in most studies. Moreover, practical designs for FHER patterns tailored to URLLC remain underexplored. In this paper, we employ a multi-connectivity (MC) scheme to overcome the effects of slow fading and enhance reliability through space diversity. We then propose a novel FHER pattern design to meet both low latency and high reliability requirements. To support more UEs with a limited number of subchannels, we further propose an FHER pattern assignment scheme that allows pattern reuse by distant UEs while mitigating the effects of slow fading. The performance of the proposed scheme is then analyzed and approximated in closed-form expressions under both selection combining (SC) and maximum ratio combining (MRC). Simulation results show that the proposed scheme significantly increases the number of successfully served UEs compared to the K-repetition schemes. Furthermore, the simulation results closely match the analytical results under SC, while the MRC-based analysis provides a tight reliability approximation when the number of associated base stations (BSs) is small (as it is in practice).

This article presents a novel concept termed integrated imaging and wireless power transfer (IWPT), wherein the integration of imaging and wireless power transfer functionalities is achieved on a unified hardware platform. IWPT leverages a transmitting array to efficiently illuminate a specific Region of Interest (ROI), enabling the extraction of ROI’s scattering coefficients while concurrently providing wireless power to nearby users. The integration of IWPT offers compelling advantages, including notable reductions in power consumption and spectrum utilization, pivotal for the optimization of future 6G wireless networks. As an initial investigation, we explore two antenna architectures: 1) a fully digital array and 2) a digital/analog hybrid array. Our goal is to characterize the fundamental tradeoff between imaging and wireless power transfer by optimizing the illumination signal. With imaging operating in the near-field, we formulate the illumination signal design as an optimization problem that minimizes the condition number of the equivalent channel. To address this optimization problem, we propose an semi-definite relaxation-based approach for the fully digital array and an alternating optimization algorithm for the hybrid array. Finally, numerical results verify the effectiveness of our proposed solutions and demonstrate the tradeoff between imaging and wireless power transfer.

Deep learning (DL) techniques have been extensively utilized to tackle challenges in the field of wireless communication, overcoming the limitations of traditional methods. However, training DL algorithms often requires large amounts of data, which is difficult to obtain in increasingly complex communication environments. Reducing the amount of data required for DL training is therefore an urgent problem to be solved. In this work, we develop a denoising diffusion probabilistic model (DDPM)-based digital twin (DT) framework of integrated sensing and communication (ISAC) multiple-input-multiple-output (MIMO) channel to address the data scarcity issue commonly found in DL-based scenarios. By sampling a small amount of data, our framework captures and simulates the data distribution, building a virtual data repository that can continuously provide samples to assist in executing control instructions to physical entities, even as the user equipment (UE) and target positions change. Specifically, we formulate the data generation problem as a distribution approximation task guided by the Kullback-Leibler (KL) divergence criterion and optimize it by meticulously designing a DDPM network composed of U-Net structure, time-embedding modules, and attention mechanisms. Moreover, we enhance the framework by formulating a task-driven objective function for two applications: 1) sensing channel estimation and 2) target detection. Numerical results demonstrate the superiority of our proposed DDPM-based DT framework compared with other data augmentation techniques in improving the performance of data-driven DL-based tasks, showcasing its robustness across diverse scenarios.

Cache-assisted ultradense mobile-edge computing (MEC) networks are a promising solution for meeting the increasing demands of numerous Internet of Things mobile devices (IMDs). To address the complex interferences caused by small base stations (SBSs) deployed densely in such networks, this article exploits the combination of orthogonal frequency-division multiple access (OFDMA), nonorthogonal multiple access (NOMA), and base station (BS) clustering. Additionally, security measures are introduced to protect IMDs’ tasks offloaded to BSs from potential eavesdropping and malicious attacks. Within this network framework, a computation offloading scheme is proposed to minimize IMDs’ energy consumption while considering constraints, such as delay, power, computing resources, and security costs, optimizing channel selections, task execution decisions, device associations, power controls, security service assignments, and computing resource allocations. To solve the formulated problem efficiently, we develop a further improved hierarchical adaptive search (FIHAS) algorithm, providing some insights into its parallel implementation, computation complexity, and convergence. Simulation results demonstrate that the proposed algorithms can achieve lower total energy consumption and delay compared to other algorithms when strict latency and cost constraints are imposed.

Due to the sharing of communication and sensing signals, integrated sensing and communication (ISAC) systems are vulnerable to potential eavesdropping attacks. This article investigates simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled secure ISAC for Internet of Everything (IoE) networks. Two typical ISAC scenarios are explored: 1) communication enhancement (CE) and 2) communication-sensing fusion (CSF). In the CE scenario, the communication user (CU) is located in the Non-Line of Sight (NLoS) region of the ISAC base station (ISAC-BS), while the sensing target is located in the Line-of-Sight (LoS) region of the ISAC-BS. In the CSF scenario, both the CU and the sensing target are located in the NLoS region of the BS. The STAR-RIS is deployed to improve communication and sensing performance. The artificial noise (AN)-aided robust and secure beamforming problems are formulated for different scenarios based on imperfect channel knowledge. Several STAR-RIS protocols are also considered to provide a more complete system design and performance analysis. Efficient iteration-based algorithms are developed to solve these nonconvex and highly coupled problems. Based on simulation results, we observe that: 1) the proposed methods can effectively improve the system performance and 2) from the perspective of communication security, the dedicated AN signal is helpful for STAR-RIS-enabled ISAC systems.

This paper investigates the challenge of acquiring channel state information at the transmitter (CSIT) in cell-free massive multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems operating in time-division duplex (TDD) mode. Although channel state information at the receiver (CSIR) is readily obtainable and CSIT is typically assumed to be its transpose, imperfections in the radio frequency (RF) chains disrupt this reciprocity. Focusing on this issue, we establish the necessary and sufficient conditions characterizing RF chain imperfections and their impact on system performance in a simplified scenario, underscoring the criticality of channel calibration. To address this challenge, a distributed source coding (DSC)-based calibration framework is proposed, leveraging the multiplexing of the sensing task to eliminate any additional communication overhead. This framework comprises a distributed compression scheme at each slave access point (AP) and a joint aggregation scheme at the central process unit (CPU). To validate the proposed DSC-based calibration framework, we analytically derive the performance gap relative to the fully collaborated approach. Building on this, a novel data-driven DSC-based deep learning method is proposed to address channel calibration without requiring clean labels. Numerical results demonstrate significant improvement in calibration performance achieved by our proposed method compared to existing calibration methods, approaching the performance of the fully collaborated method.

In quasi-static wireless channel scenarios, the generation of physical layer keys faces the challenge of invariant spatial and temporal channel characteristics, resulting in a high key disagreement rate (KDR) and low key generation rate (KGR). To address these issues, we propose a novel reconfigurable intelligent surface (RIS)-aided secret key generation approach using an autoencoder and K -means quantization algorithm. The proposed method uses channel state information (CSI) for channel estimation and dynamically adjusts the reflection coefficients of the RIS to create a rapidly fluctuating channel. This strategy enables the extraction of dynamic channel parameters, thereby enhancing channel randomness. Additionally, by integrating the autoencoder with the K -means clustering quantization algorithm, the method efficiently extracts random bits from complex, ambiguous, and high-dimensional channel parameters, significantly reducing KDR. Simulations demonstrate that, under various signal-to-noise ratios (SNRs), the proposed method performs excellently in terms of KGR and KDR. Furthermore, the randomness of the generated keys is validated through the National Institute of Standards and Technology (NIST) test suite.

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolation metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the identification rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. The codes of this paper can be downloaded from Github:https://github.com/happy-boy-cx/IntML-KNN.

Integrating wireless sensing capabilities into base stations (BSs) has become a widespread trend in the future beyond fifth-generation (B5G)/sixth-generation (6G) wireless networks. In this paper, we investigate intelligent reflecting surface (IRS) enabled wireless localization, in which an IRS is deployed to assist a BS in locating a target in its non-line-of-sight (NLoS) region. In particular, we consider the case where the BS-IRS channel state information (CSI) is unknown. Specifically, we first propose a separate BS-IRS channel estimation scheme in which the BS operates in full-duplex mode (FDM), i.e., a portion of the BS antennas send downlink pilot signals to the IRS, while the remaining BS antennas receive the uplink pilot signals reflected by the IRS. However, we can only obtain an incomplete BS-IRS channel matrix based on our developed iterative coordinate descent-based channel estimation algorithm due to the “sign ambiguity issue”. Then, we employ the multiple hypotheses testing framework to perform target localization based on the incomplete estimated channel, in which the probability of each hypothesis is updated using Bayesian inference at each cycle. Moreover, we formulate a joint BS transmit waveform and IRS phase shifts optimization problem to improve the target localization performance by maximizing the weighted sum distance between each two hypotheses. However, the objective function is essentially a quartic function of the IRS phase shift vector, thus motivating us to resort to the penalty-based method to tackle this challenge. Simulation results validate the effectiveness of our proposed target localization scheme and show that the scheme’s performance can be further improved by finely designing the BS transmit waveform and IRS phase shifts intending to maximize the weighted sum distance between different hypotheses.

Cell-free massive multiple-input multiple-output (MIMO) is a promising technology to address the requirements for higher spectral efficiency and energy efficiency in 6G networks. Downlink beamforming scheme, essential for mitigating multiuser interference and enhancing overall system performance, relies on the estimated uplink channel state information (CSI) in time-division duplex (TDD) mode exploiting channel reciprocity. However, hardware impairments render the bi-directional channel non-reciprocal. This paper focuses on channel calibration for cell-free massive MIMO systems, taking into account both radio frequency (RF) mismatches and nonlinear distortions. We derive the closed-form expression for downlink achievable rate within a specific calibration scheme. To address the calibration challenge, we introduce a novel conceptual model, in which the calibration vector is determined by optimizing the performance of the reference antenna. Expanding on this concept, we propose a novel digital twin (DT)-enabled approach to overcome the limitations in the conceptual model, where the DT model is established to perform calibration task by introducing DT services of virtual reference antennas. By exploiting this method, the calibration vector is computed utilizing the proposed alternating optimization algorithm within the DT model, obviating the need for deploying reference antennas in the real cell-free system, thereby reducing costs. The communication overheads and computation complexity for updating the calibration vector is proportional to the access point (AP) number. Simulation results demonstrate the significant improvement of system performance through channel calibration and verify the higher downlink throughput of our proposed DT-enabled calibration method compared to the existing calibration methods.

The deployment of extremely large-scale antenna array (ELAA) brings higher spectral efficiency and spatial degree of freedom, but triggers issues on near-field channel estimation. Inspired by the success of deep learning (DL) in far-field channel estimation, this paper proposes a novel spatial-attention-based method to reconstruct extremely large-scale MIMO (XL-MIMO) channel. Initially, the spatial antenna correlation in near-field channels is drawn as the expectation over spatial region, different from only over spatial angle in far-field channels. The spatial antenna correlation implies that the near-field channel exhibits spatial nonstationarity, that the inter-antenna correlation vary with the antenna index and spatial regions and reveals the weakness of the widely applied convolutional neural network (CNN) with fixed receptive field. Subsequently, we develop a multi-scale spatial attention network (MsSAN) with low computational cost to enhance near-field MIMO channel estimation. In MsSAN, the channel is refined to subchannels of different scales layer by layer and each subchannel is treated as a whole and the spatial attention (SA) map is calculated by the sum of dot products of inter-subchannel so that the complexity grows linearly with channel size. Simulation results are presented to validate the proposed MsSAN with low computational cost outperforms others in terms of near-field channel reconstruction.

Extremely large-scale multiple-input multiple-output (XL-MIMO) wideband systems may exhibit the severe delay spread, due to its spatial- and frequency-wideband (dual-wideband) effects. The typical orthogonal frequency division multiplexing (OFDM) technology have to insert a larger number of cyclic prefix (CP) to overcome the inter-symbol interference (ISI) induced by delay spread. The additional CP overhead will counteract the improvement of spectral efficiency by the large antenna array. To address the issue, this paper proposes a structured OFDM (SOFDM) modulation approach to reduce the CP overhead for wideband XL-MIMO systems with dual-wideband effects. As the ability to perform SOFDM is affected by the antenna architecture, we study the modulation technique considering different antenna structures, including fully-digital, phase shifter-based hybrid array, and dynamic metasurface antenna (DMA) architectures. Specifically, we first provide a mathematical model to represent a near-field channel with dual wideband effects. Based on the channel model, we develop the SOFDM modulation and then propose a joint spatial precoding and frequency domain equalization scheme to maximize the system spectral efficiency, where the solutions of precoding/combining and equalization matrices are derived for the three types of antenna array architectures. Numerical simulations indicate that the proposed scheme can effectively deal with the dual-wideband effects and significantly improve the spectral efficiency with low CP overhead.

For an uncrewed aerial vehicle (UAV)-aided energy-harvesting non-orthogonal multiple access (EH-NOMA) network, where an untrusted far user (UFU) overhears the confidential transmissions from base station (BS) to the trusted near user (TNU), how to ensure the security of TNU and achieve the reliability of UFU becomes an open issue. The traditional artificial noise (TAN) scheme just guarantees the security of TNU without focusing on the reliability of UFU, while the non-artificial noise (NAN) scheme only ensures the reliability of UFU but sacrifices the security of TNU. Thus, the TAN and NAN schemes are unable to guarantee the fairness between TNU and UFU. To this end, we employ digital network coding to encrypt confidential signals with artificial noise signals, and the proposed artificial noise (PAN) scheme not only protects the confidential transmissions of TNU but also guarantees the reliability of UFU. We use security-reliability tradeoff (SRT) and interference-reliability tradeoff (IRT) to evaluate the performance of TNU and UFU, respectively. Additionally, we derive the exact and asymptotic expressions of outage probabilities for both TNU and UFU, and the intercept probability for TNU. Numerical results verify the accuracy of our theoretical results and demonstrate that: 1) the SRT and IRT performance of the PAN scheme is better than that of the TAN and NAN schemes; 2) the PAN scheme achieves better secrecy for TNU compared to the TAN scheme. Meanwhile, the reliability of UFU in the PAN scheme is almost the same as the NAN scheme, indicating the PAN scheme simultaneously realizes the security of TNU and the reliability of UFU and the fairness of NOMA users is achieved by the PAN scheme.

The deployment of extremely large-scale MIMO (XL-MIMO) and short-wavelength signaling enhances communication capabilities and improves spectrum efficiency for future sixth-generation (6G) wireless communication. However, users may potentially be located in the near-field region due to the sharp increase in antenna array aperture. In the near-field region, the signal wave is spherical wave. Thus, the consideration of spatial angle and distance requires the development of novel channel estimation algorithms to reduce codebook overhead. This paper develops a novel scheme based on a low-size adaptive codebook to reconstruct the near-field channel. Initially, it is investigated that the angle spread for one channel path component is confined to a certain angular spatial region, which demonstrates the sparsity inherent in angular domain. Exploiting the angular sparsity inherent, we propose a novel adaptive joint sparse Bayesian learning (JSBL) estimation algorithm on all subcarriers to cater to reduce the codebook size. The proposed algorithm captures all spatial angular sparse information and then refines distance information so that the measurement codebook size only depends on the spatial angular resolution. Further, the proposed adaptive JSBL approach is extended to estimate the time-varying near-field channel. Moreover, Bayesian Cramér-Rao Bounds (BCRBs) are derived for quasi-static and temporal scenarios. Numerical simulations are presented to demonstrate that our approaches with low codebook overhead outperform other algorithms based on the angular-domain and polar-domain codebooks.

The deployment of extremely large-scale arrays and high-frequency signaling holds the potential to enhance communication capabilities and improve spectrum efficiency. However, channel estimation faces challenges due to the simultaneous consideration of spatial angles and distances, leading to storage constraints and energy spread. To cope with this issue, we analyze the sparsity inherent in beamspace domain representation and introduce an adaptive codebook scheme for extremely large-scale massive MIMO (XL-MIMO) channels. In this work, we transform multi-band channel estimation to sparse matrix recovery problem. Then, a novel adaptive joint sparse Bayesian learning algorithm is proposed to capture the angular-domain information and refine distance information iteratively without increasing codebook overhead for XL-MIMO channel estimation. Simulation results demonstrate our approach outperforms other algorithms based on the sampling angular-distance domain codebook with low codebook overhead.

Dual-functional radar-communication (DFRC) is a promising direction in the future integrated sensing and communication system. The joint radar and communication (JRC) beamforming scheme is recently developed in DFRC systems. To address the JRC beamforming challenge, conventional approaches predominantly rely on convex optimization methods, which severely depend on precise channel estimation and entail a high computational complexity. Motivated by this, a deep learning-based optimization approach is investigated for tackling the JRC beamforming problem. To enhance the overall performance, we design a deep alternating neural network architecture. Simulation results verify that our proposed method guarantees the required sensing performance and outperforms numerical algorithms in terms of the average data rate of communication users.

This paper concentrates on addressing the challenging problem of sensing channel estimation in cell-free integrated sensing and communication (ISAC) multiple-input multiple-output (MIMO) system. This challenge arises from the complex mixture of signals from both the direct sensing channel and target reflected sensing channel. To tackle this challenge, we introduce the digital twin (DT), as a powerful tool to exploit and characterize the inherent features of the target sensing channel by sampling data from the real world and interacting with it. To be specific, the DT model, designed as a generative adversarial network (GAN), is trained to be capable of generating the desired results from the coarse observations, where the distribution of the sensing channel in a particular cell-free ISAC system is implicitly learned via the adversarial process. With this basis, we propose a novel digital-twin-enabled channel estimation (DTE-CE) approach to enhance the performance of channel estimation, where the DTE-CE network is meticulously designed by utilizing the virtual channel matrix (VCM) model to facilitate the estimation process. Simulation results show the excellent performance of the proposed approach, as well as the effectiveness of our designed DTE-CE network, in terms of sensing channel estimation with different transmitting power and numbers of targets.

Massive access is recognized as one of the main use cases of future wireless networks. The characteristic of sporadic transmission in massive access makes the processes of channel estimation (CE) and active user detection (AUD) essential prerequisites for successful data decoding. In this article, we study joint CE and AUD in massive access system with cell-free structure. Specifically, we first establish the expectation maximization approximate message passing (EM-AMP) framework tailored for cell-free structure as a benchmark. Then, we investigate three new methods that exploit coarse user location information in different ways, namely, the variance bounding method, the variance fusion method, and the proposed EM on location method, where the last two methods can also generate finer location estimation as byproduct to CE and AUD at the cost of higher complexity. For single user scenario, we theoretically prove the optimality of the proposed method in channel variance estimation, validating the foundation of the proposed method. For multiuser scenario, we conduct various simulations to compare the performance of different methods. Our findings illustrate that harnessing coarse user location information yields substantial enhancements in CE and AUD performance. Moreover, the proposed method exhibits superior localization accuracy compared to the variance fusion method, all while maintaining comparable complexity, making it a good candidate for applications with both communication and sensing requirements.

The Internet of Vehicles (IoV) is a specific instance of the Internet of Things (IoT) in the transportation field, driven by application requirements, such as intelligent traffic services and automatic vehicle control, can improve road safety and enhancing transmission efficiency. However, highly open networks tend to bring more security threats, and secure authentication becomes an important guarantee for reliable communication. Traditional IoT authentication and key agreement methods are costly, inefficient, and rely on the third-party trusted institutions, making them unsuitable for direct application in IoV systems. To meet the security authentication needs of IoV, and improve authentication efficiency and anonymity, this article proposes a verifiable commitment-based mutual authentication and key agreement protocol, called mutual authentication and key agreement protocol based on verifiable commitment (VC-MAKA). In VC-MAKA, we construct a verifiable commitment scheme where the verifier can verify the committed secret. Furthermore, based on this verifiable commitment scheme, we implement secure authentication and session key agreement, allowing vehicles to freely negotiate secure session keys and achieving conditional anonymous protection. Additionally, the proposed VC-MAKA also achieves rapid session key updates, enhancing the security of the session keys. We have conducted formal and informal security analysis, and the results show that VC-MAKA meets security requirements, such as mutual authentication, anonymity, traceability, and untraceability. Moreover, we have used the ProVerif tool for security experiment and performance comparison analysis, and the results indicate that compared to other schemes, the VC-MAKA protocol offers higher security and better efficiency.

Massive multiple-input–multiple-output (MIMO) systems provide efficient connectivity services for a large number of industrial Internet of Things (IoT) devices. To support numerous IoT devices, this article considers downlink hybrid architecture massive MIMO systems with phase shifters-based and switches-based cases, in which the data stream is first sent to the digital domain and then processed by the analog domain. zero-forcing (ZF) receivers are considered under the assumption of perfect channel state information (CSI). For hybrid architecture MIMO systems under quantized phase shifters and switches, we derive asymptotic approximations of the spectrum efficiency (SE) for massive MIMO systems using random matrix theory and then we propose an iterative algorithm to generate corresponding analog processing matrix, which can be used arbitrary quantized phase shifters. With the help of derived theoretical SE and realistic power consumption model, we further evaluate the total energy efficiency (EE) and provide insights into the tradeoffs between the SE and total EE. Finally, Monte Carlo simulation results are presented to substantiate our theoretical analysis results and showcase that the hybrid architecture provides higher total EE compared to existing all-digital architectures, but its achievable SE is lower than that of the hybrid architecture. Furthermore, the results also show that the switchbased hybrid architecture have ability to achieve the enhanced EE performance with large number of base station antennas and low-signal-to-noise ratio regimes compared to quantized phase shifters.

Unmanned-aerial-vehicle (UAV)-assisted multiaccess edge computing (MEC) networks can effectively broaden the application scope of the Internet of Things (IoT) in complex scenarios, such as maritime operations, military communications, and emergency commands. However, uncertain factors, such as weather changes and temporary airspace control, pose great challenges to UAV flight safety. Obstacles resulting from these uncertain factors may intersect with UAVs with preplanned flight paths, leading to accidents. Therefore, generating the optimal flight trajectory to avoid these obstacles is key in the successful operation of this fuzzy system. In this article, we present a heuristic trajectory generation scheme for complex offshore environments that can generate optimal trajectories according to complex terrain conditions and avoid uncertain obstacles. First, we build a complex terrain model based on a 3-D offshore environment to simulate the conditions in UAV-assisted MEC networks. Second, we propose a network performance optimization objective function that is based on UAV characteristics. Third, we improve the existing ant colony optimization (ACO) algorithm by introducing chaotic mapping, polarizing the pheromone recording rule, and implementing a simulated annealing screening mechanism to efficiently generate trajectories. Finally, we design an efficient obstacle avoidance algorithm for different combinations of obstacle regions. The simulation results show that our proposed trajectory generation scheme can efficiently avoid obstacles and significantly improve the total trajectory loss rate compared with that of baseline schemes.

Internet of Vehicles (IoV) is a critical component of the transportation field, which can greatly facilitate the current transportation system. Meanwhile, more and more vehicles connect to the IoV and the security and privacy need to be guaranteed. Traditional authentication protocols based on bilinear pairs are computatively heavy and difficult to protect user identity and privacy in IoV environment. In addition, most existing protocols only consider the authentication between vehicles and infrastructure, but not consider between vehicles and vehicles, as well as single point of failure in the traditional single trusted authority (TA). To address these issues, this article proposes two lightweight mutual authentication protocols (MAPs) based on physical unclonable function (PUF) and multi-TA. The first protocol named V2I-MAP and is applied to vehicle-to-infrastructure (V2I) communication. The second is named V2V-MAP and is applied to vehicle-to-vehicle (V2V) communication. The protocols solve the interference of noise on PUF signals by fuzzy extractor, reduce the communication overhead and computation overhead of vehicles by utilizing PUF’s lightweight computation characteristics, deal with the problems of impersonation attack with the help of the unclonable characteristics of PUF, and work out single TA single point of failure problems with the multi-TA model. Finally, the security analysis and informal security analysis of the proposed protocols are demonstrates that the proposed protocols meet the security requirements of the IoV system. ProVerif is used to verify the security of the protocols. Performance analysis shows that the protocols can reduce the communication and computation overhead than the comparable protocols.

Ultradense networks are widely regarded as a promising solution to explosively growing applications of Internet of Things (IoT) mobile devices (IMDs). However, complicated and severe interferences need to be tackled properly in such networks. To this end, both orthogonal multiple access (OMA) and non-OMA (NOMA) are considered under base station (BS) clustering. Then, in order to attain a goal of green and secure computation offloading, under the proportional allocation of computation resources, and the constraints of latency and security cost, joint device association, channel selection, security service assignment, power control, and computation offloading are performed for minimizing the overall energy consumed by all IMDs. It is noteworthy that multistep computation offloading is concentrated to balance the network loads and fully utilize computation resources. Since the finally formulated problem is in a nonlinear mixed-integer form, it may be very difficult to find its closed-form solution. To solve it, an improved whale optimization algorithm (IWOA) is designed. As for this algorithm, the convergence, computation complexity, and parallel implementation are analyzed in detail. Simulation results show that the designed algorithm may achieve lower energy consumption than other existing algorithms under strictly satisfying constraints of latency and security cost.

Extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems introduce a new communication paradigm called near-field communications, which identifies users’ location within the near-field (Fresnel’s region). In the near-field, beams can be steered in the angle and distance dimensions, resulting in an enormous codebook and a prolonged two-dimensional beam alignment (BA) process. To keep a low BA overhead while achieving low BA error, in this paper, we design a novel hierarchical codebook and a BA scheme for near-field XL-MIMO systems. Specifically, we first propose a novel spatial partition where the angle-offset effect is revealed and leveraged to improve the beam gain inside the coverage area. Based on the partition, we design distance-coarse and focusing beams. Distance-coarse beams are leveraged to construct the high level of the codebook for angle dimension alignment. In contrast, focusing beams construct the last level codebook for distance dimension alignment. Corresponding to the proposed codebook structure, our BA scheme is a tree search consisting of two stages: the angle aligning stage and the distance aligning stage. Next, we formulate the desired codebook design problem as difference convex optimization problems, where three beam design guidelines are considered to minimize the BA error rate raised by the near-field angle-offset effect. After that, the proposed optimization problem is solved by the constrained concave-convex procedure. Numerical simulations verify the angle offset effect and our designed near-field beam. Furthermore, we show that our BA scheme only utilizes one percent of overhead but achieves a lower BA error rate than exhaustive searching.

Intelligent reflecting surface (IRS) is a promising new paradigm for enhancing wireless information transmission (WIT) and wireless power transfer (WPT) cost-effectively in the future. In this paper, we study an IRS-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid node (HN) operating in FD mode sends information signals to multiple devices in the downlink (DL), and meanwhile receives energy signals from a power station (PS) in the uplink (UL), both of which are assisted by an IRS. Our objective is to boost the weighted sum throughput by jointly optimizing the active transmit beamformer at the PS and HN, along with the passive reflection coefficients of the IRS. To deal with the formulated non-convex optimization problem with intricately coupled design variables, most of existing works employ the alternating optimization (AO) method, whose performance, however, is closely related to parameter initialization. In contrast, we develop two novel penalty-based algorithms for the single-device and multi-device cases, respectively. In particular, our proposed rank-one constraint reformulation method of matrix proves to be efficient, especially for the case where the objective function is a higher-order function of the IRS phase shifts. Numerical results demonstrate the superiority of our proposed design over benchmark schemes, and also unveil the necessity of the joint design of passive IRS beamforming and resource allocation for achieving better WPCN performance. Moreover, we draw useful insights into the fine-tuning of IRS deployment location in the studied WPCN.

Cell-free massive multiple-input multiple-output (MIMO) is known for its potential to enhance overall system performance. Thanks to the principle of channel reciprocity, it becomes possible to implement downlink beamforming by exploiting the estimated uplink channel in time-division duplex (TDD) mode. However, the assumption of perfect hardware conditions, as made in prior studies, is not reflective of practical realities. The involvement of hardware impairments disrupts this reciprocity, resulting in performance degradation. This paper investigates the impact of hardware impairments in downlink data transmission, where a novel model is established by jointly considering the radio frequency (RF) mismatches and nonlinear distortions. We first derive closed-form achievable user rate expressions and prove that the impact of RF mismatches vanishes as the number of access points (APs)$M \to \infty $in certain distributions of RF gains. Then, we study the scenarios when the number of user equipments (UEs)$K \to \infty $, as well as various degrees of hardware impairments’ severity scaling M. Finally, we introduce a channel calibration process and theoretically derive its performance, observing that in certain scenarios, the need for calibration becomes redundant as$M \to \infty $. These findings are further validated through numerical results, confirming the scaling laws derived in our study.

This work proposes an On-Off Keying (OOK) coding scheme for covert communication over complex Gaussian channels. In particular, a transmitter Alice employs phase deflection to covertly transmit information to a receiver Bob, simultaneously ensuring that the communication intent is concealed from a warden Willie. The utilization of phase deflection allows Alice to improve the transmission rate by leveraging Willie’s uncertainty about the received phase, without changing the codebook construction. Considering the asymmetry of the OOK codebook’s input distribution and shape constellation, we first analyze the relationship between the input distribution and the signal amplitude, and then propose a scheme that can achieve covert transmission with the input distribution of the “on” symbol$a_{n}=\mathcal {O}\left ({{\frac {1}{\sqrt {n}}}}\right)$and an average transmission power$\beta ^{2}=\mathcal {O}({1})$. We quantify the improvement brought from the phase resource as phase deflection gain and derive its closed-form expression by approximating the Kullback-Leibler (KL) divergence and mutual information through Taylor expansion. Numerical results show that our scheme achieves significant phase deflection gain, and the maximum gain can be achieved by fully utilizing the phase resources through three stages.

The deployment of large-scale arrays in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems has enabled highly accurate localization by reaping the benefits of high angular resolution. However, the use of massive antennas in mmWave systems results in expensive hardware costs and computational burdens. This paper considers mobile user localization in quantized mmWave MIMO systems, where each base station (BS) antenna is equipped with low-resolution analog-to-digital converters. The proposed approach integrates the beamspace model with off-grid information to capture channel sparsity in the angular domain. The temporal correlation of angle-of-arrival (AoA) is characterized by a Markov process for moving users. To estimate channel gains and time-varying AoAs, while keeping the computational complexity low, we further develop generalized approximate message passing and AoA tracking methods. In dense multipath environments, determining line-of-sight (LoS) paths for precise localization poses a challenge. To address this issue, we propose a fast direct localization based on LoS identification that can also be applied when the LoS paths of some BSs are obstructed. In the final stage, the moving user locations are recovered via triangulation. Simulation results validate the effectiveness of the proposed algorithms and showcase the feasibility of implementing quantized mmWave systems for localization purposes.

Nonorthogonal multiple access (NOMA) can facilitate simultaneous data transmissions towards multiple users by using the superposition coding and successive interference cancelation techniques, which can greatly improve the spectrum efficiency. Cooperative relaying and space-time coding can promisingly improve the communication robustness of poor-quality links by achieving the space-diversity gain. Energy harvesting (EH) can prolong the lifetime of energy-limited terminals and make them work continuously. In order to enhance the spectrum efficiency as well as the communication quality, we enable a cluster of EH relays to assist the data transmissions from a base station (BS) to two far-users by using the Alamouti coding based cooperative NOMA strategy. The relays are capable of harvesting wireless energy from a power beacon as well as BS by using time-switching or power-splitting method. According to the energy status and the data decoding status, one relay is selected in a distributed manner according to either Max-min, Max-sum, or Random criterion. We analyze the transmission success probability and the system throughput performance. Extensive simulations are performed to compare the performance of different EH-based space-time coded cooperative NOMA with various relay selection schemes as well as the counterpart orthogonal transmission schemes.

This paper investigates the distributed uplink for the hierarchically backhaul-linked cell-free network with distributed processors to maximize the advantages of jointly serving under the same time and frequency resources. It is validated in previous works that the performance of fully centralized uplink in a cell-free network overwhelms uplink methods without or with limited coordination. On the other hand, a fully centralized uplink requires extremely high costs on backhaul links and computation capacity on the central processing unit (CPU), which is impractical in a widely deployed large cell-free network. To handle the mentioned problems, the relation between centralized uplink and group sliced distributed uplink is revealed, firstly. With the uniform framework compatible with previous fully centralized and fully distributed minimal mean square error (MMSE) equalization, two theorems are derived as group-joint MMSE complementary and the column space equivalence, which indicate the relation between the local optimal and the global optimal and can include conclusions achieved in previous works. Both computation and backhaul signaling overheads are distributed among the whole network. Simulation results demonstrate the excellent performance of proposed methods based on the derived complementary kernel.

Distributed precoding is a meaningful topic in the cell-free massive multiple input multiple output system. This system faces challenges in performance degradation due to the absence of knowledge from other antennas and several realistic constraints brought by the distributed implementation of the communication system such as the presence of phase noise (PN). In this paper, a robust cascaded team minimum mean square error (RCT-MMSE) precoding based on a hierarchical fronthaul structure is exploited to handle distributed and robust precoding including not only PN but signaling noise, sharing cost constraints and channel aging uncertainty. Such RCT-MMSE precoding, characterized by its avoidance of iterations because we derive the analytic expressions, mitigates the need for high fronthaul level instantaneous information exchange. It also demonstrates scalability with distributed computation burden and flexible signaling overhead, which offers an advantageous performance-cost tradeoff. Simulation results demonstrate the effectiveness of RCT-MMSE to combat several practical constraints and provide a flexible distributed precoding framework compared with previous ones.

Ideally, the bi-directional channel in time division duplex (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems exhibits reciprocity. However, the involvement of low-cost and non-ideal radio frequency (RF) chains disrupts this reciprocity. Consequently, prior to fully leveraging the advantage of channel reciprocity, it is essential to implement channel calibration. Despite numerous over-the-air calibration methods, such as Argos, the typical least square (LS) are proposed in the literature, none of their criteria directly focus on the calibration performance. To address this gap, we propose a novel deep learning based approach that aims to optimize the calibration performance and introduce device-level intelligence towards 6G networks. To be specific, two cascaded modules are designed in a model-assisted end-to-end manner. Firstly, we propose the double-CNN-based channel denoising module for joint bi-directional channel estimation by exploiting the characteristics of mmWave channel. Secondly, the deep calibration learning module is meticulously designed to obtain the calibration coefficients with the aid of assisted model. This traceable assisted model is established by leveraging the expert knowledge of calibration process, based on which the MetrNet and the CaliNet are designed. Numerical results demonstrate the superior performance of our proposed method compared to existing calibration methods. Particularly, additional simulations and analysis are conducted to verify the effectiveness of the two properly designed modules.

Dual-functional radar-communication (DFRC) is a promising technology in future integrated sensing and communication systems. Since communication and sensing performance need to be taken into consideration for joint radar and communication (JRC) beamforming in the DFRC system, existing approaches mainly transform JRC beamforming problems into convex optimization problems and then solve them with classical convex solvers. These traditional solutions heavily rely on precise channel estimation and entail high computational complexity. In this paper, we investigate a deep learning-based optimization approach for JRC beamforming to enhance the spectral efficiency for communication users and guarantee the probability of detecting targets. To achieve better performance, we leverage the theoretical optimal structures of JRC beamforming and design an effective deep neural network architecture. To further reduce the computational burden in the training phase of neural network, we develope an improved orthogonal beamforming technique. Simulation results verify that our proposed algorithm guarantees the required sensing performance and outperforms numerical algorithms in terms of communication performance. The orthogonal beamforming technique achieves satisfactory performance with low computational complexity.

Channel estimation stands as a pivotal and challenging task for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication system, especially in a time-varying scenario, where exists a massive number of channel coefficients and severe propagation loss due to the Doppler shifts. Conventional estimation schemes may fail to track the fast varying channels and not be able to fully exploit the unique characteristics of mmWave channels in their model designs. In this work, we leverage the Generative Adversarial Networks (GANs) and meticulously design a novel framework named Homogeneous Denoising Generative Adversarial Network (HDnGAN) to tackle the challenge of time-varying channel estimation for mmWave MIMO system. Our framework incorporates the distinctive traits of mmWave channels, such as temporal and spatial correlations, as well as angular sparsity, into the network architecture design. Theoretically, a special case of our proposed HDnGAN with a linear structure is demonstrated to be not inferior to the linear minimum mean squared error (LMMSE) estimator. Numerical simulations underscore the superiority of HDnGAN over existing channel estimation methods, particularly in low signal-to-noise ratio (SNR) regions. Furthermore, it exhibits robustness across varying scenarios. Notably, it remains applicable in out-of-distribution situations and in the absence of ground truth.

Acquiring the wireless channel state information (CSI) is an essential task to reap the wireless system performance gain brought by intelligent reflecting surface (IRS). In this paper, we study an IRS-assisted wireless energy transfer (WET) network, where an energy receiver (ER) harvests the wireless energy transmitted from an energy transmitter (ET) with the help of an IRS. Different from the commonly adopted wireless CSI acquisition approaches such as pilot or codebook based methods, we propose a novel channel learning method that requires only one-bit feedback information from the ER. Specifically, each feedback bit indicates whether the increase or decrease of the harvested energy amount at the ER within the present interval as compared to the previous one. Based on the feedback information, the ET continually adjusts its transmit beamforming in subsequent channel learning intervals to help infer the cascaded ET-IRS-ER CSI. It is worth noting that an optimization technique named analytic center cutting plane method (ACCPM) is applied in the channel learning phase. Numerical results unveil that our proposed one-bit feedback based channel estimation method is able to effectively estimate the cascaded ET-IRS-ER channel, and greatly reduce the requirement on the hardware complexity of the ER simultaneously.

From practical considerations in massive multiple-input multiple-output (MIMO) systems, with the involvement of radio frequency (RF) chains, the channel reciprocity no longer holds even under time division duplex (TDD) operation. To fully leverage the advantage brought by TDD systems, channel reciprocity calibration needs to be necessarily investigated. In this paper, we propose the CNN-enhanced calibration method, which is composed of the channel estimation task and the calibration coefficient calculation task. Different from previous works, our method is based on our proposed double-CNN-based bi-directional channel estimator, which is designed specifically for the calibration problem to exploit the bi-directional channel correlation, the spatial correlation, and the angular correlation in millimeter wave (mmWave) channel. Based on this, a formulated LS calibration problem is solved. Numerical results manifest that our proposed method outperforms the existing calibration methods in the literatures.

In this paper, we consider an intelligent reflecting surface (IRS)-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) operating in FD mode sends information signals to a device in the downlink (DL) and meanwhile receives energy signals from a power station (PS) in the uplink (UL) with the help of an IRS. Our objective is to maximize the achievable data rate from the HAP to the device by jointly optimizing the transmit covariance matrix at the PS, the transmit beamforming vector at the HAP, and the phase shift vector at the IRS. The optimal transmit beamformer at the HAP is derived in closed from, and the joint optimization of the transmit covariance matrix at the PS and the phase shift vector at the IRS results in an intractable non-convex problem. To tackle this challenge, we propose an efficient penalty-based algorithm consisting of two layers. In the inner layer, we iteratively increase the device's signal-to-interference-plus-noise ratio (SINR) by applying the Dinkelbach's transform. While in the outer layer, we gradually decrease the penalty parameter. Numerical results demonstrate the superiority of our proposed design over benchmark schemes, and also unveil the necessity of the joint design of passive IRS beamforming and active beamforming for achieving better WPCN performance.

In this article, an intelligent reflecting surface (IRS)-assisted integrated satellite-unmanned aerial vehicle (UAV)-terrestrial (SUT) Internet of Things (IoT) network faced with a smart jammer under imperfect channel state information (CSI) conditions is considered. We propose a Stackelberg game model to describe the adversarial relationship between the satellite, UAV, and IRS and the jammer, which are modeled as the leader and the follower, respectively. For the follower subgame, the jammer aims to minimize the jamming power while guaranteeing that the jamming energy efficiency surpasses a certain threshold. Under this setup, the Angle of Arrival (AoA)-based discretization method is utilized to address the imperfect CSI issue. Then, the use-and-then-forget method and the Lagrangian function are employed to obtain a closed-form expression for the jammer’s power. Finally, a feasible jamming power solution is obtained by means of the Cauchy–Schwarz inequality. For the leader subgame, we aim to optimize the hybrid beamforming design of the satellite, UAV and IRS, with the goal of minimizing the total transmit power, while guaranteeing that the downlink received signal-to-interference-plus-noise ratio (SINR) surpasses the minimum communication threshold. We propose an alternating optimization scheme, in which the Cauchy–Schwarz inequality, the AoA-based discretization method, and nonsmooth penalty functions are employed to alternately obtain the optimal satellite beamforming vector, UAV beamforming vector and IRS phase matrix when the other variables are fixed. Through our analytical and numerical results, the proposed beamforming scheme achieves a reduction of 16.9% in average power consumption compared with other benchmark schemes when obtaining the same anti-jamming performance.

By offloading some computational tasks to the edge server, edge computing can help relieve the increasing computation burden of mobile users and improve the quality of experience of users. In this article, we investigate the computation offloading in edge computing-enabled multiuser cell-free (CF) multi-input multioutput (MIMO) networks with multiple building baseband units (BBUs). The user association, transmit covariances, CPU-cycle frequencies, and allocated bandwidths are jointly optimized to minimize two objectives: 1) the energy consumption of mobile devices (MDs) and 2) execution delay of tasks. We formulate it as a vector optimization problem and adopt the scalarization technique to transform it into a scalar mixed-integer nonlinear programming (MINLP). Then, to solve the MINLP, we introduce user sorting into the framework of branch and bound and propose a sorted MD association (MDA) method. Two key issues are tackled in the proposed MDA, i.e., the lower bound of the MINLP and the updation of incumbent solution. For the first issue, we present a Lagrangian dual relaxation algorithm based on subgradient projection, while the second one involving another nonconvex minimization problem, we propose a successive convex approximation (SCA)-based algorithm to solve it. Finally, extensive simulations demonstrate that the proposed method is able to reduce the system cost significantly and achieve better system performance by comparing with the benchmark schemes.

In this paper, we propose a robust max-min fairness transmission design for intelligent reflecting surface (IRS)-aided wireless network in the presence of user location uncertainty. In particular, the non-isotropic reflection property for the IRS element is considered. We investigate the joint design of the active transmit beamformer at the base station (BS) and the passive phase shift matrix along with the deployment orientation (facing/pointing direction) of the IRS for maximizing the worst-case minimum signal-to-interference-plus-noise ratio (SINR) received by the users. In order to show the potential gains obtained by adjusting the deployment orientation of the IRS, a single-input-single-output (SISO) system is studied where a closed-form signal-to-noise ratio (SNR) of the user is obtained. For the multi-user case, to solve the resulting non-convex problem, an inexact-alternating-optimization algorithm consisting of a double-loop iteration is proposed. Specifically, in the inner loop, an optimization problem with semi-infinite constraints needs to be solved to increase the worst-case min-SINR compared to the given SINR reference value. We first transform the semi-infinite constraints into linear matrix inequality (LMI) constraints with finite form by applying the Taylor expansion approximation method, the general S-procedure, and the general sign-definiteness lemma. Then an efficient alternating optimization (AO) algorithm based on the two-dimensional search method, negative square penalty (NSP) method, and successive convex approximation (SCA) technique is proposed. While in the outer loop, we update the given SINR reference value as the worst-case minimum SINR obtained after each inner loop iteration. The whole algorithm terminates when the updated SINR reference values converge. Simulation results demonstrate the effectiveness of the proposed algorithm, and also show the additional system performance gain brought by the optimization of the IRS deployment orientation compared to its counterpart with fixed IRS deployment orientation, especially for a smaller IRS element number and a more prominent non-isotropic reflection property of the IRS element.

Large antenna arrays and beamforming are necessary for the mmWave communication system, resulting in heavy time and energy consumption in the beam training stage. Therefore, dual-band operations are expected to be deployed in future communication systems, where low-frequency channels are used to meet basic communication needs, and millimeter wave (mmWave) channels are exploited when the high-rate transmission is required. Existing works utilize deep learning methods to extract low-frequency channel state information (CSI) to reduce the mmWave beam training overheads. However, an important limitation of deep learning approaches is that the model is usually trained in a given environment. When employed in an unseen environment, it usually requires a large amount of data to retrain. In this paper, a model-agnostic optimization algorithm based on meta-learning is proposed to provide a general mmWave beam prediction model. This model can be deployed to edge base stations and effectively adapted to the environment without the need for a heavy collection of data. Simulation results demonstrate that the proposed approach could reduce the model adaptation overheads. The meta-learning-based beam prediction model is robust and achieves high prediction accuracy and spectral efficiency in different signal-to-noise ratio (SNR) regimes.

Deep neural network-based learning methods have been considered promising techniques used in beam selection problems. However, existing research ignores the peculiar vulnerabilities of neural networks. The adversaries can use data poisoning to embed predefined triggers into a model during training time such that the neural network-based beam model may make an incorrect output decision of a test example when patched with the trigger. Data poisoning offers attackers the possibility to build backdoors. The goal of backdoors is often unethical, such as giving users a poor experience by manipulating infected models to output inappropriate beams. In this paper, first, we introduce a simple backdoor attack method by using data poisoning in a mmWave beam selection system. By numerical simulations, we verify that this poisoning attack is effective for neural networks with different structures. In addition, we explore the effect of poisoned data volume on the effect of backdoor attacks. The results show that the backdoor can be successfully implanted into the beam selection neural network. Besides, we fine-tune the trained model for a new wireless communication environment, and the results show that backdoors still exist even when the model is tuned with data from new scenarios. Then, we propose a machine unlearning solution to mitigate the backdoor of the trained beam selection model. The problem of eliminating backdoors is modeled as a minimax optimization problem. We propose a novel adversarial unlearning method along with label smoothing to solve the backdoor removal problem. We compared the proposed backdoor elimination method with the classical fine-tuning elimination method and the neural network pruning method through numerical simulations. The results show that the fine-tuning and the pruning methods cannot effectively remove the backdoor. The proposed machine unlearning method can make the trained model forget about the backdoor under the condition that the performance of the benign task (beam selection tasks when the trigger does not appear) is guaranteed to be slightly degraded. In summary, our work illustrates that data poisoning-based backdoor attacks may exist in wireless networks, and we propose a scheme to eliminate backdoors.

We study the secure distributed matrix multiplication (SDMM) problem under arbitrary collusion pattern. In the one-sided SDMM problem, where only one matrix of the matrix multiplication needs to be kept secure, we propose an achievable scheme that attains the optimal normalized download cost. The optimal scheme distributes a different number of encoded copies to each server, and the servers that collude more with others are given fewer encoded copies. The converse result is proved using Shearer’s lemma. In the two-sided SDMM problem under arbitrary collusion pattern, where the user would want to keep both matrices of the matrix multiplication secure, we provide an achievable scheme whose key parameters, including the method with which the random matrices are appended, the number of random matrices appended, the number of encoded copies generated, the number of encoded copies distributed to each server, are given by the proposed algorithm. We also demonstrate, via numerical results, the performance of the proposed scheme in terms of normalized upload-download cost trade-off, and show that it is much better than the current known scheme devised for the homogeneous collusion pattern.

The rapid development and continuous innovation of wireless services have led to a boom in the number and types of smart terminals. The huge amount of data that deep learning needs to calculate relies on the continuous improvement of hardware devices to solve. Therefore, the realization of intelligent transportation system(ITS) has become the general trend. The increase in the number of Internet of Things devices and the changes in the location of vehicles in the Internet of Vehicles(IOV) system have put forward higher requirements for the reliability and effectiveness of information transmission and the effective use of spectrum resources. Aiming at the influence and elimination of aggregated interference in intelligent transportation system, an anti-fragile communication algorithm is proposed to improve the reliability of signal transmission. At the same time, the outage probability of energy harvesting and cognitive radio technology enhanced with relay cooperative transmission under cognitive wireless network system with the aggregate interference can be deduced in detail. Finally, the correctness of theoretical analysis and the reliability of the proposed anti-fragile communication algorithms are verified by simulation experiments.

Cell-free massive multiple-input multiple-output (MIMO) is an attractive network in 6G communications that significantly increases the spectral efficiency. Operating in time-division duplex (TDD) mode, the downlink beamforming is achieved by the estimated uplink channel, which is equal to the downlink channel due to the property of channel reciprocity. However, the involvement of different radio frequency (RF) gains in transceiver antennas renders the whole channel non-reciprocal. Therefore, it is of great necessity to calibrate the bi-directional channel. In this paper, we focus on the issue of over-the-air channel calibration in cell-free system. Taking a toy scenario as an example, we examine the performance differences between the calibration methods of ‘Direct Process’ and ‘Indirect Process’. A novel low-cost calibration method based on spanning tree model is proposed specifically for this distributed AP scenario, where a calibration tree is established to calculate calibration coefficients. Numerical results manifest that higher accuracy of our method is achieved compared to the existing calibration methods in the literatures. Our method is less sensitive to the location of master AP compared to Argos, and practical applications under the impact of phase noise show the priority of our method compared to LS method.

Deep learning (DL) is an emerging paradigm for accurate channel estimation for intelligent reflecting surface (IRS)-aided wireless communication systems. It has been proven to be a promising way to achieve better channel estimation performance for the IRS-aided wireless communication system than traditional methods (e.g., least-square algorithm). However, existing DL-based methods rely on ground truth (labels of the true channels) which is difficult to obtain in real networks. In this paper, we propose a self-supervised learning (SSL) method for the IRS channel estimation problem. No ground truth channel is needed in the training, while a simple and novel self-supervised denoising formula without a clean reference signal is presented. Particularly, in the training phase, the self-supervised signal and the input are the received signal vector and its noisy version, respectively. While in the inference phase the input is the estimated channel by using the least-square method and the output is the refined channel estimation. That is, our neural network-based channel estimation algorithm is not reciprocal for training and testing. We demonstrate that the proposed SSL solution has good convergence performance and generalization ability through numerical simulations. Interestingly, we find a “double descent” phenomenon in the learning curve during the test phase, i.e., when we gradually increase the number of training epochs, the performance first gets better, then becomes worse, and further gets better again. Besides, we propose to analyze SSL using the loss landscape and centered kernel alignment method. The results show that the self-supervised model has a similar loss landscape and representational similarity to the supervised model. We explored the effects of different signal-to-noise ratios (SNRs), different neural network sizes, and different training data volumes on our algorithm through numerical simulations. Extensive numerical simulation results show that our SSL algorithm is still competitive without ground truth. We also show that the developed scheme exhibits robustness to SNR ratio mismatch.

This paper proposes a novel bi-directional non-orthogonal multiple access (NOMA)-based cooperative underlay satellite-terrestrial network (CUSTN), in which two secondary satellite users can serve as a potential relay for each user, thus gaining higher cooperative diversity order (DO). We analyze the exact and asymptotic outage probabilities (OPs) of both users under a multi-antenna setup and a practical residual hardware impairment (RHI) consideration. Numerical results are provided to validate the analysis, reveal the impacts of key parameters on the system performance, and demonstrate the advantages of our proposed scheme over other benchmarks.

This paper considers the channel tracking and angle of arrival (AoA) acquisition for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, in which each antenna at the base station is equipped with low-resolution analog-to-digital converters (ADCs) to quantize the received signals. We utilize the beamspace Gauss-Markov model to capture the sparsity and temporal correlation of the time-varying mmWave channel, and an off-grid model is incorporated for an accurate AoA acquisition. Essentially, the beamspace channel tracking is a quantized sparse Bayesian learning problem, which is solved under the expectation maximization (EM) framework. We employ the variational inference to calculate the statistics in the expectation step. In this way, we propose a variational inference joint channel tracking and data detection (VIJ-CTDD) algorithm, in which the detected data symbols are reused to enhance the tracking without extra pilot overhead. Finally, extensive simulations validate the superiority of the proposed VIJ-CTDD algorithms over several existing works.

Abstract This paper investigates a novel wireless information surveillance scheme assisted by reconfigurable intelligent surface (RIS) beamforming and artificial noise jamming cooperation, aiming at monitoring the information sent by an access point (AP) to a suspicious illegal user (SIU). It is assumed that the AP adopt the fixed maximum ratio transmission (MRT) precoding scheme, which is not affected by the information monitoring party. The goal of this paper is to maximize the effective information monitoring rate by jointly optimizing the RIS phase shifts, the receive beamforming vector of the legitimate receiver (LR), and the transmit beamforming vector along with jamming power of the jamming antenna (JA). The resultant optimization problem is non‐convex, and its optimization variables are highly coupled in the objective function and constraints. To tackle this difficulty, the optimization variables are optimized under the alternate optimization (AO) framework. Especially, the intractable RIS phase shifts are optimized by using Riemannian manifold optimization (RMO) algorithm under the penalty dual decomposition (PDD) framework and the semidefinite relaxation (SDR) technique, respectively. Numerical results verify the effectiveness of the proposed algorithms, and also demonstrate the superiority of the designed wireless information surveillance scheme over other benchmark schemes.

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 propose a novel localization and channel estimation integration framework for cell-free massive multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems, in which position information supports accurate channel estimation and accurate channel information can, in turn, improve positioning accuracy. Under this integration framework, we propose a two-phase fingerprint-based localization method consisting of both initial and accurate localization phases and a coarse-location-based (CLB) pilot reassignment scheme. The coarse location information for pilot reassignment is obtained in the initial localization phase of the two-phase localization method, and the fingerprint information used in the accurate localization phase is extracted through channel estimation based on the CLB scheme. Furthermore, for localization, two different fingerprint similarity criteria are proposed to meet the requirements of the different localization phases. Simulation results demonstrate that our proposed two-phase fingerprint-based localization method achieves better positioning performance than existing methods, although there is a slight increase in computational complexity compared to the initial localization. Moreover, our proposed CLB pilot reassignment scheme outperforms the conventional pilot assignment schemes in the comprehensive performance considering both channel estimation performance and complexity.

With the emergence of more and more applications of Internet of Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultradense IoT networks, the ultradensely deployed small base stations (SBSs) will consume a large amount of energy. To reduce the network-wide energy consumption and prolong the standby time of IMDs and SBSs, under the proportional computation resource allocation and devices’ latency constraints, we jointly perform the device association, computation offloading, and resource allocation to minimize the network-wide energy consumption for ultradense multidevice and multitask IoT networks. To further balance the network loads and fully utilize the computation resources, we take account of multistep computation offloading. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we develop an improved hierarchical adaptive search (IHAS) algorithm to find its solution. Then, we give the convergence, computational complexity, and parallel implementation analyses for such an algorithm. By comparing with other algorithms, we can easily find that such an algorithm can greatly reduce the network-wide energy consumption under devices’ latency constraints.

For the estimation of real-valued Gaussian signals, the unscented Kalman filter (UKF) can provide a state estimate with second-order accuracy. However, when a general complex-valued system is considered, a direct extension of UKF from the real domain to the complex domain is inadequate, since the complementary covariance information associated with general improper complex-valued signals has been systematically ignored. To this end, in this work, we propose a general complex-valued unscented Kalman filter (GCUKF) algorithm which can be applied for both proper and improper signals. This is achieved by first proposing a novel sigma points selection scheme for the general complex-valued case, followed by a modified state update method to fully utilize both the innovation and its conjugate. A rigorous MSE analysis illustrates the superiority of the proposed state update method, and simulations support the analysis.

Random interleavers are widely employed in digital communication systems to combat channel fading and burst errors. In applications such as grant-free access by Internet of Things (IoT) devices, accurately identifying a specific irregular interleaving pattern within an interleaver period is vital to both terminal recognition and data recovery. In this work, we investigate effective approaches for random interleaving pattern identification in Reed-Solomon (RS) coded data streams. We first propose an algorithm of low computational complexity to detect positions of code symbols belonging to the same RS codeword group (RSCG) under modest bit error rate. We further develop another low-complexity algorithm to successfully identify random interleaving patterns for RS code symbols within each RS codeword under moderate to high error rate applications. Our theoretical analysis and simulation results corroborate to demonstrate the effectiveness of our algorithms.

Federated learning (FL) is an emerging paradigm for distributed machine learning that uses the data and the computational power of user devices while maintaining user privacy (e.g., position and motion track). It has been proved a promising way to help the learning-based millimeter wave (mmWave) system achieve efficient link configuration. However, FL systems have an inherent vulnerability to backdoor attacks during training, and this has not received attention in current FL-based beam selection research. The goal of a backdoor attacker is to implant a backdoor in the model such that at test time, the model will mispredict a certain family of inputs, and corrupt the performance of the trained model on specific sub-tasks. We study backdoor attacks in an FL-based beam selection system based on a deep neural network that utilizes user location information. Specifically, we propose a backdoor attack scheme that can be configured in the real world. The attacker’s trigger is an obstacle placed in certain locations. When the model encounters an input with these obstacles, the backdoor will be triggered, and the model will output the beam specified by the attacker. Through experiments, we show that the proposed attack can achieve a high attack success rate in a system without a defense mechanism. Moreover, we show that the traditional norm-clipping defense method cannot effectively defend against our attack. Furthermore, we propose a new backdoor attack defense method and verify the effectiveness of this scheme through experiments. In addition, we propose a backdoor detection method: the federated noise titration method, which can diagnose whether the model has a backdoor. Overall, our work explored backdoor attacks, defenses, and detection of the FL-based mmWave beam selection system.

The incorporation of intelligent reflecting surface (IRS) into wireless communication systems can extend the coverage and enhance the data transmission rate. This paper studies the joint transceiver and IRS designs in IRS-assisted multi-input multi-output (MIMO) systems under both perfect channel state information (CSI) and imperfect CSI. Specifically, the transmit precoder, reflection coefficients at the IRS, and receive equalizer are jointly optimized to minimize the data detection mean square error (MSE), subject to the transmission power constraint and the modulus constraints for IRS reflection coefficients. The design problems, non-convex and challenging, are tackled under the framework of alternating optimization. For the design with perfect CSI, we successively optimize the IRS reflection coefficients given the precoder and present the closed-form optimal angle of one reflection coefficient given the others. For the robust design with imperfect CSI, we first average the detection MSE over channel uncertainties by using a generalized statistical CSI error model. Then, the averaged MSE is approximated by a more tractable upper bound. Subsequently, the robust design problem is elaborately transformed into a form similar to the problem with perfect CSI. Numerical results demonstrate the effectiveness of the proposed designs as compared to various benchmark schemes.

Bistatic backscatter communication is emerged as a promising technique to significantly enlarge the lifetime of Internet of Things (IoT) network due to its inherently low-power passive component. However, the effective communication range is limited to only several meters. This article studies the tag circuit shunt network, and propose three modes, namely series mode, parallel mode, and mixed mode, to adjust circuit load impedance of the tag to extend the communication range as well as address the integrated circuit (IC) power supply problem. Specifically, we formulate the bit error rate (BER) minimization problems for the three modes by changing the reflection coefficients, subject to power supply constraint. The resulting problems are shown to be nonconvex fractional optimization problems, which are hard to be solved optimally in general. We first obtain a globally optimal solution to the series mode problem by exploiting the hidden monotonic structure based on monotonic optimization theory. Subsequently, we propose a low-complexity iterative suboptimal algorithm for the three modes based on the successive convex approximation (SCA) techniques. Numerical results show that when the direct link is available, the mixed mode outperforms the parallel mode and series mode, and can adaptively adjust the reflection coefficient to satisfy the requirement of IC power supply. In contrast, when the direct link is unavailable, the series mode is the best choice in terms of IC power supply. In addition, traditional on-off keying modulation is shown to be suitable for a low IC power supply, whereas a shunt network is necessary for high of power supply. Furthermore, the performance of SCA-based method closely approaches the optimal solution while with much lower complexity.

Intelligent reflecting surfaces (IRSs) have significant advantages in enhancing the coverage and reducing the deployment cost of wireless networks. This paper studies an aerial IRS- (AIRS-) enhanced cell-free massive multiple-input multiple-output- (MIMO-) based wireless sensor network (WSN) in which multiple access points (APs) serve several sensor users (SUs). Direct links between the APs and SUs are blocked due to occlusion by tall buildings. Hence, we deploy an AIRS to improve the communication quality of the SUs. Our goal is to minimize the total transmit power of all APs under a given minimum signal-to-interference-plus-noise ratio (SINR) requirement. We propose a joint iterative optimization algorithm by designing an active beamforming mechanism at each AP and a passive beamforming mechanism at the AIRS to solve this problem. Simulation results illustrate the good performance of the proposed method.

The ongoing deployment of 5G network involves the Internet of Things (IoT) as a new technology for the development of mobile communication, where the Internet of Everything (IoE) as the expansion of IoT has catalyzed the explosion of data and can trigger new eras. However, the fundamental and key component of the IoE depends on the computational intelligence (CI), which may be utilized in the sixth generation mobile communication system (6G). The motivation of this article presents the 6G enabled network in box (NIB) architecture as a powerful integrated solution that can support comprehensive network management and operations. The 6G enabled NIB can be used as an alternative method to meet the needs of next-generation mobile networks by dynamically reconfiguring the deployment of network functions, providing a high degree of flexibility for connection services in various situations. Especially the CI technology such as evolutionary computing, neural computing and fuzzy systems utilized as a part of NIB have inherent capabilities to handle various uncertainties, which have unique advantages in processing the variability and diversity of large amounts of data. Finally, CI technology for NIB, which is widely used is also introduced such as distributed computing, fog computing, and mobile edge computing in order to achieve different levels of sustainable computing infrastructure. This article discusses the key technologies, advantages, industrial scenario applications of CI technology as NIB, typical use cases and development trends based on IoE, which provides directional guidance for the development of CI technology as NIB for 6G.

This article studies the confidential transmission performance of an electric vehicle (EV) in heterogeneous network when it communicates with vehicle to grid (V2G). Based on the relay selection strategy that maximizes the signal-to-noise ratio(SNR), the electric vehicle as a legitimate user in this article uses a multi-antenna maximum ratio combining method for signal reception. Among them, a single antenna is configured for the power grid sender, relay nodes and illegal eavesdropping users. The wireless channel adopts Nakagami-m fading channel and the relay adopts decode and forward (DF) method. First, based on the stochastic geometric analysis method, statistical characteristics such as probability density function(PDF) and cumulative distribution function(CDF) of the received SNR are obtained at legitimate users and illegal eavesdropping users, respectively. Then, a functional analysis method is used to derive closed expressions for the secrecy outage probability (SOP) and non-zero security capacity probability in multieavesdropping user systems. Finally, the effects of the system's related parameters on SOP and non-zero security capacity probability are verified through simulations. The simulation results prove the correctness of the theoretical analysis, which can guarantee the privacy and security of electric vehicle users in heterogeneous network.

This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum bit error rate (BER) minimization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the fairness BER optimization problem. We find that the relaxation-based method cannot solve this fairness BER problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks.

This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks.

Proposed by Maddah-Ali and Niesen, a coded caching scheme has been verified to alleviate the load of networks efficiently. Recently, a new technique called placement delivery array (PDA) was proposed to characterize the coded caching scheme. In this paper, we consider a caching system in the scope of ultra dense networks (UDNs). Each base station (BS) has a finite cache and stores some contents. We propose an efficient coded content caching scheme called double coded caching to make the transmission robust to in-and-out wireless network quality. Then the dynamic caching and multicast scheduling are considered to jointly minimize the average delay and power of the content-centric wireless networks. This stochastic optimization problem can be formulated as a Markov decision process (MDP) with unknown transition probabilities and large state space. We propose a deep reinforcement learning approach to deal with the decision problem. Our algorithm uses a variational auto-encoder (VAE) neural network to approximate the state sufficiently, and uses a weighted double Q-learning scheme to reduce variance and overestimation of the Q function. Numerical results demonstrate that the proposed double coded caching scheme increases the probability of the successful transmission, and the caching and scheduling policy can effectively reduce the delay and the power consumption.

Direct-conversion receivers (DCRs) have been adopted in wideband communication systems owing to their simple structure and low cost, however, their operation is affected by amplitude and phase mismatches between their analog inphase (I) and quadrature (Q) branches, as well as the discrepancy of low-pass filter coefficients between these two channels. In this paper, a blind adaptive frequency-dependent I/Q imbalance compensator is proposed, which exploits the complex properness (second-order circularity) of ideal constellation mappings to provide more enhanced insight into the problem setting within the proposed compensator. This serves as a basis for a novel full second-order performance assessment framework, which is established through a joint consideration of the weight error covariance and complementary covariance in both the transient and steady-state stages. This conjoint analysis is further shown to facilitate accurate quantification of the overall mirror-frequency interference attenuation capability of the proposed compensator. Simulation results in an orthogonal frequency division multiplexing (OFDM) transmission system demonstrate the excellent performance of the proposed compensator.

This paper investigates unmanned aerial vehicle (UAV)- aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver using transmit- backscatter (TB) protocol. Our goal is to maximize the system ergodic capacity by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. The resulting optimization problem is shown to be non-convex, which is challenging to solve. We consider two different circuit power consumption models, namely dynamic and static models. We first consider the dynamic model, and decompose the original problem into three sub- problems, and an iterative algorithm is proposed to optimize three subproblems alternately by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity using the Lagrangian dual method. Simulation results show that the proposed scheme is able to achieve significant throughput gains over the compared benchmarks.

Coded caching is a promising approache to support low latency transmission over broadcast wireless networks. The process of selecting the nodes that forward coded messages can be considered as a set cover problem. However, existing research efforts don't focuse on solving the set cover problem. This paper investigates the problem of the cover problem for coded caching in wireless networks. First, we propose a novel coded caching method using deep neural networks. Then, we establish a mathematical model for cover problem of the coded caching system. Then, we propose a deep learning approach to solve it. Different from previous works, our proposed deep neural architecture uses sequence-to- sequence model to learn the solutions. Finally, numerical results are given to demonstrate the proposed coded caching method have lower load than traditional coded caching method, and that proposed method for solving the cover problem can effectively implement coded caching with lower computational complexity.

Complex least mean square (CLMS) based adaptive computation of discrete orthogonal transforms has been extensively investigated in the literature. However, all of these results provide only a means for the calculation of either forward orthogonal transforms or their inverse orthogonal transforms, separately. In this work, a way to simultaneously calculate the discrete Fourier transform (DFT) and the inverse DFT (IDFT) is established via the widely linear (WL) signal processing framework. We show that by appropriately selecting the input vector and adaptation speed of the widely linear complex least mean square (WL-CLMS), the resulting spectrum analyzer is capable of simultaneously performing DFT and IDFT of the signal to be Fourier analyzed in both the block-based and online manners.

The energy reduction for wireless systems becomes more and more important due to its impact on the operation cost and global carbon footprint. In this paper, we design two kinds of energy-efficient association schemes under an open loop power control for uplink heterogeneous cellular networks (HCNs), which are formulated as problems with maximizing sum energy efficiency (EE) and EE utility respectively. In them, the second scheme integrates with the load balancing level and user fairness. Since the first problem is in a simple form, we can easily solve it without any iteration. As for the second problem, we first introduce a dual variable to decouple the constraint and then develop a distributed algorithm using dual decomposition. In addition, we also give some convergence proofs for the proposed algorithms. In the simulation, we investigate the influences of different parameters on the association performance of designed association schemes.

This study investigates a novel unmanned aerial vehicle (UAV)‐based wireless sensor network, where the UAV acts as a flying base station to serve multiple wireless sensor nodes (SNs). The authors goal is to maximise the system energy efficiency of the UAV while satisfying the fairness among SNs by jointly optimising the UAV trajectory and UAV time allocation. The formulated problem is shown to be a non‐convex fractional optimisation problem, which is hard to tackle. To this end, they decompose the original problem into two sub‐problems, and the block coordinate descent method and successive convex optimisation technique are employed to solve these two sub‐problems iteratively. Specifically, in the first sub‐problem, the optimal UAV time allocation is obtained by maximising the minimum achievable rate of SNs with given UAV trajectory constraints. In the second sub‐problem, the UAV trajectory is achieved by minimising the energy consumption of the UAV with the given UAV time allocation. Subsequently, an iterative algorithm is proposed to optimise the time allocation and UAV trajectory alternately. Furthermore, the convergence and complexity of their proposed algorithm are provided. Numerical results show that the proposed scheme outperforms the existing benchmark strategies in terms of energy efficiency.

This paper investigates the problem of proactive caching for multi-view 3D videos in the fifth generation (5G) networks. We establish a mathematical model for this problem, and point out that it is difficult to solve the problem with traditional dynamic programming, then we propose a deep reinforcement learning approach to solve it. First, we model the proactive caching system for multi-view 3D videos as a Markov decision process jointing views selection and local memory allocation. Then, we present an actor-critic, model-free algorithm based on the deep deterministic policy gradient to find effective proactive caching policy. Since the action space is affected by the system state, we embed dynamic k-Nearest Neighbor algorithm into actor-critic algorithm to implement the deep reinforcement learning algorithm working in an action space of variable size. Finally, the numerical results are given to demonstrate that the proposed solution can effectively maintain high-quality user experience for high-mobility 5G users moving among small cells. We also investigate the impact of configuration of critical parameters on the performance of the algorithm.

In this paper, we employ the unmanned aerial vehicle (UAV) as a flying base station (BS) to offload the data computing tasks from mobile terminal (MT) for saving mobile energy consumption. Our goal is to minimize consumption of the computational tasks at MT by jointly designing the resource partitioning scheme and bit allocation strategy. Specifically, the portion of total bits for local computation at MT is optimized, and the other portion of bits is computed by jointly optimizing the number of bits transmitted in the uplink, the number of bits computed locally at UAV and the number of bits transmitted in the downlink. The formulated problem has been shown in a convex form, which has optimal solutions. Instead of solving original problem using standard convex optimization techniques, we propose a resource partitioning scheme and bit allocation strategy based on dual decomposition, which has been shown in a low computational complexity. Furthermore, the numerical results are provided to demonstrate the superiority of our proposed scheme over the compared benchmarks.

Traveling by bicycle has become a rising trend recently for its convenience and flexibility, which calls for considerate bicycle trip planning schemes. While research for traditional trip planning has focused on quantized quality of point-of-interest (POI) or correlations among POIs, problems appear for distinct influential factors in bicycle trips and being unable to plan in a foreseeable stage with satisfying various demands of cyclists. In this paper, to alleviate the deficiencies of conventional approaches that merely concentrating on temporary interests and fully depending on greedy algorithm, the active Q-learning algorithm derived from reinforcement learning (RL) is adopted for Q-value iteration for planning overall optimal bicycle trips. To further meet personal improvised demands such as containing some specific places in the trip, Tailored Trip is provided and a dynamic and flexible place inserting algorithm is proposed to automatically tweak the trip and keep the planning optimum status. Experiments have been conducted to intuitively evaluate the performance of our schemes on two real-world datasets. The planning results clearly illustrate that the optimal node choosing policy is continuously reinforced in our schemes and the result for Tailored Trip highlights the guarantee of overall superiority after trip tweaking.

Performance analysis is studied in this paper for the wireless transmissions in Internet of Things (IoT) system, where both the direct link and the multihop relaying caching wireless transmission from the source node to the destination node are taken into the consideration. The key feature is the Nakagami channels of the wireless channel from the source node to the destination node, which results in the difficulty of the theoretical analysis over the system performance. To tackle this difficulty, the probability distribution function (PDF) of the received signal‐to‐noise ratio (SNR) at the destination node is derived by exploiting the function and integral properties. Then, the outage probability and bit error rate (BER) of the whole wireless IoT system are derived in the analytical expression without any approximation. Numerical simulations demonstrate the accuracy of the derived theoretical analysis for this system.

Low cost and high efficiency, defined as energy efficiency (EE) and spectral efficiency (SE), have raised more and more attention in the fifth generation (5G) communication systems due to steadily rising hardware cost, energy consumption, and mobile traffic. This paper studies the hybrid architecture of multiuser massive MIMO systems, where the digital domain utilizes the zero‐forcing (ZF) precoding scheme and the analog domain uses discrete Fourier transform (DFT) processing that significantly reduces hardware cost and energy consumption. We derive analytical expressions on the total achievable SE and EE, as well as offering insight into some engineering parameters in the system performance. Our aim is to achieve low cost and high efficiency massive MIMO system, with constraints on the overall transmit power, the number of users, and the number of radio frequency (RF) chains. Results exhibit that the total achievable SE of the hybrid architectures with DFT precessing is inferior to the full digital architectures and hybrid architectures with the ideal phase shifters, but the performance attenuation can be compensated by providing the more input SNR and higher number of RF chains. Moreover, we find that the total achievable EE of hybrid architectures with DFT precessing outperforms other massive MIMO architectures that include a full digital implementation, ideal phase shifters, and a switched network.

Driver distraction behaviors are usually blind to autonomous vehicles (AVs), leading to probable late preparation for AVs to take emergency measures. Hence, this paper aims to build a bridge between AV control and driver behavior detection, to assist AVs to predict the potential risk and avoid abnormal drivers carefully like experienced drivers. Our main contributions of this paper consist: i) put forward a practicable system framework integrating driver distraction monitoring, vehicle-to-vehicle communication and AV velocity control; ii) provide a real-time driver distraction monitoring implementation building on convolutional neural network trained offline; iii) propose a method of longitude velocity control of AV considering the risk of driver distraction behavior based on model predictive control strategy. Simulation results validate the effectiveness of our work.

In this study, the authors address the downlink resource (subchannels and power) allocation problem for device‐to‐device communication with wireless power transfer technique in a cellular network to improve the energy efficiency (EE). The considered problem is formulated as maximising the weighted EE and is solved by leveraging a game‐theoretic learning approach. Specifically, they first prove that an exact potential game applies to the resource allocation problem and there exists the best Nash equilibrium (NE) which is the optimal solution of the optimisation problem. Then, aiming to this optimisation problem with imperfect information, a robust and distributed learning algorithm is proposed and is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.

The computational complexity required in the channel estimation plays an important role in underwater acoustic communications (UAC) with orthogonal frequency duplex access (OFDM), especially when the channel is sparse. The authors develop an algorithm to carry out the orthogonal matching pursuit (OMP) for the sparse channel estimation based on the compressive sensing, where the goal is to obtain the minimum computational complexity. It is discovered that the inter‐carrier interference (ICI) mainly depends on the adjacent subcarriers since the ICI interferences become more and more marginable with the increase of the distance from the other subcarriers to the current desired subcarrier in the frequency domain, which can be utilised to reduce the complexity of the design over the sparse channel estimator. By exploiting this property, the authors propose that the diagonal band of the ICI channel matrix is employed in the calculation of the objective function to minimise the required computational complexity, which develops an adaptive algorithm that is theoretically proved to be a faster algorithm. Numerical simulations are demonstrated for the typical UAC system that the proposed algorithm achieves the remarkable gain of the computational complexity compared to the existing algorithm.

This letter studies the cooperative precoding design for a coexisting wireless energy transfer (WET) and cognitive radio (CR) system, where the WET system share the same spectrum with the CR system. Different from the traditional wireless networks, interference here is regarded as a useful rather than harmful resource. Specifically, we address the transmit covariance design to minimize the total transmit power at the energy transmitter and the secondary transmitter while satisfying secrecy rate, energy harvesting, and interference temperature constraints. We propose an iterative algorithm to tackle the formulated nonconvex optimization problem, and prove that it could converge to a Karush-Kuhn-Tucker point of the original problem. Simulation results are finally provided to illustrate the effectiveness of our proposed algorithm.

Hybrid multiple input multiple output (MIMO) beamforming can be divided into shared and split MIMO beamforming architectures, according to different concatenations of radio frequency (RF) chains and antennas. This paper considers split MIMO beamforming for millimeter wave system, i.e., each antenna subarray is only connected with one RF chain. To obtain analog precoding matrix and combining matrix, an algorithm based on signal to leakage plus noise ratio (SLNR) is proposed to align the transmitter's and receiver's antenna subarrays in one- to-one way. The effectiveness of our proposed subarray alignment algorithm is validated by simulation, and the hybrid and purely digital beamforming are compared in terms of channel capacity. Numerical results show that for the number of transmit and receive antennas, the performance gap between purely digital and hybrid beamforming decreases by increasing the number of RF chains. Moreover, effective degree of freedom (EDOF) is introduced to analyze the channel characteristic.

When massive MIMO technology is combined with dense heterogeneous networks (HetNets), the user association and power allocation problems are fundamentally different although the energy- efficiency benefits can be intensified. This paper aims to investigate the energy efficient joint user association and power allocation problem in downlink massive MIMO empowered dense HetNets under proportional fairness criterion. The joint optimization problem is a non-convex mixed-integer nonlinear program (MINLP) which is NP-hard, and hence it is difficult to efficiently obtain exact solution. In order to obtain the highquality suboptimal solution, the joint optimization problem is first decomposed into two subproblems with alternating iterative method. Then a two-layer iterative suboptimal algorithm is proposed to solve the joint optimization problem with guaranteed convergence. The involved association subproblem adopts dual decomposition to achieve the optimal association index, whilst the power allocation subproblem allocates the transmit power of each BS with Newton's method. Numerical results verify the effectiveness of our proposed algorithm and show that our proposed algorithm outperforms conventional association schemes in the enhancement of energy efficiency performance. Furthermore, it can be seen that the energy efficiency performance is enhanced by increasing the number of antennas at macro base station (MBS).

This paper investigates the joint power control and subchannel allocation problem for device-to-device (D2D) and small cell uplink communications in a cellular network to improve the cellular throughput. For this considered throughput maximization problem, we propose to solve it leveraging a game-theoretic learning approach. However, there is an intractable issue for obtaining the optimal power allocation profile in the continuous space. To this end, we first deduce the optimal power expressions under any given subchannel allocations. Based on the optimal power profile, we then formulate the subchannel allocation problem into a game framework. Next, aiming to this optimization problem, a cloud-assisted learning algorithm with conditioned strategies is proposed to converge to an equilibrium point which maximizes the optimization objective. Finally, numerical results verify the effectiveness of the proposed scheme.

A hierarchy precoding approach is proposed in this study for multi‐cell multiuser systems with any number of base stations and that of users, which is suitable for any number of data streams. The key feature of this approach is aligning the inter‐user interferences within the same cell to the room spanned by the inter‐cell interferences, by which both the inter‐cell and inter‐user interferences are cancelled simultaneously. Then, the inter‐stream interference for each user can be easily tackled. It is found that the interference alignment‐based hierarchy precoder achieves to the full freedom of degree. With interference‐free transmissions achieved by the proposed precoder, the transmit power is optimised in an analytical expression by maximising the sum rate and minimising the sum weighted mean square error. Extensive simulations demonstrate the effectiveness of the proposed method.

An adaptive overhearing protocol is proposed for the two-way multi-antenna-relay network composed of a base station (BS), relay, and two user equipments (UEs), where one UE is in the uplink (UL) transmission mode (UE-Tx) while the other is in the downlink (DL) reception mode (UE-Rx). Specifically, UE-Rx not only receives the DL signal transmitted by BS but also overhears the signal transmitted by UE-Tx, and exploits the overheard signal to improve the detection performance. The transmit adaptive weights of UE-Tx over the two times slots and the precoding matrix at the relay in the second time slot are jointly optimized via the proposed iterative algorithm in the sense of maximizing the minimum signal-to-interference-plus-noise-ratio. Numerical results show that the proposed joint design provides significant sum-rate gain over the existing overhearing scheme.

This paper studies the secure beamforming design for simultaneous wireless information and power transfer in a multiple-input single-output broadcast channel with confidential messages and external eavesdroppers, where each receiver adopts the power splitting (PS) scheme to decode information and harvest energy concurrently, and it is also seen as a potential eavesdropper for messages not intended for it. Our objective is to minimize the total transmit power while guaranteeing the individual secrecy rate and energy harvesting constraints at each receiver by jointly optimizing transmit beamforming vectors, artificial noise covariance, and receive PS ratios. Both scenarios of perfect and imperfect channel state information (CSI) at the transmitter are considered. For the perfect CSI case, we propose a two-stage optimization approach to solve the original non-convex problem global optimality, and also provide a low-complexity suboptimal solution based on the particle swarm optimization algorithm. Furthermore, we also extend the above result to the colluding eavesdroppers scenario. For the imperfect CSI case, we propose a worst-case-based robust formulation, where the CSI errors are norm-bounded. With the aid of S-Procedure, we derive the equivalent forms for constraints and then transform the non-convex robust design into a convex optimization problem. Simulation results are finally presented to demonstrate the performance of our proposed schemes.

In this paper, we propose a secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) in a multiple-input single-output (MISO) broadcast channel with confidential messages, where each receiver utilizes the power splitting approach to decode information and harvest energy simultaneously, and it also acts as a potential eavesdropper for the independent message sent to the others. By jointly optimizing the transmit beamforming vectors, covariance of artificial noise, and receive power splitting (PS) ratios for all receivers, we aim to maximize the total harvested energy while guaranteeing the secrecy rate constraint at each receiver and the total transmit power constraint at the transmitter, which is a non-convex optimization and hard to solve. In this paper, we propose a two-stage optimization approach based iterative algorithm to tackle such a challenging problem. Moreover, we prove that the proposed algorithm can achieve convergence, and also analyze its computational complexity. Finally, simulation results are provided to demonstrate the performance of our proposed algorithm.

The IEEE 802.11ah complements the other standards to provide support for machine-type communications (MTC) in local area network using a different spectrum from the highly congested WLAN operating at 2.4GHz/5GHz ISM bands. In order to satisfy massive station communications, the paper presents traffic indication map (TIM) compressed scheme for the Internet of Things and Smart Grid based on IEEE 802.11ah. The simulation results are discussed by evaluating the impact of the TIM compression on the system performance and cost. Results indicate that the proposed scheme has an acceptable cost and can support massive communications with higher performance enhancement. The proposed compressed TIM indication length can efficiently save the TIM bit map space and simultaneously be superior to the traditional structure.

This paper considers a downlink massive MIMO frequency division duplexing (FDD) system. Due to the large number of antennas, the required length of training sequence for downlink training significantly increases in FDD mode, which leads to prohibitive overhead in real system. Thus, in this work we investigate how the length of training sequence affects the system performance. For this purpose, we derive an analytical expression of the ergodic achievable rate from a worst case viewpoint with the the training sequence length as a parameter in it. It is revealed from the analytical results that i.) the length of training sequence divided by the number of base station antennas approaches to zero yet the achievable rate can increase to infinity as long as the antenna number is sufficient large; ii.) there is a ceiling effect on the achievable rate if the antenna number grows large with any fixed training length. Furthermore, we propose a guideline for the selection of the training length. Numerical results validate the derivations and analysis.

In this paper, a massive MIMO downlink frequency division duplexing (FDD) system over correlated Rayleigh fading channel is considered. It is well known that the length of training sequence not only affects the accuracy of channel estimation but also accounts for the rate loss resulting from training overhead. However, as the number of the base station antennas becomes large, the required length of training sequence cannot increase unlimitedly. Thus, in this work we derive the analytical expression of achievable rate and investigate the impacts of the training sequence length on system asymptotic performance. It is discovered from the analytical results in two-fold that (1) the length of training sequence normalized by the antenna number approaches to zero yet the system capacity is guaranteed to positive infinity as long as the antenna number is large enough; (2) the transmission capability saturates to a certain level if the antenna number grows to very large with any given training length. Simulation results verify the theoretical derivations and demonstrate the performance limit.

This paper investigates the resource allocation issue between downlink training stage and data transmission stage for the frequency division duplexing (FDD) massive multiple-input multiple-output system from the viewpoint of energy efficiency (EE). For a given total energy budget during a coherence period, how to jointly select the training duration, training power and data power is of great significance for the system EE. Thus, an optimization problem of energy-efficient resource allocation is put forward. Since the analytical expression of the involved average spectral efficiency (SE) is intractable, a closed-form approximation of the SE is deduced using deterministic equivalent. Based on the simplified expression, the original non-convex fractional optimization problem is transformed into an equivalent problem in subtractive form by the means of fraction programming, which includes an achievable solution. Then, an iterative algorithm is proposed. Numerical results validates the benefits of the proposed resource allocation scheme.

In this paper, we design a secure transmission scheme for multiple-input single-output (MISO) broadcast channel with simultaneous wireless information and power transfer (SWIPT), where a multi-antenna transmitter simultaneously transmit independent confidential messages to multiple potentially malicious receivers, in the presence of external eavesdroppers. Our objective is to minimize the total transmit power while guaranteeing the security communication and energy harvesting constraints by jointly optimizing the transmit beamforming vectors, covariance of artificial noise, and power splitting ratios, which is non-convex optimization and hard to tackle. We first solve this non-convex problem by using the technique of semi-definite relaxation (SDR), and then prove that the relaxation is tight and thus achieves the globally optimal solution of the original problem. Simulation results are finally presented to demonstrate the performance of our proposed scheme.

Two‐way relaying systems efficiently accomplish transmissions in both directions within dual‐hop, hence, two time slots can be saved compared with one‐way relaying. However, the conventional two‐way relaying protocol requires the assumption of symmetric traffic demands, that is, each transmitter node has to act as a receiver in latter slot. This assumption restricts applying two‐way relay to general and practical scenarios. In this study, the authors release this unpractical constraint by assuming that the transmitter in slot 1 and receiver in slot 2 can be any nodes, which are not necessarily being the same. For this scenario, a novel transmission protocol exploiting the overhearing link to suppress the interference caused by asymmetric traffic, denoted as overhearing transmission, is proposed. With the overhearing transmission protocol, and in the light of green communications, the precoding matrices at the decode‐and‐forward multi‐antenna relay are optimised to improve energy efficiency in both uplink and downlink (DL) transmission directions, where the objective is to minimise the transmit power at the relay while guaranteeing a target transmission rate. The authors transform the original non‐convex problem to an equivalent form, which can be readily solved by typical semi‐definite relaxation approaches. An efficient algorithm is further proposed to implement the precoding design in practice. Simulation results show that the proposed algorithm is able to minimise the power consumption at the relay, with the minimum rate constraints of both the uplink and DL transmissions being satisfied.

A joint source-and-relay beamforming scheme is proposed for multiple-input multiple-output (MIMO) systems with distributed single-antenna relays. First, a lower-bound of the signal-to-noise ratio at the destination is derived as an objective function to formulate a constrained beamforming optimisation problem. The joint beamforming problem is then divided into two sub-optimisation problems corresponding to the source and the relay beamforming, respectively. The first sub-problem is shown to be a quadratic concave minimisation, and is tackled by developing an iterative algorithm with each iteration solving a linear problem. The second one corresponds to a Rayleigh–Ritz ratio problem which is then solved by the generalised singular-value decomposition in a closed form. Based on the solutions to the subproblems, a global iterative algorithm is designed to implement the joint source-and-relay beamforming. Simulation results show that the proposed method outperforms some existing relaying schemes in terms of the capacity and outage probability of the whole MIMO relay system.

A maximum mean squared error optimal relay beamformer is proposed here for two-way single-antenna distributed relaying systems. A constrained optimisation problem with respect to the relay beamforming vector is first formulated. It is then shown that the design problem of such a relay beamformer supporting both downlink and uplink transmissions simultaneously can be converted to convex optimisation when full channel state information (CSI) is available at the relays. By employing the Lagrangian multiplier method, a closed-form solution for the relaying vector is obtained. The proposed relay beamforming method is also extended to the situation where only the statistics of the CSI are available. A simulation study is conducted to confirm the merit of the proposed two-way relaying scheme.

In this paper, a robust optimal distributed relay beamforming scheme is presented for two-way wireless relay systems with two sources (one base station and one mobile terminal) and multiple relays, each having a single antenna. Considering that the perfect channel state information (CSI) between the mobile terminal and the relays is usually not available, the new beamforming problem, based on the minimization of the sum MSE (mean squared error) subject to a total relay power, is formulated such that only the CSI between the base station and the relays is required. The constrained beamforming optimization problem is then solved by the Lagrangian multiplier method, leading to a closed-form solution for the distributed relay beamforming. Monte Carlo simulations show that the proposed scheme performs better than the conventional relaying method in terms of both sum rate and the bit-error-rate (BER).

Some of the existing relay schemes for cooperative networks based on one-way relaying protocol are not applicable to the case of two-way relaying protocol. In this letter, a cooperative relay scheme for distributed amplify-and-forward relays working under the two-way relaying protocol is designed in a closed-form, which is asymptotically optimal in the high levels of signal-to-noise ratio (SNR). An upper-bound of the mean squared error (MSE) is derived via a tight approximation of the SNR expression. Based on the minimization of this upper-bound, the two-way relay scheme is then derived as a Rayleigh-Ritz ratio problem. The new relay scheme achieves the minimum MSE for both directional transmissions. Simulation results illustrate the effectiveness of the proposed scheme especially in the high SNR regime.

In this paper, a two-way relay precoder is designed for multiple-input multiple-output (MIMO) distributed cooperative relay systems. A constrained optimization problem with respect to (w.r.t.) the relay precoder is formulated for the most general relay and antenna scenario, namely, multiple relays each with multiple antennas, It is shown that due to the difficulty of two-way distributed relaying mechanism as well as the block-diagonal nature of the relay precoding matrix, this optimization problem cannot be solved by existing one-way relaying methods. It is then proved that with full channel state information available at relays, the underlying problem can be converted to a convex optimization w.r.t. the non-zero entries of the relay precoding matrix only, such that the Lagrangian multiplier method is applicable to the relay precoder design, leading to a closed-form relay precoding solution. A simulation study is conducted to justify the superior performance of the proposed two-way relaying scheme.

A new optimization criterion is proposed to minimize error probability for the proposed joint optimal power allocation (PA) of the MIMO systems enhanced by relay in this paper. It is proved that the cost function obtained is only convex with respect to (w.r.t.) the power parameters of the source or those of the relay separately, but not convex w.r.t. the whole parameters. In order to use convex optimization methods with high efficiency to solve this complicated problem, a tight upper bound of the sum MSE (mean squared error) is derived, and employed to modify the cost function in order to obtain a convex problem. It is verified through simulation results that the proposed PA scheme outperforms the existing one.

In this paper, new dynamic resource allocation algorithms are presented for the downlink of multi-user MIMO-OFDMA/SDMA systems. Since it is difficult to obtain the optimal solution to the joint optimization problem, the whole procedure is divided into two steps, namely, the subcarrier-user scheduling and the resource allocation. In the first step, a new metric is proposed to measure the spatial compatibility of multiple users each with multiple receive antennas, based on which a new subcarrier-user scheduling algorithm is designed. In the second step, two dynamic resource allocation algorithms are developed to assign radio resources to the scheduled users accordingly. Simulation results demonstrate the superiority of the proposed algorithms in terms of the system throughput.