This paper investigates covert communication of multi-antenna relaying system with probabilistic interference, where a multi-antenna Alice transmits its covert message to a single-antenna Bob through a multi-antenna relay. The relay operates under the amplify-and-forward (AF) protocol, and employs antenna selection based on the relay-Bob channel quality. To enhance the covert communication, a multi-antenna jammer probabilistically transmits jamming signals to degrade Willie’s detection performance. For this system, we formulate the covert communication problem as an optimization framework which maximizes the received signal-to-interference-plus-noise ratio (SINR) at Bob while ensuring covertness constraints, by jointly optimizing the beamforming vectors at Alice and the jammer, the jamming probability, and the relay’s amplification factor. To address this non-convex optimization problem, we first derive Willie’s detection error probabilities for both transmission phases. Next, we analytically determine Willie’s optimal detection threshold and minimum detection error probability by examining their monotonicity and extremal properties. Then, we transform the original problem into a tractable convex form based on these analytical results. We further propose an efficient alternating optimization algorithm to iteratively update the system parameters. Simulation results are finally demonstrated to show the effectiveness of the proposed scheme, showing that increasing the jamming power or the number of antennas at the relay significantly improves Bob’s received SINR.

This paper proposes a secure active reconfigurable intelligent surfaces (RIS)-assisted multiple input multiple output integrated sensing, communication, and computation over-theair system. Distributed sensors simultaneously sense targets and transmit private data to an access point (AP) via over-the-air computation, while a passive eavesdropper attempts to intercept. We formulate a joint optimization problem to minimize the AP’s computational mean-squared error (MSE) under constraints on the eavesdropper’s computational MSE, sensing accuracy, and transmit power through the jointly design of transmit beam-forming, aggregation beamforming, and active RIS reflection coefficients. Under perfect wiretap channel state information (CSI), the formulated non-convex problem is decomposed and solved via a penalty-based alternating optimization algorithm, using closed-form aggregation beamformer and successive convex approximation. The framework is extended to imperfect wiretap CSI with norm-bounded uncertainty. By deriving a conservative lower bound on the eavesdropper’s distortion and applying the Generalized S-Procedure, a robust alternating optimization algorithm is developed to solve the reformulated problem. Simulations validate the superiority of the proposed scheme over benchmarks with passive or randomly configured active RIS. Key system parameters, including sensor count, security and sensing accuracy thresholds, are analyzed to provide practical insights.

This contribution focuses on the optimization of bit error rate (BER) performance in fluid antenna (FA)-aided wireless communication systems, which leverage the new degrees of freedom provided by positional flexibility, thus surpassing the limitations of conventional fixed-position antenna (FPA) systems. In this context, a theoretical analysis identifies key metrics for maximum likelihood (ML) and zero-forcing (ZF) detectors, specifically the minimum singular value and effective rank, as determinants of system performance. Then, for single-input single-output (SISO) channels, the optimization problem is formulated as channel gain maximization constrained by the predefined moving region and then solved using a mixed-integer linear programming (MILP) model. Furthermore, for multiple-input multiple-output (MIMO) channels, an alternating optimization (AO) algorithm incorporating the Frank-Wolfe method is proposed to optimize ML and ZF performances, targeting minimum singular value maximization for ML and singular value balancing for ZF, both subject to moving region and antenna spacing constraints. Finally, numerical results exhibit significant performance gains for the FA system over its conventional FPA alternative. In general, these findings highlight the potential of FA systems for addressing ultra-reliable communication challenges in the sixth generation (6G) wireless communications.

Semantic communication (SemCom) has recently emerged as a promising approach to enhance communication efficiency by transmitting only important semantic information. In eavesdropping environments, the semantic information may be overheard by the eavesdropper even under poor channel conditions with a powerful semantic decoder. Some advanced secure communication deployments such as autoencoder-enhanced Sem-Com (AE-SemCom) and generative model-enhanced SemCom (GEN-SemCom) have been introduced to efficiently transmit the semantic information. However, these secure solutions require retraining the semantic encoder and decoder, which limits the flexibility and practical deployment. To address this issue, we propose a secure SemCom framework for AE-SemCom and GEN-SemCom without altering the parameters of the semantic encoder and decoder. Specifically, we propose to deploy a cooperative jammer to transmit optimized jamming signal to deteriorate the decoding ability of eavesdropper, while a residual refinement module (RRM) is used at the legitimate receiver to mitigate the impact of these jamming signal. Moreover, to achieve an efficient trade-off between transmission security and reliability, we then introduce a cooperative training strategy by formulating this optimization problem as a two-player game between the jammer and Bob. Finally, we conduct extensive simulations to evaluate the effectiveness of the proposed framework across face recognition dataset. In particular, with a jamming power of 0.3, the eavesdropper experiences up to a 12dB reduction in the peak signal-to-noise ratio (PSNR) of reconstructed images compared to the baselines without the proposed security framework while maintaining high-quality image reconstruction for legitimate users.

Extremely large-scale multiple-input multiple-output (XL-MIMO) significantly enhances wireless communication capability, yet the inherent strong near-field effects incur prohibitive pilot overhead and severely constrain the exploitable sparsity for channel estimation. Conventional far-field estimators become inadequate in this regime, and existing near-field techniques usually suffer from basis mismatch, high computational complexity, and limited adaptability to complex propagation behaviors. To overcome these challenges, this paper proposes a robust near-field channel estimation framework that integrates model-driven inference, data-driven refinement, and adaptive learning. First, we develop a dual-stage optimization network (DONet) based on approximate message passing (AMP). By jointly learning the sensing and sparse transform matrices in a gridless manner and performing hierarchical-global optimization of inference parameters, DONet achieves enhanced representational capability and high-fidelity channel reconstruction. To further suppress unmodeled residuals, a lightweight residual refinement network (RRNet) is designed as a plug-and-play correction module. RRNet leverages learnable linear projections and a residual-enhanced multilayer perceptron to capture nonlinear deviations while preserving the DONet backbone for interpretability and stable convergence. Finally, a transfer learning-aided network adaptation (TLNA) strategy is introduced to improve reliability under dynamic environments via a pretraining-fine-tuning mechanism, enabling fast domain adaptation with minimal retraining cost. Simulation results demonstrate that the proposed framework achieves substantial performance gains over representative baselines, offering an efficient and robust solution for near-field XL-MIMO channel estimation.

This paper explores the security issues of a rate-splitting multiple access (RSMA)-aided integrated sensing and communication (ISAC) system. A dual-function base station communicates with multiple users via rate-splitting technology and simultaneously senses multiple targets, which also act as colluding malicious eavesdroppers. For the case where coarse target locations are known but subject to estimation errors, we construct an equivalent wiretap channel model incorporating channel uncertainty under the collusion scenario. Based on this model, the transmit covariance matrix is optimized for the sensing-only task, representing an ideal sensing-oriented transmitter design. However, in ISAC systems, the practical transmit covariance also needs to account for communication performance. In order to improve the sensing performance while ensuring communication security, we formulate an optimization problem that jointly optimizes transmit beamforming and rate-splitting to minimize covariance mismatch, subject to secrecy rate and transmit power constraints. This non-convex problem is transformed using convex relaxation techniques and solved via a robust block coordinate descent algorithm. We further consider the case where the locations of malicious sensing targets are unknown, and then formulate an optimization problem to minimize communication power while enhancing sensing capability via an omnidirectional beam, subject to rate constraints. We then develop an efficient block-wise algorithm based on convex reformulation. Simulation results confirm the efficacy of the proposed RSMA scheme in addressing system uncertainties, as well as reveal influences of various key parameters.

Jamming in some cases observed at its transmitting side could be modeled as a constant amplitude but random phase signal:J(t)= AJ · ejϕ(t). Such situation may happen in single-tone or multi-tone jammed frequency hopping systems, in cellular mobile systems under co-channel interference, etc. In order to appropriately handle this kind of jamming through channel coding technologies, a jamming model based log-likelihood ratio (LLR) initialization method is proposed to replace their conventional AWGN model based LLR initialization method. Accordingly, the optimal LLR initialization formula is derived based on the analysis of the probability density function of jammed signals. Then, the approximation of optimal LLR initialization as well as its robust variant are further provided, hence the complexity is significantly reduced and only the signal-to-noise ratio (SNR) and signal-to-jamming ratio (SJR) are required as thea prioriknowledge. The proposed LLR initialization method is tested in a Polar Code (PC) coded Orthogonal Frequency Division Multi-plexing (OFDM) system, where both the AWGN and the Rayleigh fading channel models are considered. The obtained simulation results demonstrate that the proposed method outperforms the conventional Gaussian model based one and other existed robust initialization methods.

Recently, the pinching-antenna system (PASS) has emerged as a promising architecture owing to its ability to reconfigure large-scale path loss and signal phase by activating radiation points along a dielectric waveguide. However, existing studies mainly focus on line-shaped PASS architectures, whose limited spatial flexibility constrains their applicability in multiuser and indoor scenarios. In this paper, we propose a novel two-dimensional (2D) pinching-antenna system (2D-PASS) that extends the conventional line-shaped structure into a continuous dielectric waveguide plane, thereby forming a reconfigurable radiating plane capable of dynamic beam adaptation across a 2D spatial domain. An optimization framework is developed to maximize the minimum received signal-to-noise ratio (SNR) among user equipments (UEs) by adaptively adjusting the spatial configuration of pinching antennas (PAs), serving as an analog beamforming mechanism for dynamic spatial control. For the continuous-position scenario, a particle swarm optimization (PSO)-based algorithm is proposed to efficiently explore the nonconvex search space, while a discrete variant is introduced to accommodate practical hardware constraints with limited PA placement resolution. Simulation results demonstrate that the proposed 2D-PASS substantially improves the minimum SNR compared with conventional line-shaped PASS and fixed-position antenna (FPA) benchmarks, while maintaining robustness under varying user distributions and distances.

In this paper, we consider a novel optimization design for multi-waveguide pinching-antenna systems, aiming to maximize the weighted sum rate (WSR) by jointly optimizing beamforming coefficients and antenna position. To handle the formulated non-convex problem, a gradient-based meta-learning joint optimization (GML-JO) algorithm is proposed. Specifically, the original problem is initially decomposed into two sub-problems of beamforming optimization and antenna position optimization through equivalent substitution. Then, the convex approximation methods are used to deal with the nonconvex constraints of sub-problems, and two sub-neural networks are constructed to calculate the sub-problems separately. Different from alternating optimization (AO), where two sub-problems are solved alternately and the solutions are influenced by the initial values, two sub-neural networks of proposed GML-JO with fixed channel coefficients are considered as local sub-tasks and the computation results are used to calculate the loss function of joint optimization. Finally, the parameters of sub-networks are updated using the average loss function over different sub-tasks and the solution that is robust to the initial value is obtained. Simulation results demonstrate that the proposed GML-JO algorithm achieves 5.6 bits/s/Hz WSR within 100 iterations, yielding a 32.7% performance enhancement over conventional AO with substantially reduced computational complexity. Moreover, the proposed GML-JO algorithm is robust to different choices of initialization and yields better performance compared with the existing optimization methods.

With the rapid growth of high-speed rail (HSR) networks, reliable communication is increasingly challenging due to complex terrain and coverage gaps in remote areas. Uncrewed aerial vehicles (UAVs), with their mobility and flexibility, provide aerial relay support to bridge these gaps, enhance signal strength, and improve HSR communication reliability. However, their performance in millimeter-wave (mmWave) systems is significantly degraded by mechanical jitter caused by environmental factors such as wind and turbulence, which adversely affects beam alignment and overall communication quality. To address these challenges, this work introduces a comprehensive analytical framework. We first develop a statistical model that characterizes the relationship between beam gain and jitter intensity. Subsequently, closed-form expressions are derived for the outage probability and ergodic data rate of UAV-assisted HSR mmWave systems under jitter influence, taking into account both co-located (CA) and distributed antenna (DA) configurations. Furthermore, we propose an adaptive beamwidth design that maximizes the average ergodic rate by adjusting the beamwidth according to UAV jitter severity. Numerical simulations verify that this approach significantly improves system capacity and robustness compared with conventional static beamforming, confirming its effectiveness.

The challenges associated with channel state information (CSI) acquisition in the frequency division duplexing (FDD) extremely large-scale MIMO (XL-MIMO) system significantly impede its application in 6G communications. In such context, this contribution introduces a CSI feedback framework with pilot precoding capitalizing on the partial FDD reciprocity towards significantly enhanced efficiency. Specifically, by exploiting the near-field channel sparsity in the polar-delay domain, the proposed polar-delay sparsity (PDS) codebook remarkably reduces pilot and CSI feedback overhead while maintaining high spectral efficiency (SE). Furthermore, a class of innovative compression and decompression methods are also developed to enable further reduction of training overhead without compromising system performance. Notably, the proposed design ensures consistently low computational complexity and feedback overhead at the user equipment (UE), making it well-suited for the massive connectivity demands of heterogeneous devices in 6G Internet of Everything (IoE) applications. Finally, simulation results demonstrate the satisfactory SE performance enhancement achieved by the proposed schemes.

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

Low Earth orbit (LEO) satellite communications are the key enabler for achieving 6G ubiquitous connectivity. With the rapid progress of small satellite technology and the surging demands on direct-to-satellite services, a global wave of building LEO satellite constellations has been arisen. LEO satellite communications are the typical high mobility scenarios and suffer from severe Doppler effects. To overcome this challenge, orthogonal time frequency space (OTFS)-based LEO satellite communications have recently been studied, which exploit high mobility to obtain delay-Doppler diversity. However, due to limited satellite transmit power and very long propagation distance, the satellite-to-ground (S2G) links are very weak, and also suffer from inter-beam and inter-satellite interference. In this paper, we study coordinated multi-satellite transmission for OTFS-based LEO satellite communications to significantly improve the performance of S2G transmission, through enabling multiple satellites to cooperatively serve ground users. Furthermore, considering different delay and Doppler offsets among cooperative LEO satellites, we propose simultaneous pilots-based aggregate channel estimation (SP-ACE) scheme to improve channel estimation, which aggregately estimates the channels in S2G joint transmission by regarding the channels of all cooperative links as a single channel. Besides integer Doppler, we also consider fractional Doppler and propose three-stage peak-searching correlation (PSC)-based fractional Doppler estimation. Finally, simulations are conducted and the results demonstrate the effectiveness of the proposed coordinated multi-satellite transmission scheme, SP-ACE and three-stage PSC fractional Doppler estimation schemes.

Multi-modal semantic communication (MSC) has gained great attention due to its multi-modal processing ability. However, the existing MSC systems are mainly built on multi-modal large models that lead to inefficient computation on non-essential tokens, potentially restricting MSC from achieving more advanced levels of intelligence. To address this challenge, we propose a large model-empowered MSC system with a cross-modal attention-based token selection mechanism, denoted as LMECM-SC, which effectively utilizes the attention score across multi-modal tokens to filter out noisy or unuseful tokens, selectively learning the tokens that best benefit downstream applications. Meanwhile, we introduce the multi-modal adaptive semantic encoder and decoder that dynamically assign weights to encode multi-modal semantic information extracted from the selected tokens based on their modality and integrate semantic information with cross-modal attention scores at the receiver, optimizing the performance on downstream tasks. Experiment results indicate that LMECM-SC effectively reduces the number of tokens used for training, outperforming four baseline methods in terms of bilingual evaluation understudy score for text, learned perceptual image patch similarity for image, and perceptual evaluation of speech quality score for speech.

This paper investigates the covert communication network assisted by the multi-antenna relay, by taking into account two typical scenarios of eavesdropping channel state information (ECSI). In this network, the relay operates in half-duplex amplify-and-forward (AF) relaying protocol, where the relay either forwards the Alice’s confidential signal to the Bob by designing the precoding matrix to enhance the communication performance or sends artificial noise (AN) to disrupt the warden’s detection. For this covert communication system with either instantaneous ECSI (I-ECSI) or statistical ECSI (S-ECSI), an optimization problem is formulated with the aim of maximizing the received signal-to-noise ratio (SNR) at the Bob while ensuring the constraints on the transmit power and covert performance. To solve the non-convex optimization problem, an alternating optimization method is proposed through jointly optimizing the transmit power at Alice and precoding matrix at the relay. Numerical results are provided to demonstrate the effectiveness of our proposed method. In particular, our method is superior to the conventional ones up to 49.6% with I-ECSI and 57.9% with S-ECSI.

In this paper, we investigate the secure transmission design for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multiple-input multiple-output (MIMO) systems. By considering both perfect and imperfect channel state information (CSI) scenarios, we jointly optimize the covariance matrix of the transmitter and the transmitting and reflecting coefficients of the STAR-RIS and formulate two transmit power minimization problems. For the optimization problem in the perfect CSI scenario, we develop a penalty-based alternating optimization (AO) algorithm to handle it. For the optimization problem in the imperfect CSI scenario, this paper is the first work to study the robust beamforming design for STAR-RIS-assisted secure MIMO systems. To address this challenging problem, we first use the inequalities of the determinant to transform it into an equivalent form. Then, we use the generalized S-procedure to handle the worst-case constraints. Finally, we develop a penalty-based AO algorithm. Performance evaluation results show that the two proposed optimization algorithms significantly reduce the transmit power compared to other baseline schemes.

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

This paper studies the joint optimization of multi-objective dependent task scheduling, resource allocation, and service caching in an aerial-ground integrated mobile edge computing system that includes multiple uncrewed aerial vehicles (UAVs). These UAVs, in coordination with a high-altitude platform, work together to process numerous dependent tasks collected by the UAVs. The optimization problem involves two conflicting objectives that need to be minimized simultaneously: the average execution delay of all dependent tasks and the average energy consumption of all UAVs. The conflict between the two objectives makes the problem quite challenging. Recently, some multi-objective approaches, such as multi-objective evolutionary algorithms (MOEAs), have been introduced to address dependent task scheduling. However, these approaches often suffer from premature convergence and tend to fall into local optima. To address these issues, we propose a modified MOEA based on decomposition that incorporates two performance-improving strategies. The first one is a probability-based neighborhood search strategy that selects two individuals to update neighborhood individuals based on the neighborhoods and external population, thereby improving population updating efficiency. The second one is a dynamic voltage and frequency scaling-based energy reduction strategy that further enhances the quality of solutions by adjusting the computing frequencies. Experimental results verify that the proposed algorithm obtains a number of outstanding nondominated solutions and achieves a better balance between objectives compared with several algorithms.

The development of Internet of Things (IoT) networks causes an increasing demand for high-quality video streaming, which results in the burden of traditional mobile cloud computing (MCC) networks, such as high energy consumption and dissatisfaction with the user’s Quality of Experience (QoE). To address this issue, mobile-edge computing (MEC) networks have recently been widely employed for high-quality video streaming scenarios under time-varying wireless channels. However, in MEC networks, limited resources deteriorate the video transmission rate and the quality of videos. Therefore, in this article, we investigate a MEC network with cloud-edge integration for adaptive bitrate (ABR) video streaming, where the edge server (ES) performs cloud-edge selection to decide whether the requested video should be directly obtained from the cloud server (CS) or through a transcoding process. We first design edge caching and transcoding processes to enhance resource utilization and reduce computational consumption through bitrate adaptation strategy and cloud-edge selection decision. Moreover, we utilize the energy efficiency by jointly considering the user’s QoE and energy consumption to measure the network’s performance, and then formulate the optimization objective to maximize energy efficiency. In further, we employ a deep deterministic policy gradient (DDPG)-based scheme to solve the optimization problem by applying video quality adaptation technique, allocating computational resources and transmit power. Finally, simulation results demonstrate that the proposed scheme accomplishes superior energy efficiency compared to the competing schemes at least 41.1%.

In this paper, we investigate a sensing-enabled integrated space-air-ground (SAG) data collection network, in which an unmanned aerial vehicle (UAV) can not only work singly to sense data from multiple targets but also collaborate with a low-earth orbit (LEO) satellite to collect communication data from multiple users. Since the coverage of the UAV is much smaller than that of the LEO satellite, we first determine the set of usable users and targets for the UAV by analyzing the signal-to-noise ratios between the UAV and the users and targets. Based on this, we pose an optimization problem designed to maximize the total amount of data collected in the network while satisfying the constraints of UAV energy consumption, memory capacity, and minimum amount of sensor data per target. Moreover, considering that the network consists of three layers and the UAV has dual functions of communication and sensing, this problem is solved by jointly optimizing the scheduling of the users’ data upload scheme, the UAV trajectory, and the allocation of communication and sensing time. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, so it is difficult to find the optimal solution. Therefore, we further design an alternating iterative optimization algorithm (AIOA) framework to find an appropriate solution. Specifically, we alternately optimize the UAV trajectory, time allocation strategy, and data upload schedule in each iteration. Finally, simulation experiments validate the effectiveness of the AIOA and its superiority over other benchmarks in terms of the amount of data collected.

Driven by the ever-increasing requirements of ultrahigh spectral efficiency, ultralow latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next-generation multiple access schemes to facilitate the provisioning of these stringent demands. This inspires the embrace of nonorthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats due to the open nature of the air interface, the broadcast characteristic of radio propagation, and the intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security-aware design; for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview of the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends.

Recently, there has been a growing interest in learning-based semantic communication because it can prioritize the preservation of meaningful semantic information over the accuracy of the transmitted symbols, resulting in improved communication efficiency. However, existing learning-based approaches still face limitations in defining semantic level loss and often struggle to find a good trade-off between preserving semantic information and preserving intricate details. In addition, the existing semantic communication approaches cannot effectively train semantic encoders and decoders without the support of downstream models. To address these limitations, this paper proposes a contrastive learning (CL)-based semantic communication system. First, inspired by practical observations, we introduce the concept of semantic contrastive loss and propose a semantic contrastive coding (SemCC) approach that treats data corruption during transmission as a form of data augmentation within the CL framework. Moreover, we propose a semantic re-encoding (SemRE) operation, which uses a duplicate of the semantic encoder deployed at the receiver to guide the entire training process when the downstream model is inaccessible. Further, we design the training procedure for SemCC and SemRE approaches, respectively, to balance the semantic information and intricate details. Finally, simulations are performed to demonstrate the superiority of the proposed approaches over competing approaches. In particular, our approaches achieve a significant accuracy improvement of up to 53% on the CIFAR-10 dataset with a bandwidth compression ratio of 1/24, and also obtain comparable image reconstruction quality as the bandwidth compression ratio is improved.

A robust transmission scheme for intelligent reflecting surface (IRS) aided cognitive radio systems is designed to provide security against internal eavesdropping. By considering the secondary receiver as an internal eavesdropper, a total transmit power (TTP) minimization problem is formulated. Through a novel optimization strategy which jointly considers the transmit beamforming vector at the primary transmitter, the transmit beamforming vector at the secondary transmitter and the phase shifts at the IRS, a solution to the formulated problem is presented. By considering perfect channel state information (CSI) and imperfect CSI scenarios, two optimization algorithms are proposed both aiming at reducing the TTP. For the former scenario, instead of applying the commonly used inner approximation (IA) algorithm which cannot handle maximum allowable leaked rate constraint, a novel penalty-based IA algorithm is proposed. For the latter scenario, a non-convex robust optimization problem is formulated. Because of its non-convex nature, a novel upper-bounding technique and S-procedure are used to transform it into a more tractable form which is then solved by deriving of a penalty convex-concave procedure based alternating optimization algorithm. Performance results show that, as compared to other baseline schemes, the proposed algorithms significantly reduce the TTP.

Recently, semantic communication has been widely applied in wireless image transmission systems as it can prioritize the preservation of meaningful semantic information in images over the accuracy of transmitted symbols, leading to improved communication efficiency. However, existing semantic communication approaches still face limitations in achieving considerable inference performance in downstream AI tasks like image recognition, or balancing the inference performance with the quality of the reconstructed image at the receiver. Therefore, this paper proposes a contrastive learning (CL)-based semantic communication approach to overcome these limitations. Specifically, we regard the image corruption during transmission as a form of data augmentation in CL and leverage CL to reduce the semantic distance between the original and the corrupted reconstruction while maintaining the semantic distance among irrelevant images for better discrimination in downstream tasks. Moreover, we design a two-stage training procedure and the corresponding loss functions for jointly optimizing the semantic encoder and decoder to achieve a good trade-off between the performance of image recognition in the downstream task and reconstructed quality. Simulations are finally conducted to demonstrate the superiority of the proposed method over the competitive approaches. In particular, the proposed method can achieve up to 56% accuracy gain on the CIFAR10 dataset when the bandwidth compression ratio is 1/48.

This paper studies a multi-tier cache-aided relaying network, where the destination$D$is randomly located in the network and it requests files from the source$S$through the help of cache-aided base station (BS) and$N$relays. In this system, the multi-tier architecture imposes a significant impact on the system collaborative caching and file delivery, which brings a big challenge to the system performance evaluation and optimization. To address this problem, we first evaluate the system performance by deriving analytical outage probability expression, through fully taking into account the random location of the destination and different file delivery modes related to the file caching status. We then perform the asymptotic analysis on the system outage probability when the signal-to-noise ratio (SNR) is high, to enclose some important and meaningful insights on the network. We further optimize the caching strategies among the relays and BS, to improve the network outage probability. Simulations are performed to show the effectiveness of the derived analytical and asymptotic outage probability for the proposed caching strategy. In particular, the proposed caching is superior to the conventional caching strategies such as the most popular content (MPC) and equal probability caching (EPC) strategies.

In this paper, we study a relay-assisted federated edge learning (FEEL) network under latency and bandwidth constraints. In this network,$N$users collaboratively train a global model assisted by$M$intermediate relays and one edge server. We firstly propose partial aggregation and spectrum resource multiplexing at the relays in order to improve the communication of the relay-assisted FEEL system. Furthermore, we derive analytical and asymptotic expressions of the system outage probability and convergence rate. For the purpose of improving the system performance, we further optimize the relay-assisted FEEL network by maximizing the number of users who participate in each round of federated learning, through allocation of the wireless bandwidth among users and relays. Specifically, two bandwidth allocation (BA) schemes have been proposed, assuming either instantaneous or statistical channel state information (CSI). Simulations show the advantages of the proposed BA schemes over other benchmarks, regarding the accuracy and convergence rate of the considered relay-assisted FEEL network.

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

This paper presents the first performance analysis of physical layer downlink secure transmissions in a scalable cell-free massive MIMO (SCF-mMIMO) system. A stochastic geometry approach is used to model the locations of the access points (APs), user equipments (UEs) and eavesdroppers (Eves) as independent homogeneous Poisson point processes (HPPPs). In addition to applying maximum ratio transmission (MRT) to send the confidential messages, null-space artificial noise is also injected for secrecy enhancement. We analytically characterize the secrecy performance in terms of both the outage-based secrecy transmission rate (STR) and the ergodic secrecy rate (ESR), appropriate for slow quasi-static fading channels and fast block-fading channels, respectively. By utilizing moment matching and Gil-Pelaez inversion theorem, we are able to obtain mathematically tractable approximations for the performance metrics. These approximations are shown to have high accuracy as compared to simulation results. Our numerical results reveal useful design insights that cannot be inferred from existing studies. These insights answer important questions such as whether it is best to deploy as many APs each with fewer antennas and to what extent the artificial noise insertion is beneficial.

A scalable cell-free massive MIMO (SCF-mMIMO) system where all user equipments (UEs) and access points (APs) employ finite resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) over correlated Rician fading is presented and analysed in this paper. A closed-form expression for the uplink (UL) spectral efficiency (SE) using maximal-ratio combining (MRC) detection for centralized scheme is first derived. Moreover, a novel low complexity partial MMSE (P-MMSE) detector is proposed, which achieves very similar SE performance and maintains much less computational complexity in comparison with the original partial MMSE (P-MMSE) detector. In addition, a joint algorithm consisting of AP cluster formation, pilot assignment, and power control policy is proposed, which yields much higher SE performance than random pilot assignment and user-group based pilot assignment policies do, and meanwhile improves the quality of service (QoS) fairness for all accessing UEs as compared to the equal power transmit policy.

Semantic communication is envisioned as a promising technique to break through the Shannon limit. However, the existing semantic communication frameworks do not involve inference and error correction, which limits the achievable performance. In this paper, in order to tackle this issue, a cognitive semantic communication framework is proposed by exploiting knowledge graph. Moreover, a simple, general and interpretable solution for semantic information detection is developed by exploiting triples as semantic symbols. It also allows the receiver to correct errors occurring at the symbolic level. Furthermore, the pre-trained model is fine-tuned to recover semantic information, which overcomes the drawback that a fixed bit length coding is used to encode sentences of different lengths. Simulation results on the public WebNLG corpus show that our proposed system is superior to other benchmark systems in terms of the data compression rate and the reliability of communication.

In this paper, we investigate the stability and effectiveness of intelligent reflecting surface (IRS) aided systems in the context of mobile multi-users and time-varying channel status. Different from the previous researches in the IRS-aided communication mostly based on one or more independent channel realization, we consider dynamic channel status varying with the mobility of users. Specifically, a dynamic problem as maximizing the time-average rate of all users is formulated. A fractional programming method based on Lagrangian dual theory is proposed as a solution. Simulation results demonstrate that the IRS can be more efficient than amplified forward (AF) relay in adapting the dynamically changing channels stably.

With the evolution of mobile terminals and the development of user demands, the existing mobile system is facing with serious challenges, such as the nearly saturated spectrum, limited processing capability of mobile terminals, and the high complexity of emerging technologies. To deal with these challenges, 6G is attracting extensive attentions. In this paper, we firstly investigate the potential technologies for 6G from the view of network design and resource management, then highlight the potential challenges for resource allocation, imposed for satisfying multi-demands in future hyper-heterogeneous networks with massive different devices and applications. In addition, the existing criteria for resource allocation are summarized for their common forms and limitations when dealing with multi-demands. Considering the limitations of the existing criteria, a novel quality-of-experience (QoE)-aware criterion on demand side is introduced toward efficient resource allocation in 6G, including its modeling, the potential application forms and typical scenarios, the future directions and challenges.

In radar sensing and communications, designing Doppler resilient sequences (DRSs) with low ambiguity function for delay over the entire signal duration and Doppler shift over the entire signal bandwidth is an extremely difficult task. However, in practice, the Doppler frequency range is normally much smaller than the bandwidth of the transmitted signal, and it is relatively easy to attain quasi-synchronization for delays far less than the entire signal duration. Motivated by this observation, we propose a new concept called low ambiguity zone (LAZ) which is a small area of the corresponding ambiguity function of interest defined by the certain Doppler frequency and delay. Such an LAZ will reduce to a zero ambiguity zone (ZAZ) if the maximum ambiguity values of interest are zero. In this paper, we derive a set of theoretical bounds on periodic LAZ/ZAZ of unimodular DRSs with and without spectral constraints, which include the existing bounds on periodic global ambiguity function as special cases. These bounds may be used as theoretical design guidelines to measure the optimality of sequences against Doppler effect. We then introduce four optimal constructions of DRSs with respect to the derived ambiguity lower bounds based on some algebraic tools such as characters over finite field and cyclic difference sets.

We investigate the optimal signal detection problem in large-scale multiple-input multiple-output (MIMO) system with the generalized spatial modulation (GSM) scheme, which can be formulated as a closest lattice point search (CLPS). To identify invalid signals, an efficient pruning strategy is needed while searching on the GSM decision tree. However, the existing algorithms have exponential complexity, whereas they are infeasible in large-scale GSM-MIMO systems. In order to tackle this problem, we propose a memory-efficient pruning strategy by leveraging the combinatorial nature of the GSM signal structure. Thus, the required memory size is squared to the number of transmit antennas. We further propose an efficient memory-bounded maximum likelihood (ML) search (EM-MLS) algorithm by jointly employing the proposed pruning strategy and the memory-bounded best-first algorithm. Theoretical and simulation results show that our proposed algorithm can achieve the optimal bit error rate (BER) performance, while its memory size can be bounded. Moreover, the expected time complexity decreases exponentially with increasing the signal-to-noise ratio (SNR) as well as the system’s excess degree of freedom, and it often converges to squared time under practical scenarios.

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available athttps://github.com/skypitcher/hats.

In this paper, we investigate a secure mobile edge computing (MEC) network in the presence of multiple eavesdroppers, where multiple users can offload parts of their tasks to the computational access point (CAP). The multiple eavesdroppers may overhear the confidential task offloading, which leads to information leakage. In order to address this issue, we present the minimization problem of the secrecy outage probability (SOP), by jointly taking into account the constraints from the latency and energy consumption. With the aim to improve the system secrecy performance, we then introduce three user selection criteria to choose the best user among multiple ones. Specifically,criterion Imaximizes the locally computational capacity, whilecriterion IIandIIImaximize the secrecy capacity and data rate of main links, respectively. For these criteria, we further analyze the system secrecy performance by deriving analytical and asymptotic expressions for the SOP, from which we can conclude important insights for the system design. Finally, simulation and analytical results are provided to verify the proposed analysis. The results show that the three criteria can efficiently safeguard the MEC networks, compared to the traditional local computing and fully offloading, especially with a large value of user number.

In the vision of the next generation communications, the wireless devices may work on different transmission media, such as microwave and visible light. In this case, how to bridge these different devices remains an open challenge. Following the framework of [1], we consider a cross-media base station (BS) for supporting devices working on different media, as visible light and radio frequency (RF). Specifically, we conceive two criteria for power allocation toward enhanced performance, by maximizing the sum and minimum rates. Furthermore, we also analyze the impact of the position of BS to the system performance. Finally, the theoretical results are verified by simulations for supporting the effectiveness of the developed power allocation schemes in cross-media communications.

As a potential development direction of future transportation, the vacuum tube ultra-high-speed train (UHST) wireless communication systems have newly different channel characteristics from existing high-speed train (HST) scenarios. In this paper, a three-dimensional non-stationary millimeter wave (mmWave) geometry-based stochastic model (GBSM) is proposed to investigate the channel characteristics of UHST channels in vacuum tube scenarios, taking into account the waveguide effect and the impact of tube wall roughness on channel. Then, based on the proposed model, some important time-variant channel statistical properties are studied and compared with those in existing HST and tunnel channels. The results obtained show that the multipath effect in vacuum tube scenarios will be more obvious than tunnel scenarios but less than existing HST scenarios, which will provide some insights for future research on vacuum tube UHST wireless communications.

In this paper, a novel jointly adaptive transmission strategy for buffer-aided multiple-relay non-orthogonal multiple access (NOMA) networks is proposed. By leveraging time diversity brought by the data storage buffer, and multiple relays’ beamforming gains, the source node broadcasts data in the first hop and all relays perform distributed beamforming to transmit information to users with NOMA in the second hop. Our focus is on maximizing the average network throughput (ANT) by jointly optimizing the mode selection, power and rate allocation at the source node, as well as the rate allocation and distributed beamforming coefficients at the relays along the time. As a solution to this complex problem, we propose an online scheduling scheme, which first adopts the Lyapunov optimization methodology to convert the ANT maximization problem into the drift-plus-penalty function minimization problem at each time slot. Secondly, focusing on the non-convexity of the transformed problem at each time slot, a novel two-loop algorithm is proposed as an effective solution. Extensive performance evaluation results have revealed the superiority of the proposed scheme as compared to other benchmark access schemes, namely, buffer-aided single relay NOMA, maximum ratio transmission based NOMA and buffer-aided time division multiple access schemes.

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

In this paper, we investigate a secure relaying network with simultaneous wireless information and power transfer (SWIPT). It is assumed that Alice wants to send confidential information to Bob under the existence of a passive eavesdropper (Eve), while the relay is equipped with a data buffer and an energy storage device. More specifically, we aim at achieving higher secrecy throughput, while retaining the stability of the data and energy queues. To achieve this with acceptable complexity, we transform the original long-term stochastic optimization problem into a series of online subproblems using the framework of Lyapunov optimization. The proposed scheme shows that the optimal time switching factor is 0 or 1, which is different from the conventional secure relaying network with SWIPT. In addition, simulation results verify that the proposed scheme can improve the secrecy throughput compared with the baseline scheme.

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

Cooperative non-orthogonal multiple access (C-NOMA) and buffering are promising techniques to improve spectrum efficiency in the next generation of wireless networks. In this article, a buffer-aided cooperative NOMA network with direct links is studied. The throughput maximization problem is firstly formulated under the assumption of fixed power allocation and optimally solved by designing a mode selection policy. In order to further improve system throughput, the problem is extended into the case that power allocation and mode selection are jointly optimized. An optimal solution is obtained, while a sub-optimal one is also provided in order to decrease the implementation complexity. Furthermore, considering the case where the buffer has finite size and the users are delay-sensitive, a throughput-delay aware strategy is also presented. Moreover, it is shown that the proposed schemes outperform the baseline one in terms of throughput. Finally, simulations demonstrate that the sub-optimal solution achieves similar performance to the optimal one, while significantly reduces the implementation complexity.

In this article, we investigate a communication and computation problem for industrial Internet of Things (IoT) networks, where K relays can help accomplish the computation tasks with the assist of M computational access points. In industrial IoT networks, latency and energy consumption are two important metrics of interest to measure the system performance. To enhance the system performance, a three-hierarchical optimization framework is proposed to reduce the latency and energy consumption, which involves bandwidth allocation, off-loading, and relay selection. Specifically, we first optimize the bandwidth allocation by presenting three schemes for the second-hop wireless relaying. We then optimize the computation off-loading based on the discrete particle swarm optimization algorithm. We further present three relay selection criteria by taking into account the tradeoff between the system performance and implementation complexity. Simulation results are finally demonstrated to show the effectiveness of the proposed three-hierarchical optimization framework.

Non-orthogonal multiple access (NOMA) has recently attracted the academic and industrial interest, due to offering higher spectral efficiency and connectivity compared to conventional orthogonal multiple access schemes. In this paper, buffer-aided relaying for a downlink NOMA system with direct links is proposed, while in order to take advantage the extra degrees of freedom, appropriate transmission modes are presented. Targeting at the throughput maximization, the corresponding optimization problem is formulated and solved and theoretical expressions are derived for the optimal mode selection policy and maximum throughput. Finally, simulation results illustrate the efficiency of the proposed scheme and its superiority compared with a previously presented baseline scheme.

In this paper, we investigate the security of a cache-aided multi-relay communication network in the presence of multiple eavesdroppers, where each relay can pre-store a part of the requested files in order to assist secure data transmission from source to destination. If the relays have cached the requested file, then they can directly send it to the destination; otherwise, traditional dual-hop data transmission is used. For both cases, relay selection is performed to assist the secure data transmission. We analyze the network secrecy performance in both scenarios ofnon-colludingandcolludingeavesdroppers, and obtain a closed-form expression for the average secrecy outage probability (SOP), as well as an asymptotic expression for the high main-to-eavesdropper ratio (MER). Through minimizing the network SOP, we further optimize the cache placement by proposing a stochastic sampling based cache learning (SacLe) strategy, which can be implemented in parallel and thus reduces the implementation latency substantially. Numerical and simulation results are finally presented to verify the proposed analysis, and show that the caching strategy has a significant impact on the network secrecy performance through affecting the caching diversity gain and signal cooperation gain at the relays. The proposed SacLe strategy is shown to be able to achieve the optimal performance obtained by the brute force (BF) algorithm.

In this paper, we consider a radio-frequency identification (RFID) backscatter system with two scenarios: single input multiple output (SIMO) and multiple input single output (MISO). We investigate the impact of channel correlation between the forward and backscatter links on the symbol error rate (SER), and we compare the performance of SIMO and MISO RFID systems with maximum ratio combining reception over arbitrarily correlated Nakagami-m fading channels. In order to gain an insight for the system design, closed-form expressions for the asymptotic SER and an upper bound are derived for M-ary phase-shift keying and quadrature amplitude modulation. The diversity order in the fully correlated cases, revealed from these expressions, is half of that in the partially correlated and uncorrelated cases. Finally, simulations are performed to validate the theoretical analysis.

In this paper, we study decode-and-forward relaying networks in the presence of direct links, where they are used by the eavesdropper to overhear the confidential message from the source and relay. The secure data transmission can go through from either the direct or the relaying branch, and we focus on the practical communication scenarios, where the main and eavesdropper channels are correlated. Although traditional opportunistic selection techniques can choose one better branch to ensure the secure performance, it needs to continuously know the channel state information (CSI) of both branches and may result in a high branch switching rate. To overcome these limitations, we propose a distributed secure switch-and-stay combining (DSSSC) protocol, where only one between direct and relaying branches is activated to assist the secure data transmission, and the switching occurs when the branch cannot support the secure communication any longer. The DSSSC protocol uses either the instantaneous or the statistics of the eavesdropping CSI. For both cases, we quantify the impact of correlated fading on secure communication by deriving an analytical expression for the secrecy outage probability (SOP) as well as an asymptotic expression for the high main-to-eavesdropper ratio region. From the asymptotic SOP, we can conclude that the DSSSC can achieve the optimal secure performance of opportunistic selection with less implementation complexity, and the channel correlation can further enhance the transmission security.

Radio frequency identification (RFID) backscatter communication systems play an important part in the future Internet of Things (IoT) applications. In this paper, we consider a multiple input single output (MISO) backscatter system, where the reader is equipped with multiple antennas and the tag is equipped with single antenna. The impact of the channel correlation between the forward and backscatter links on the symbol error rate (SER) is investigated. Then, we derive expressions for asymptotic SER and an upper bound, assuming M-ary phase-shift keying (M-PSK) and quadrature amplitude modulation (M-QAM) with maximum ratio combining (MRC) reception, over arbitrarily correlated Nakagami-m fading channels. From these expressions, we can directly obtain the diversity order. Finally, numerical results and simulations are provided to demonstrate the accuracy of the theoretical expressions.

Airborne radio access networks (A-RANs) is a particularly promising technology due to its ability to offer fast, cost-efficient, and on-demand enhancement of the existing telecommunication infrastructure. The main challenges of ARANs are the energy sustainability of the aerial platforms (APs) and the establishment of reliable links with the ground nodes. To this direction, we propose a novel approach, which is based on mixed free-space optical (FSO)/radio frequency (RF) relaying protocol and simultaneous lightwave information and power transfer (SLIPT). In this context, we also formulate and optimally solve the max-min fairness problem, which regulates the trade-off between the energy and information transfer to the AP and allocates the available resources to multiple endusers. Finally, the impact of the number of users and weather conditions on system's optimal configuration and performance is investigated through simulations. The offered results provide meaningful theoretical and practical insights on the capabilities of the proposed scheme.

Ultra-dense networks (UDNs) are the promising technology for the fifth-generation wireless networks and beyond to significantly boost network capacity and improve network coverage by exploiting spatial spectrum reuse through the deployment of massive base stations (BSs). In this paper, the fundamental tradeoffs of non-orthogonal multicast, multicast, and unicast in the UDN are studied, to understand the impact of network densitification on them and provide some insights on UDN deployment. Non-orthogonal multicast with imperfect channel estimation and successive interference cancellation is also investigated. To evaluate the performance, a tractable model for performance analysis is developed by using stochastic geometry, and then the analytical expressions for downlink signal-to-interference-plus-noise ratio coverage probability, spectrum efficiency, area traffic capacity, and energy efficiency are derived. The numerical results together with the Monte Carlo simulations are also provided. The results demonstrate that non-orthogonal multicast can further improve the performance of multicast and achieve higher spectrum efficiency, area traffic capacity, and energy efficiency than unicast from the low-to-high BS density regions, but suffers from inferior performance to unicast in the very high BS density region. The results also show that the non-orthogonal multicast and multicast exhibit different performance trends from unicast.

The power allocation for multi-user sparse code multiple access (SCMA) downlink systems with finite- alphabet constraints is investigated. An explicit expression for the achievable rate for downlink SCMA systems with finite-alphabet inputs is derived, which is applicable to arbitrary number of users. Moreover, a novel power allocation scheme that can ensure users' fairness for multi-user SCMA downlink systems is proposed. In an effort to solve the formulated non-convex optimization problem, a low- complexity polynomial algorithm is proposed, which yields an optimal solution. Simulation results demonstrate that the proposed power allocation algorithm is capable of enhancing the performance significantly compared to the equal power allocation scheme.

In this paper, the challenging problem of data detection for orthogonal frequency division multiplexing (OFDM) systems under rapidly time- varying channels is considered. Time-varying channels within a multicarrier symbol will lead to a loss of sub-channel orthogonality, and result in inter-channel interference (ICI) and an irreducible error floor in traditional receivers. The genetic algorithm (GA) assisted data detection for the single input single output (SISO) and single input multiple output (SIMO) OFDM systems are presented, respectively. Theoretical analysis and simulations show that the proposed algorithms are valid compared with minimum mean square error (MMSE) and minimum mean square error successive interference cancellation (MMSE-SIC). The GA-assisted data detection for OFDM systems under rapidly time- varying channels is flexible to provide tradeoff between performance and complexity. To accelerate the convergence of GA for SIMO OFDM, the individuals of GA are selected based on the concept of Pareto optimality.

Non-orthogonal multiple access (NOMA) is an essential enabling technology for the fifth-generation (5G) wireless networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The key idea behind NOMA is to serve multiple users in the same resource block, such as a time slot, subcarrier, or spreading code. The NOMA principle is a general framework, and several recently proposed 5G multiple access schemes can be viewed as special cases. This survey provides an overview of the latest NOMA research and innovations as well as their applications. Thereby, the papers published in this special issue are put into the context of the existing literature. Future research challenges regarding NOMA in 5G and beyond are also discussed.

Sparse code multiple access (SCMA) is a new non- orthogonal multiple access scheme, which effectively exploits the shaping gain of multi-dimensional codebook. In this paper, a multi-dimensional SCMA (MD-SCMA) codebook design based on constellation rotation and interleaving method is proposed for downlink SCMA systems. In particular, the first dimension of mother constellation is constructed by subset of lattice Z2. Then the other dimensions are obtained by rotating the first dimension. Further, the interleaving is used for even dimensions to improve the performance in fading channels. In this way, we can design different codebooks for the aim of spectral efficiency or power efficiency. And the simulation results show that the bit error rate (BER) performance of MD-SCMA codebooks outperforms that of the existing SCMA codebooks and low density signature (LDS) in downlink Rayleigh fading channels.

Interference alignment (IA) is a well-known transmission strategy for interference channels, with which the sum rate can linearly scale with the number of users at high signal-to-noise ratio (SNR). However, most existing works on IA considered only the maximization of the sum rate without taking into account the bit error rates (BER) performance. In this work, we firstly propose a novel IA algorithm to improve the BER performance for MIMO-OFDM interference channels on a per-subcarrier basis. The proposed IA algorithm is then combined with the sparse code multiple access (SCMA) technique to further enhance the BER performance by exploiting the inherent frequency diversity in the MIMO-OFDM systems. At the receiver, a linear filter is first used to mitigate the inter-user aligned interference and separate the spatial streams, and then an iterative frequency-domain message passing algorithm (MPA) is applied to efficiently separate the overlapped multiple codewords. Simulation results demonstrate that the proposed joint IA and SCMA strategy can provide significant BER gain compared to the existing IA algorithms.

In this paper, an optimized codebook design based on rotated angles and extrinsic information transfer (EXIT) chart for a non-orthogonal multiple access scheme, called irregular sparse code multiple access (IrSCMA), is presented. Unlike regular SCMA, in IrSCMA, the defined degree of layer's resource nodes is different, which is beneficial to system performance.It is demonstrated that the new codebook can greatly improve the BER performance especially when the signal-to-noise ratio(SNR) is high, without sacrificing the low detection complexity, compared with the existing codebooks and LDS.

Abstract In this paper, we consider switch‐and‐stay combining (SSC) in two‐way relay systems with two amplify‐and‐forward relays, one of which is activated to assist the information exchange between the two sources. The system operates in either analog network coding (ANC) protocol where the communication is only achieved with the help of the active relay or time‐division broadcast (TDBC) protocol where the direct link between two sources can be utilized to exploit more diversity gain. In both cases, we study the outage probability and bit error rate (BER) for Rayleigh fading channels. In particular, we derive closed‐form lower bounds for the outage probability and the average BER, which remain tight for different fading conditions. We also present asymptotic analysis for both the outage probability and the average BER at high signal‐to‐noise ratio. It is shown that SSC can achieve the full diversity order in two‐way relay systems for both ANC and TDBC protocols with proper switching thresholds. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract We consider a multi‐source two‐way relay network, in which one source communicates with N other sources (n = 1,2,…,N) with the help of a single amplify‐and‐forward relay. We propose two opportunistic source scheduling schemes in such a network. According to the proposed schemes, in each transmission interval, only a single out of the N sources is selected, and this selected node acts as either transmitter or receiver depending on the channel conditions. For both schemes, tight closed‐form lower bounds of outage probability and bit error rate (BER) are derived. Asymptotic outage probability and BER that are valid for high signal‐to‐noise ratio regime are also analyzed, which can provide important insights on the impact of system parameters. The analytical results show that the full diversity order N + 1 can be achieved by both proposed schemes. Simulation results are also presented to corroborate the analysis. Copyright © 2014 John Wiley & Sons, Ltd.

A multiuser scheduling multiple-input multiple-output (MIMO) cognitive radio network (CRN) with space-time block coding (STBC) is considered in this paper, where one secondary base station (BS) communicates with one secondary user (SU) selected from K candidates. The joint impact of imperfect channel state information (CSI) in BS → SUs and BS → PU due to channel estimation errors and feedback delay on the outage performance is firstly investigated. We obtain the exact outage probability expressions for the considered network under the peak interference power IPat PU and maximum transmit power Pmat BS which cover perfect/imperfect CSI scenarios in BS → SUs and BS → PU. In addition, asymptotic expressions of outage probability in high SNR region are also derived from which we obtain several important insights into the system design. For example, only with perfect CSIs in BS → SUs, i.e., without channel estimation errors and feedback delay, the multiuser diversity can be exploited. Finally, simulation results confirm the correctness of our analysis.

In this paper, we study the information-theoretical security of a downlink multiuser cooperative relaying network with multiple intermediate amplify-and-forward (AF) relays, where there exist multiple eavesdroppers which can overhear the message. To prevent the wiretap and strength the network security, we select one best relay and user pair, so that the selected user can receive the message from the base station assisted by the selected relay. The relay and user selection is performed by maximizing the ratio of the received signal-to-noise ratio (SNR) at the user to the eavesdroppers, which is based on both the main and eavesdropper links. For the considered system, we derive the closed-form expression of the secrecy outage probability, and provide the asymptotic expression in high main-to-eavesdropper ratio (MER) region. From the asymptotic analysis, we can find that the system diversity order is equivalent to the number of relays regardless of the number of users and eavesdroppers.

In this paper, we study a downlink multiuser network in which one base station sends its message to one of multiple users, assisted by one of multiple intermediate decode-and-forward (DF) relays. Multiple eavesdroppers exist at the receiver side, and they can overhear the message from the base station, which brings out the issue of information security. We consider moderate shadowing environments so that direct links from the base station to the users and eavesdroppers exist. Maximal ratio combing (MRC) technique is employed at the receivers to combine the signals from the relaying and direct links. We select the best user and relay pair by maximizing the received signal-to-noise ratio (SNR) at the user. We study the effects of the selection on the system secrecy performance by deriving the closed-form expression of secrecy outage probability. We also provide the asymptotic secrecy outage probability with high transmit power and high main-to-eavesdropper ratio (MER). From the asymptotic expression, we can find that the system can achieve the diversity of the total number of users and relays, irrespective of the number of eavesdroppers. Simulation and numerical results are finally demonstrated to verify the proposed studies.

In this paper, we investigate a multiuser cognitive relay network with direct source-destination links and multiple primary destinations. In this network, multiple secondary users compete to communicate with a secondary destination assisted by an amplify-and-forward (AF) relay. We take into account the availability of direct links from the secondary users to the primary and secondary destinations. For the considered system, we select one best secondary user to maximize the received signal-to-noise ratio (SNR) at the secondary destination. We first derive an accurate lower bound of the outage probability, and then provide an asymptotic expression of outage probability in high SNR region. From the lower bound and the asymptotic expressions, we obtain several insights into the system design. Numerical and simulation results are finally demonstrated to verify the proposed studies.

Long Term Evolution uses a new location update (LU) scheme, called a tracking area list (TAL)-based scheme, to overcome the defects of the LU scheme used in 2G and 3G cellular networks. Under the TAL-based LU scheme, each time a user equipment (UE) performs an LU, it is allocated a group of tracking areas, referred to as a TAL, within which the UE can move freely without any LU. The UE performs an LU when moving out of the TAL. The performance of the TAL-based LU scheme depends on the allocated TAL. In this paper we develop a mathematical model to analyze the signaling overhead of the TAL-based LU scheme for local UEs whose mobility exhibits strong regularity. We derive formulas for the LU cost and the paging cost of a TAL allocation strategy. With these formulas we can find an optimal TAL allocation strategy to minimize the signaling cost of the TAL-based LU scheme.

In this paper, a new partial power and rate adaptation scheme for orthogonal frequency division multiplexing (OFDM) systems is proposed in the presence of carrier frequency offset (CFO). The conventional adaptive scheme is shown to be a special case of the partially adaptive scheme technique which enables the resulting non-convex optimization problem, solved in a feasible way. It leads to a solution for optimal power adaptation that maximizes the spectral efficiency of an OFDM system using M-ary quadrature amplitude modulation (MQAM) under average power and instantaneous BER constraints. Closed-form expressions for the average spectral efficiency (ASE) of adaptive OFDM systems are derived. The theoretical results and computer simulations show that the range of the partial adaptation becomes narrow and the performance of constant power and continuous rate is very close to that of the partially adaptive power and continuous rate for higher CFO or high signal noise ratio (SNR) values.

This paper proposes relay selection to increase the physical layer security in multiuser cooperative relay networks with multiple amplify-and-forward relays, in the presence of multiple eavesdroppers. To strengthen the network security against eavesdropping attack, we present three criteria to select the best relay and user pair. Specifically, criteria I and II study the received signal-to-noise ratio (SNR) at the receivers, and perform the selection by maximizing the SNR ratio of the user to the eavesdroppers. To this end, criterion I relies on both the main and eavesdropper links, while criterion II relies on the main links only. Criterion III is the standard max-min selection criterion, which maximizes the minimum of the dual-hop channel gains of main links. For the three selection criteria, we examine the system secrecy performance by deriving the analytical expressions for the secrecy outage probability. We also derive the asymptotic analysis for the secrecy outage probability with high main-to-eavesdropper ratio. From the asymptotic analysis, an interesting observation is reached: for each criterion, the system diversity order is equivalent to the number of relays regardless of the number of users and eavesdroppers.

In this paper, we propose a multiuser cognitive relay network, where multiple secondary sources communicate with a secondary destination through the assistance of a secondary relay in the presence of secondary direct links and multiple primary receivers. We consider the two relaying protocols of amplify-and-forward (AF) and decode-and-forward (DF), and take into account the availability of direct links from the secondary sources to the secondary destination. With this in mind, we propose an optimal solution for cognitive multiuser scheduling by selecting the optimal secondary source, which maximizes the received signal-to-noise ratio (SNR) at the secondary destination using maximal ratio combining. This is done by taking into account both the direct link and the relay link in the multiuser selection criterion. For both AF and DF relaying protocols, we first derive closed-form expressions for the outage probability and then provide the asymptotic outage probability, which determines the diversity behavior of the multiuser cognitive relay network. Finally, this paper is corroborated by representative numerical examples.

This paper considers a two-phase two-way relay network (TWRN) with two sources and multiple amplify-and-forward (AF) relays, where the direct link between the sources exists and one best relay is chosen for data communication to maximize the minimum signal-to-noise ratio (SNR) of bidirectional communication. To efficiently exploit the direct link within two phases, a switch-and-stay combining (SSC) protocol is employed. In SSC, one branch out of the relay and direct branches is activated for data communication, and the branch switching occurs when the end-to-end SNRs fall below the given thresholds. We analyze the system performances over the independent but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels, by deriving lower bounds and asymptotic expressions with high SNR for the outage probability and bit error rate (BER). It is shown that SSC can preserve the same spectral efficiency as analog network coding (ANC), while can concurrently achieve the full diversity order as the optimal selection (OS) with less implementation complexity. Numerical and simulation results verify the proposed studies.

In this paper, we consider a two-way relay network (TWRN) consisting of two sources and N amplify-and-forward (AF) relays (N ≥ 1), among which the best relay is chosen to assist the data communication. In a time-varying fading channel, the relay selection may be based on the outdated channel state information (CSI). We study the impact of outdated relay selection on the system performance by deriving a tight lower bound for the outage probability in Rayleigh block-fading channels. We further provide an asymptotic analysis in high signal-to-noise ratio (SNR) region. From the asymptotic results, we can find that the system diversity order degenerates into unity as long as the CSI is outdated. Numerical results demonstrate the tightness of the performance bounds as well as the effects of outdated relay selection on the system performance. Simulation results are also provided to corroborate the theoretical analysis.

In this paper, switch-and-stay combining (SSC) is considered in two-way relay systems with amplify-and-forward (AF) relays, among which one is activated to assist the information exchange between two sources. We assume that the two-way relay system operates in the analog network coding (ANC) protocol where the communication can only be achieved through the active relay without the direct link. The outage probability and the average bit error rate (BER) for Rayleigh fading channels are studied. In particular, we derive closed-form lower bounds for the outage probability and the average BER, which is tight for different fading conditions. Moreover, we present asymptotic analysis for both the outage probability and the average BER at high signal-to-noise ratio (SNR) region. It is shown that SSC can achieve the full diversity order in two-way relay systems with proper switching thresholds. Numerical and simulation results are also provided to verify the theoretical study.

This study analyses the delay‐limited throughput performance of an uplink cellular relay network utilising hybrid automatic repeat request (HARQ) protocol in which one mobile station (MS) communicates with one base station (BS) with the aid of one amplify‐and‐forward relay station (RS). The authors’ analysis allows for a practical fading scenario where MS → RS and RS → BS channels undergo independent Rayleigh and Rician fading, respectively. Delay‐limited throughput expressions for two HARQ protocols are derived, respectively, namely Repetition Time Diversity and Incremental Redundancy. Furthermore, the impact of different parameters on the achievable throughput, such as the maximum HARQ round number L , line‐of‐sight signal K , information rate R etc., is analysed. The authors’ analysis unveils that, for the presumed dual‐hop cooperation model with the specified tolerable delay requirement and given channel condition, there exists an optimal information rate R * that achieves the maximum delay‐limited throughput for the two HARQ protocols. Moreover, it is disclosed that, both HARQ protocols with the adaptive optimised information rate outperform the counterpart with the fixed information rate in low and moderate SNR regions, suggesting that an adaptive transmission scheme should be employed to achieve a better throughput performance.

Abstract In this paper, large code division multiple access (CDMA) random access systems employing the decorrelator and minimum mean square error (MMSE) detectors are investigated over Rayleigh fading channels under the assumption that both the number of users and the spreading gain tend to infinity, but their ratio converges to a constant. The signal to interference ratio (SIR) is shown to converge almost surely to a constant and the bit‐error rate (BER) is expressed as a function of the traffic load, transmission probability, channel coefficient, and distribution of transmission power. Furthermore, the throughput, the spectrum efficiency, and the stability region are analyzed and simulated. For dominating systems, it is shown that the MMSE detector achieves much higher throughput and spectral efficiency than decorrelator detector. Besides, it is also disclosed that, when the signal to noise ratio (SNR) is larger than an optimum value, the spectrum efficiency increases as the ratio of bit energy to noise power spectrum density (Eb/N0) increases; however, when SNR is smaller than the optimum value, the spectrum efficiency decreases as Eb/N0 increases. For ordinary stable systems, it is demonstrated that their stability region gets narrower as the traffic load increases. Copyright © 2010 John Wiley & Sons, Ltd.

The performance of two-way amplify-and-forward (AF) relaying networks over independently but not necessarily identically distributed (i.n.i.d.) Nakagami-m fading channels, with integer and integer plus one-half values of fading parameter m, is studied. Closed-form expressions for the cumulative distribution function (CDF), probability density function (PDF), and moment generating function (MGF) of the end-to-end signal-to-noise ratio (SNR) are presented. Utilizing these results, we analyze the performance of two-way AF relaying system in terms of outage probability, average symbol error rate (SER), and average sum-rate. Simulations are performed to verify the correctness of our theoretical analysis.

The random access systems, exploiting retransmission diversity (RD), over large random spreading CDMA channel subject to fading is investigated, under the assumption of infinite number of users and infinite spreading gain but with their ratio converging to a constant. The SIR, throughput and spectrum efficiency are analyzed. Simulation and numerical results show that RD exploitation can significantly increase the throughput and spectrum efficiency in channels both with and without fading.

In this note, a closed-form expression is derived for the outage probability considered by Min et al. [1].