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.

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.

Distributed computing has been widely applied in distributed edge networks for reducing the processing burden of high-dimensional data centralization, where a high-dimensional computational task is decomposed into multiple low-dimensional collaborative processing tasks or multiple edge nodes use distributed data to train a global model. However, the computing power of a single-edge node is limited, and collaborative computing will cause information leakage and excessive communication overhead. In this paper, we design a parallel collaborative distributed alternating direction method of multipliers (ADMM) and propose a three-phase parallel collaborative ADMM privacy computing (3P-ADMM-PC2) algorithm for distributed computing in edge networks, where the Paillier homomorphic encryption is utilized to protect data privacy during interactions. Especially, a quantization method is introduced, which maps the real numbers to a positive integer interval without affecting the homomorphic operations. To address the architectural mismatch between large- integer and Graphics Processing Unit (GPU) computing, we transform high-bitwidth computations into low-bitwidth matrix and vector operations. Thus the GPU can be utilized to implement parallel encryption and decryption computations with long keys. Finally, a GPU-accelerated 3P-ADMM-PC2 is proposed to optimize the collaborative computing tasks. Meanwhile, large-scale computational tasks are conducted in network topologies with varying numbers of edge nodes. Experimental results demonstrate that the proposed 3P-ADMM-PC2 has excellent mean square error performance, which is close to that of distributed ADMM without privacy-preserving. Compared to centralized ADMM and distributed ADMM implemented with Central Processing Unit (CPU) computation, the proposed scheme demonstrates a significant speedup ratio.

In privacy-preserving mobile network transmission scenarios with heterogeneous client data, personalized federated learning methods that decouple feature extractors and classifiers have demonstrated notable advantages in enhancing learning capability. However, many existing approaches primarily focus on feature space consistency and classification personalization during local training, often neglecting the local adaptability of the extractor and the global generalization of the classifier. This oversight results in insufficient coordination and weak coupling between the components, ultimately degrading the overall model performance. To address this challenge, we propose FedeCouple, a federated learning method that balances global generalization and local adaptability at a fine-grained level. Our approach jointly learns global and local feature representations while employing dynamic knowledge distillation to enhance the generalization of personalized classifiers. We further introduce anchors to refine the feature space; their strict locality and non-transmission inherently preserve privacy and reduce communication overhead. Furthermore, we provide a theoretical analysis proving that FedeCouple converges for nonconvex objectives, with iterates approaching a stationary point as the number of communication rounds increases. Extensive experiments conducted on five image-classification datasets demonstrate that FedeCouple consistently outperforms nine baseline methods in effectiveness, stability, scalability, and security. Notably, in experiments evaluating effectiveness, FedeCouple surpasses the best baseline by a significant margin of 4.3%.

ABSTRACT Vehicle re‐identification (Re‐ID) facilitates the recognition and distinction of vehicles based on their visual characteristics in images or videos. However, accurately identifying a vehicle poses great challenges due to (i) the pronounced intra‐instance variations encountered under varying lighting conditions such as day and night and (ii) the subtle inter‐instance differences observed among similar vehicles. To address these challenges, the authors propose A ngle M etric learning for D iscriminative F eatures on vehicle Re‐ID (termed as AMDF), which aims to maximise the variance between visual features of different classes while minimising the variance within the same class. AMDF comprehensively measures the angle and distance discrepancies between features. First, to mitigate the impact of lighting conditions on intra‐class variation, the authors employ CycleGAN to generate images that simulate consistent lighting (either day or night), thereby standardising the conditions for distance measurement. Second, Swin Transformer was integrated to help generate more detailed features. At last, a novel angle metric loss based on cosine distance is proposed, which organically integrates angular metric and 2‐norm metric, effectively maximising the decision boundary in angular space. Extensive experimental evaluations on three public datasets including VERI‐776, VERI‐Wild, and VEHICLEID, indicate that the method achieves state‐of‐the‐art performance. The code of this project is released at https://github.com/ZnCu‐0906/AMDF .

Recently, optical privacy protection has emerged as a promising approach for safeguarding visual privacy at the physical acquisition stage. However, existing methods often face a trade‐off between privacy strength and human pose recognition accuracy, particularly in long‐range and multi‐scale scenarios. To address this challenge, we propose a novel adaptive optical privacy‐preserving framework that integrates a learnable optical modulation system with a human pose recognition network. The core of our method lies in a sparse‐weighted multi‐lens model, where a lightweight multilayer perceptron (MLP) predicts a sparse set of coefficients to linearly combine predefined lens phase profiles based on facial region geometry. This enables dynamic control over the point spread function (PSF), adapting the degree of image degradation to subject scale in real time. Additionally, we introduce a privacy‐aware loss function that selectively reduces facial localization accuracy while preserving body pose information. Extensive experiments on MSCOCO and FLIC datasets demonstrate that the proposed method achieves a favorable balance between privacy protection and pose estimation, outperforming previous optical‐ and software‐based baselines.

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.

Federated learning (FL) is an effective mobile edge computing framework that enables multiple participants to collaboratively train intelligent models, without requiring large amounts of data transmission while protecting privacy. However, FL encounters challenges due to non-independent and identically distributed (non-IID) data from different participants. The existing methods, whether focusing on local training or global aggregation, often suffer from insufficient unilateral optimization. Achieving effective local-global collaborative optimization, particularly in the absence of additional reference models or datasets, is both crucial and challenging. To address this, we propose a novel approach:Dual-AggregatedFederated learning based on a tripleSiamese network (FedSiam-DA). This method enhances the FL algorithm on both client and server sides. On the client side, we establish a triple Siamese network incorporating a stop-gradient scheme, which leverages a contrastive learning strategy to control the update directions of local models. On the server side, we introduce a dual aggregation mechanism with dynamic weights for local updates, improving the global model’s ability to assimilate personalized knowledge from local models. Extensive experiments on multiple benchmark datasets demonstrate that FedSiam-DA significantly improves model performance under non-IID data conditions compared to existing methods.

Neural network models have become integral to Internet of Things (IoT) systems, with applications spanning from industrial automation to critical infrastructure management. Despite their prevalence, the deployment of these models within IoT systems introduces distinctive security vulnerabilities. In particular, adversaries may execute model poisoning attacks, which aim to alter the decision-making processes of embedded models, leading to erroneous outcomes. Existing model poisoning attacks necessitate access to extensive auxiliary datasets, such as the training dataset itself or one with same distribution. These requirements often render such attacks impractical in IoT contexts, given the constrained storage and computational resources of IoT devices. This paper proposes the first model poisoning attack against interpreters without auxiliary datasets to manipulate the model’s behavior. We evaluate the attack on three real-world datasets, and results indicate that this attack can successfully coerce the targeted interpreters to produce outcomes aligned with an adversary’s intentions, while maintaining nearly indistinguishable performance from the original model, thereby ensuring its stealthiness. Furthermore, beyond directly affected interpreters, our experiments reveal that four additional interpreters coupled to the poisoned model are indirectly influenced, underscoring the attack’s transferability.

Internet of Things (IoT) devices are frequently deployed in highly dynamic environments and need to continuously learn new classes from data streams. Incremental Learning (IL) has gained popularity in IoT as it enables devices to learn new classes efficiently without retraining model entirely. IL involves fine-tuning the model using two sources of data: a small amount of representative samples from the original training dataset and samples from the new classes. However, both data sources are vulnerable to Membership Inference Attack (MIA). Fortunately, the existing MIAs result in poor performance against IL, because they ignore features such as the similarity between old and new models at the old classification layer. This paper presents the first MIA against IL, capable of determining not only whether a sample was used for training/fine-tuning but also distinguishing whether it belongs to the representative dataset or the new classes (unique in IL). Extensive experiments validate the effectiveness of our attack across four real-world datasets. Our attack achieves an average attack success rate of 74.03% in the white-box setting (model structure and parameters are known) and 70.08% in the black-box setting. Importantly, our attack is not sensitive to the IL hyper-parameters (e.g., distillation temperature), confirming its accurate, robust, and practical.

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.

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.

In this article, we propose a novel federated learning (FL) framework for wireless Internet of Medical Things (IoMT) based healthcare systems, where multiple mobile clients and one edge server (ES) collaboratively train a shared model on long-tail data through wireless channels. However, the presence of long-tailed data in this system may introduce a biased global model which fails to handle the tail classes. Additionally, the occurrence of severe fading in wireless channels may prevent mobile clients from successfully uploading local models to the ES, thereby excluding them from participating in the model aggregation. These situations adversely affect the performance of FL. To overcome these challenges, we propose a novel scoring aided FL framework that uses a scoring-based sampling strategy to select mobile clients with more tailed data and better transmission conditions to upload their local models. Specifically, we leverage the logits to explore the data distribution among local clients and propose a logits based scoring client selection method to alleviate the impact of long-tailed data. Moreover, we address the impact of severe fading by incorporating the channel state information (CSI) and data rate of clients into the logits based scoring and proposing a novel logits and model upload rate based client selection method. Experimental results demonstrate the effectiveness of our proposed framework. In particular, compared to the conventional FedAvg, the proposed framework can achieve accuracy gains ranging from 4.44% to 28.36% on the CIFAR-10-LT dataset with an imbalance factor (IF) of 50.

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.

Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential and fundamental operation in many applications from many domains such as multimedia database, information retrieval and computer vision. With the rapidly growing volume of data and the dramatically increasing demands of users, traditional heuristic-based ANNS solutions have been facing great challenges in terms of both efficiency and accuracy. Inspired by the recent successes of deep learning in many fields, substantial efforts have been devoted to applying deep learning techniques to ANNS for learning to index and learning to search, resulting in numerous algorithms that achieve state-of-the-art performance compared with conventional methods. In this survey paper, we comprehensively review the different types of deep learning-based ANNS methods according to two learning paradigms:learning to indexandlearning to search. We provide a comprehensive overview and analysis of these methods in a systematic manner. Based on the overview, we point out thatend-to-end learningwill be a new and promising research direction for deep learning-based ANNS, i.e., applying deep learning techniques to jointly learn the indexing and searching together, such that the underlying knowledge learned from data can directly contribute to the final searching performance. Finally, we conduct experiments and provide general performance analyses for the representative deep learning-based ANNS algorithms.

To overcome the challenge of limited bandwidth, client selection has been considered an effective method for optimizing Federated Learning (FL). However, since the volatility of the learning environment, the available clients exhibit some volatility over the training process in terms of client population, client data, training status, and transmitting status, which greatly increases the difficulty of client selection. To find a practical solution, we explore a client selection problem in volatile federated learning (Volatile FL). Specifically, we first derive the convergence analysis for non-convex and strongly convex cases to illustrate the main factors affecting the convergence speed. Then, we introduce the client utility to quantify the client’s contribution to model training and discuss the key problems of client selection in Volatile FL. For an efficient settlement, we propose CU-CS, a Combinatorial Multi-Arm Bandit (C2MAB) based decision scheme for the proposed selection problem. Theoretically, we prove that the regret of CU-CS is strictly bounded by a finite constant, justifying its theoretical feasibility. The experimental results demonstrate that our method significantly boosts FL by speeding up model convergence, promoting model accuracy, and reducing energy consumption.

Federated learning (FL) has shown great potential as a privacy-preserving solution to training a centralized model based on local data from available clients. However, we argue that, over the course of training, the available clients may exhibit some volatility in terms of the client population, client data, and training status. Considering these volatilities, we propose a new learning scenario termed volatile federated learning (volatile FL) featuring set volatility, statistical volatility, and training volatility. The volatile client set along with the dynamic of clients’ data and the unreliable nature of clients (e.g., unintentional shutdown and network instability) greatly increase the difficulty of client selection. In this article, we formulate and decompose the global problem into two subproblems based on alternating minimization. For an efficient settlement for the proposed selection problem, we quantify the impact of clients’ data and resource heterogeneity for volatile FL and introduce the cumulative effective participation data (CEPD) as an optimization objective. Based on this, we propose upper confidence bound-based greedy selection, dubbed UCB-GS, to address the client selection problem in volatile FL. Theoretically, we prove that the regret of UCB-GS is strictly bounded by a finite constant, justifying its theoretical feasibility. Furthermore, experimental results show that our method significantly reduces the number of training rounds (by up to 62%) while increasing the global model’s accuracy by 7.51%.

In theera of Internet of Things (IoT), sensor plays a more vital role and its quality has a great impact on final measurement. From the viewpoint of final measurement, there are two kinds of effects: bias-only measurement and error-growth measurement. The bias-only measurement can be directly read from a sensing device, and each measurement is only distorted by the inherited bias error of the sensor, e.g., measuring temperature by a thermometer sensor. On the other hand, the error-growth measurement cannot be read directly but be calculated by combining multiple former sampling data, therefore multiple bias errors are accumulated in the measurement. For instance, in inertial navigation, the raw data sampled from an inertial measurement unit (IMU) are converted into a trajectory by multiple integral operations, so the errors of new sampling data are continuously added into the trajectory, and the deviation of the traced trajectory will reach an unacceptable level over time. Clearly, a good IMU trajectory strategy is the one with a less error-accumulation effect. Unfortunately, the analysis of error-growth effect remains not well studied, which motivates this work. This letter first proposes a theoretical error-growth effect analysis framework. Next, we use it to analyze three typical inertial methods, namely single-IMU method, gyro-free-IMU (GF-IMU) method, and$\omega$-free accelerometer pair (OFAP) method. Finally, the theoretical derivations of the three inertial methods are proved by simulation results.

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.

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.

In most multi-label image recognition tasks, human visual perception keeps consistent for different spatial transforms of the same image. Existing approaches either learn the perceptual consistency with only image-level supervision or preserve the middle-level feature consistency of attention regions but neglect the (global) label dependencies between different objects over the dataset. To address this issue, we integrate graph convolution network (GCN) and propose G-CAM, which learns visual attention consistency via GCN based class attention mapping (CAM) for multi-label image recognition. G-CAM consists of an image feature extraction module to generate the feature maps of the original image and its transformed one and a GCN module to learn weighted classifiers that capture the label dependencies between different objects. Different from previous works which use fully-connected classification layer, G-CAM first fuses weighted classifiers with the feature vector to generate the predicted labels for each input image, then combines weighted classifiers with the feature maps to respectively obtain the transformed attention heatmaps of the original image and the attention heatmaps of its transformed one. We can compute the attention consistency loss according to the distance between these two attention heatmaps. Finally, this loss is combined with the multi-label classification loss to update the whole network in an end-to-end manner. We conduct extensive experiments on three multi-label image datasets including FLICKR25K, MS-COCO and NUS-WIDE. Experimental results demonstrate G-CAM can achieve better performance compared with the state-of-the-art multi-label image recognition methods.

One of the main challenges in wireless powered networks (WPNs) is the doubly near-far problem, i.e., the twofold degradation of the users' performance due to different path-loss values that affects both the energy harvesting and the information transmission efficiency. To this end, we propose and optimize the application of decentralized power transfer and radio access in WPNs, which is implemented by using multiple remote radio heads (RRHs) with the capability to both transfer energy and receive information. More specifically, the use of non-orthogonal multiple access (NOMA) and time division multiple access (TDMA) is investigated, while two novel schemes are proposed, hereinafter termed as partially and fully asynchronous transmission TDMA (AT-TDMA). According to the proposed schemes, the users harvest energy and transmit information to the RRHs in different portions of time. Furthermore, the sum and minimum throughput among users are jointly maximized by obtaining the Pareto optimal solutions for the scheduling of power transfer and information transmission. To evaluate the performance of the decentralized architecture compared to the centralized one, we solve the aforementioned optimization problem for both architectures, taking also into account the circuit power consumption. Simulations show that the use of multiple RRHs improves spectral efficiency compared to the centralized implementation, since it can tackle more efficiently the doubly near-far problem. In addition, the proposed AT-TDMA schemes increase the achievable data rate.

This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) systems. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics. To tackle this issue, we propose a novel ML detection framework to effectively recover the desired signal. Our framework is a fully probabilistic one that can efficiently approximate the unknown noise distribution through a normalizing flow. Importantly, this framework is driven by an unsupervised learning approach, where only the noise samples are required. To reduce the computational complexity, we further present a low-complexity version of the framework, by utilizing an initial estimation to reduce the search space. Simulation results show that our framework outperforms other existing algorithms in terms of bit error rate (BER) in non-analytical noise environments, while it can reach the ML performance bound in analytical noise environments.

This paper considered an energy-harvesting based secure two-way relay (EH-STWR) network, where two users exchanged information with the assistance of one buffer-aided relay that harvested energy from two users. To realize the confidential message exchange between two users in the presence of a potential eavesdropper, a secure bidirectional relaying scheme based on time division broadcast (TDBC) was proposed, where one user sent artificial noise to suppress the eavesdropper and another user transmitted data to the relay. A secure sum-rate maximization problem was formulated subject to average and peak transmit power constraints, data buffer and energy storage causality, and transmission mode constraints. By employing the Lyapunov optimization framework, a security-aware adaptive transmission scheme was proposed to jointly adapt transmission mode selection, power allocation, and security rate allocation according to channel/buffer/energy state information (CSI/BSI/ESI). Analysis results showed that the average achievable secrecy rate region can be significantly improved and there exists an inherent trade-off among transmission delay, requirement of transmit power consumption, and achievable secure sum-rate. Moreover, the channel condition between the energy-constrained relay and the potential eavesdropper is a critical factor on the achievable long-term average secrecy rate performance.

In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami-m distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami-m fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption.

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.

In a downlink non-orthogonal multiple access (NOMA) system, the reliable transmission of cell-edge users cannot be guaranteed due to severe channel fading. On the other hand, the presence of eavesdroppers can severely threaten the secure transmission due to the open nature of wireless channel. Thus, a two-user NOMA system assisted by a multi-antenna decode-and-forward relay is considered in this paper, and a two-stage jamming scheme, full-duplex-jamming (FDJam), is proposed to ensure the secure transmission of NOMA users. In the FDJam scheme, using full-duplex, the relay transmits the jamming signal to the eavesdropper while receiving confidential messages in the first stage, and the base station generates the jamming signal in the second stage. Furthermore, we eliminate the self-interference and the jamming signal at the relay and the legitimate node, respectively, through relay beamforming. To measure the secrecy performance, analytical expressions for secrecy outage probability (SOP) are derived for both the cell-center and cell-edge users, and the asymptotic SOP analysis at high transmit power is presented as well. Moreover, two benchmark schemes, half-duplex-jamming and full-duplex-no-jamming, are also considered. Simulation results are presented to show the accuracy of the analytical expressions and the effectiveness of the proposed 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.

Non-orthogonal multiple access (NOMA) has been proposed as a promising multiple access approach for 5G mobile systems because of its superior spectrum efficiency. However, the privacy between the NOMA users may be compromised due to the transmission of a superposition of all users' signals to successive interference cancellation (SIC) receivers. In this paper, we propose two schemes based on beamforming optimization for NOMA that can enhance the security of a specific private user while guaranteeing the other users' quality of service (QoS). Specifically, in the first scheme, when the transmit antennas are inadequate, we intend to maximize the secrecy rate of the private user, under the constraint that the other users' QoS is satisfied. In the second scheme, the private user's signal is zero-forced at the other users when redundant antennas are available. In this case, the transmission rate of the private user is also maximized while satisfying the QoS of the other users. Due to the non-convexity of optimization in these two schemes, we first convert them into convex forms, and then, an iterative algorithm based on the Concave-Convex Procedure is proposed to obtain their solutions. The extensive simulation results are presented to evaluate the effectiveness of the proposed schemes.

In this paper, the access point (AP) in a wireless network is assumed to provide energy supply via wireless energy transfer to multiple terminals in the downlink, and all the terminals use the harvested energy to transmit their collected data to the AP in the uplink in a time division multiple access (TDMA) manner. Each terminal is provisioned with a finite energy storage and a finite data buffer to store the harvested energy and to buffer the arrived data traffic, respectively. Due to the limited data buffer and energy storage size, there might be data loss due to either data buffer overflow or energy storage depletion. Firstly, we aim at maximizing the long-term weighted sum-rate through energy beamforming vector design, power allocations, rate control, time allocations, and transmission mode selection subject to average transmit power, peak transmit power, data loss ratio requirements, practical data buffer as well as energy storage constraints. Secondly, the weighted max-min scheduling scheme is proposed to guarantee the fair access requirement by multiple terminals. Numerical analyses are presented to show that, the proposed adaptive design can substantially improve the average achievable rate region, while the proposed weighted max-min fair scheduling can effectively ensure the fair access requirements.

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.

In this paper, a multi-tag selection combining (SC) scheme is proposed in a radio frequency identification (RFID) backscatter communication system which contains a reader and L tags. The proposed scheme could efficiently combat the double-fading channel in RFID system since the diversity of multiple tags is utilized. Different from the conventional one-way communication system, the forward link channel and backscatter link channel could be correlated in backscatter communication system. Hence, we investigate the system performance under fully correlated and partially correlated Nakagami-m fading channels. The closed-form analytical outage probabilities for the two correlation circumstances are derived. Furthermore, the asymptotic outage probability is derived in a high signal-to- noise ratio (SNR) range, from which the insight on how the channel and system parameters affect the outage performance is gained. Finally, the simulation results are presented to verify the theoretical analysis.

In order to reap the full scale of benefits of millimetre wave (mmWave) massive multiple‐input multiple‐output (MIMO) systems, the design of antenna arrays at the transmitter or receiver becomes more critical due to the propagation characteristic at mm‐frequencies. In this study, the authors investigate the performance of two types of antenna array, namely uniform rectangular planar array (URPA) and uniform cylindrical array (UCYA). The channel behaviour is presented in full‐dimensional mmWave propagation conditions by considering both the azimuth and elevation dimensions. The squared inner product and singular value spread are studied for URPA and UCYA configurations, these properties reveal the effective interference and channel's stability of antenna array. The authors also evaluate the achievable rate with the equal power allocation and water‐pouring power allocation schemes. Simulation results show that under the same system configurations, the performance that includes the radiation pattern, the channel eigenvalue distribution, the effective interference, and the achievable rate, of UCYA configuration always outperforms that of URPA configuration. Therefore, it can be concluded that in three‐dimensional propagation environments, the UCYA configuration is especially appealing for mmWave MIMO systems.

In recent years, high‐speed railways (HSRs) are being developed rapidly all over the world because of their convenience, safety, comfort, and other advantages. However, the reliability and security of HSR wireless communication systems have faced severe challenges, such as the Doppler effect, complicated wireless channel model, frequent handovers and so on. The International Union of Railways is pushing the evolution of the global system for mobile communications for railway (GSM‐R) internationally. In this study, a high‐speed based adaptive beamforming handover scheme is proposed to improve the handover performance for HSR wireless communication systems. When the high‐speed train enters the overlapping region, the serving evolved NodeB (eNodeB) and target eNodeB are using beamforming with different gain factors to improve the received signal quality. In addition, this scheme can dynamically adjust the handover hysteresis margins of the reference signal receiving power (RSRP) and the reference signal receiving quality (RSRQ) based on the speed and position. Simulation results have demonstrated that the proposed handover scheme can improve the handover performance by increasing the handover trigger probability and success probability effectively.

In this article, intra‐ and inter‐cell interference caused by the non‐ideal backhaul in coordinated multipoint (CoMP) are studied. A recursive estimation‐based zero‐forcing beamforming (RE‐ZFBF) scheme is proposed to mitigate the interference. Based on an initial value, the RE‐ZFBF scheme recursively approximates the beamformer variation between two consecutive time points by exploiting the temporal channel correlation. In contrast to a previous work, the authors convert the original non‐convex constraint quality‐of‐service problem to an unconstrained convex one, by optimising the estimation of the beamformer error, rather than the beamforming power. It is shown that the proposed scheme can mitigate at most half the intra‐ and inter‐cell interference, enhance the ZFBF performance, and outperform the existing works for CoMP with a non‐ideal backhaul in heterogeneous small‐cell networks.

This paper studies the impacts of antenna selection algorithms in decode‐and‐forward (DF) cooperative nonorthogonal multiple access (NOMA) networks, where the secure information from the relay can be overheard by an eavesdropper in the networks. In order to ensure the secure transmission, an optimal antenna selection algorithm is proposed to choose one best relay’s antenna to assist the secure transmission. We study the impact of antenna selection on the system secure communication through deriving the analytical expression of the secrecy outage probability along with the asymptotic expression in the high regime of signal‐to‐noise ratio (SNR) and main‐to‐eavesdropper ratio (MER). From the analytical and asymptotic expressions, we find that the system secure performance is highly dependent on the system parameters such as the number of antennas at the relay, SNR, and MER. In particular, the secrecy diversity order of the system is equal to the antenna number, when the interference from the second user is limited.

The wireless caching has attracted a lot of attention in recent years, since it can reduce the backhaul cost significantly and improve the user‐perceived experience. The existing works on the wireless caching and transmission mainly focus on the communication scenarios without eavesdroppers. When the eavesdroppers appear, it is of vital importance to investigate the physical‐layer security for the wireless caching aided networks. In this paper, a caching network is studied in the presence of multiple eavesdroppers, which can overhear the secure information transmission. We model the locations of eavesdroppers by a homogeneous Poisson Point Process (PPP), and the eavesdroppers jointly receive and decode contents through the maximum ratio combining (MRC) reception which yields the worst case of wiretap. Moreover, the main performance metric is measured by the average probability of successful transmission, which is the probability of finding and successfully transmitting all the requested files within a radius R. We study the system secure transmission performance by deriving a single integral result, which is significantly affected by the probability of caching each file. Therefore, we extend to build the optimization problem of the probability of caching each file, in order to optimize the system secure transmission performance. This optimization problem is nonconvex, and we turn to use the genetic algorithm (GA) to solve the problem. Finally, simulation and numerical results are provided to validate the proposed studies.

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.

nonPeerReviewed

This paper investigates the effect of untrusted relay on the secrecy outage probability of amplify- and-forward relay networks with the help of direct links and multiple users. We consider three selection criteria which are based on the direct and relaying link. The impacts of both the direct and relaying links on SOP performance is studied by deriving the closed-form analytical SOP as well as the asymptotic expressions. Simulation results are provided to confirm the theoretical analysis. It can be found that the diversity order of optimal selection criterion is equal to the number of users, while the diversity order of the sub-optimal criterion based on relaying links is equal to one.

This paper studies user selection for an untrusted decode-and-forward relay channel, in which one base station is selected for secure transmission to the destination node in the presence of direct links. To enhance the secrecy performance, three selection criteria are investigated based on the the direct and the relaying links. The close-form analytical secrecy outage probability (SOP) as well as the asymptotic expressions are derived to reveal the impact of the direct and the relaying link on the system secrecy performance. From the asymptotic expressions, it shows that the proposed sub-optimal criterion, which substantially reduces the system complexity, achieves the same diversity order with the optimal criterion.

In this study, the authors study an energy harvesting relaying system with multiuser, in which the relay node is capable of harvesting radio‐frequency energy and retransmitting data with harvested energy by employing the power splitting strategy. The joint impact of direct and relaying links is taken into account in the process of user selection, and furthermore exploited for data transmission by using the selection combing receiver. To evaluate the system performance, analytical outage probabilities regarding both amplify‐and‐forward (AF) and decode‐and‐forward (DF) relaying have been derived under Rayleigh fading channel. Furthermore, they calculate the asymptotic expressions of outage probability, and they confirm that AF relaying as well as DF relaying approaches the system diversity order of , where N denotes the user number. Numerical results are demonstrated to validate the proposed analysis as well.

This paper quantifies the impact of correlated fading on secure communication of multiple amplify-and-forward (AF) relaying networks. In such a network, the base station (BS) is equipped with multiple antennas and communicates with the destination through multiple AF relays, while the message from the relays can be overheard by an eavesdropper. We focus on the practical communication scenario, where the main and eavesdropper's channels are correlated. In order to enhance the transmission security, transmit antenna selection is performed at the BS, and the best relay is chosen according to the full- or partial-relay selection criterion, which relies on the dual-hop relay channels or the second-hop relay channels, respectively. For these criteria, we study the impact of correlated fading on the network secrecy performance, by deriving an analytical approximation for the secrecy outage probability and an asymptotic expression for the high main-to-eavesdropper ratio. From these results, it is concluded that the channel correlation is always beneficial to the secrecy performance of full relay selection. However, it deteriorates the secrecy performance if partial-relay selection is used, when the number of antennas at the BS is less than the number of relays.

This paper investigates the physical layer security problem of cognitive decode-and-forward relay networks over Nakagami-m fading channels. We consider the relaying communication between one secondary user (SU) source and one SU destination by using an opportunistic relay selection from multiple SU relays and sharing the licensed spectrum of multiple primary users (PUs) in the underlay network. While the transmission between the SUs imposes interference on each PU, the relayed transmission is intercepted by one SU eavesdropper. In the absence of the eavesdropper's channel state information, the relay selection is based on the largest channel gain of relay-to-destination link, which is assumed to be outdated due to feedback delay. We derive the exact probability of non-zero secrecy capacity and the exact secrecy outage probability (SOP) in the closed form. Furthermore, we derive the asymptotic SOP in two different cases, and explicitly show the effects of system parameters on the secrecy diversity order and the secrecy diversity gain, respectively. Both asymptotic analysis and simulation results show that the secrecy performance can be improved by increasing either the number of relays or the Nakagami parameter of the legitimate relay channels, whereas the secrecy diversity gain deteriorates as the number of the PUs increases.

This paper investigates the secure communications of multiuser untrusted amplify-and-forward relay networks in the presence of direct links. In the considered system, one user is selected among multiple ones for secure transmission to the destination node with the help of an untrusted relay node. To reduce the information leakage to the untrusted relay, the paper considers two selection criteria to select the user based on the direct and the relaying links, respectively. The impact of direct and the relaying link on the system secrecy performance is studied by deriving the close-form ergodic secrecy rate (ESR) as well as the asymptotic expression. From the asymptotic expression, it can be found that the asymptotic ESR increases linearly with the logarithm of the average channel gain ratio of the direct link to the relaying link.

In this paper, we investigate the secure communication of cognitive full-duplex relay networks in the presence of multiple eavesdroppers and multiple primary receivers. In the considered network, multiple full-duplex relays are deployed to transfer information in the secondary network, under the malicious attempts of non- colluding/colluding eavesdroppers. Meanwhile, the transmit powers of secondary transmitters are constrained by the quality-of-service of the primary network. The optimal relay selection scheme is proposed to enhance the secrecy performance of the considered system. We study the secrecy performance by providing the exact closed- form and asymptotic expressions of the proposed system secrecy outage probability. We have demonstrated that increasing the number of full- duplex relays can improve the security performance. At the illegitimate side, using colluding eavesdroppers and increasing the number of eavesdroppers put information confidentiality at a greater risk. Besides, the transmit power and the desired outage probability of the primary network have great influences on the secrecy outage probability of the secondary network.

In this paper, we study a two-phase underlay cognitive relay network, where there exists an eavesdropper who can overhear the message. The secure data transmission from the secondary source to secondary destination is assisted by two decode-and-forward (DF) relays. Although the traditional opportunistic relaying technique can choose one relay to provide the best secure performance, it needs to continuously have the channel state information (CSI) of both relays, and may result in a high relay switching rate. To overcome these limitations, a secure switch-and-stay combining (SSSC) protocol is proposed where only one out of the two relays is activated to assist the secure data transmission, and the secure relay switching occurs when the relay cannot support the secure communication any longer. This security switching is assisted by either instantaneous or statistical eavesdropping CSI. For these two cases, we study the system secure performance of SSSC protocol, by deriving the analytical secrecy outage probability as well as an asymptotic expression for the high main-to-eavesdropper ratio (MER) region. We show that SSSC can substantially reduce the system complexity while achieving or approaching the full diversity order of opportunistic relaying in the presence of the instantaneous or statistical eavesdropping CSI.

In this paper, a multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system, including one base station (BS) equipped with multiple antennas, one desired single-antenna information receiver (IR), and N (N > 1) single-antenna energy-harvesting receivers (ERs) is considered. Assuming that the information signal to the desired IR may be eavesdropped by ERs if ERs are malicious, we investigate the secrecy performance of the target MISO SWIPT system when imperfect channel state information (CSI) is available and adopted for transmit antenna selection at the BS. Considering that each eavesdropping link experiences independent but not necessarily identically distributed Rayleigh fading, the closed-form expressions for the exact and the asymptotic secrecy outage probability, and the average secrecy capacity are derived and verified by simulations. Furthermore, the optimal power splitting factor is derived for each ER to realize the tradeoff between the energy harvesting and the information eavesdropping. Our results reveal the impact of the imperfect CSI on the secrecy performance of MISO SWIPT systems in the presence of multiple wiretap channels.

This paper studies an untrusted relay channel, in which the destination sends artificial noise simultaneously with the source sending a message to the relay, in order to protect the source's confidential message. The traditional amplify-and-forward (AF) scheme shows poor performance in this situation because of the interference power dilemma. Providing better security by using stronger artificial noise will consume more power of the relay, impairing the confidential message's transmission. To solve this problem, this paper proposes a modulo-and-forward (MF) operation at the relay with nested lattice encoding at the source. For the proposed MF scheme with full channel state information at the transmitter (CSIT), theoretical analysis shows that the MF scheme approaches the secrecy capacity within 1/2 bit for all channel realizations, and, hence, achieves full generalized security degrees of freedom (G-SDoF). In contrast, the AF scheme can only achieve a small fraction of the G-SDoF. For the MF scheme without CSIT, the total outage event, defined as either connection outage or secrecy outage, is introduced. Based on this total outage definition, analysis shows that the proposed MF scheme achieves the full generalized secure diversity gain (G-SDG) of order one. On the other hand, the AF scheme can achieve a G-SDG of only 1/2 at most.

We present two physical layer secure transmission schemes for multiuser multirelay networks, where the communication from M users to the base station is assisted by direct links and by N decode-and-forward relays. In this network, we consider that a passive eavesdropper exists to overhear the transmitted information, which entails exploiting the advantages of both direct and relay links for physical layer security enhancement. To fulfill this requirement, we investigate two criteria for user and relay selection and examine the achievable secrecy performance. Criterion I performs a joint user and relay selection, while Criterion II performs separate user and relay selections, with a lower implementation complexity. We derive a tight lower bound on the secrecy outage probability for Criterion I and an accurate analytical expression for the secrecy outage probability for Criterion II. We further derive the asymptotic secrecy outage probabilities at high transmit signal-to-noise ratios and high main-to-eavesdropper ratios for both criteria. We demonstrate that the secrecy diversity order is min (MN, M + N) for Criterion I, and N for Criterion II. Finally, we present numerical and simulation results to validate the proposed analysis, and show the occurrence condition of the secrecy outage probability floor.

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.

Opportunistic relaying scheme (ORS), where the best relay is selected for dual-hop communication, has been widely considered as the global optimum relaying technique. However, due to the requirement of acquiring the full channel state information (CSI) of all links, ORS has increased the system's complexity and might be harmful to the network stability, especially for the large-scale networks. In this paper, we therefore proposed an alternative scheme, namely, secure switch-and-stay combining (SSSC) protocol for providing the best secure performance. In particular, a two-phase underlay cognitive relay network, where one out of two decode-and-forward (DF) is activated to assist the secure data transmission. The secure relay switching occurs when the relay cannot support the secure communication any longer. We study the system secure performance of SSSC protocol by deriving an analytical secrecy outage probability as well as an asymptotic expression in the high main-to-eavesdropper ratio (MER) region. It is shown that SSSC can substantially reduce the switching rate with lower channel estimation complexity, and approach the full diversity meanwhile.

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.

The performance of the vector orthogonal frequency division multiplexing (V‐OFDM) system over fast fading channels is investigated. The channel is time varying within one V‐OFDM data‐block period, which causes the inter‐carrier interference (ICI). With the help of an equivalent V‐OFDM system model and an auxiliary transform matrix, a novel mathematical expression for the received signal with the ICI signal is derived, which clearly expresses how the signal of a target vector block (VB) is interfered by other VBs. The ICI signal is analysed and two theorems about its property are proposed. The first theorem presents the expression of the power of the ICI signal, showing that the ICI signal power increases with the VB number. The other one indicates that ICI signals at different subcarriers are not correlated. With the two theorems, a novel detection method utilising the correlation of the ICI signal is therefore proposed. Numerical results verify the validity of the derived theorems and simulation results demonstrate that the proposed minimum mean square error (MMSE) detection can suppress the ICI effect and it is superior to the conventional MMSE detection in terms of both detection mean square error and bit error rate performance.

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.

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.

As an important class of non‐Gaussian statistic model, α ‐stable distribution has received much attention because of its generality to represent impulsive interference. Unfortunately, it does not have a closed‐form probability density function (PDF) except for a few cases. For this reason, suboptimal zero‐memory non‐linearity (ZMNL) function has to be used as an approximation in designing locally optimal detector, such as classical Cauchy and Gaussian‐tailed ZMNL (GZMNL). To enhance the performance of detectors, the authors first investigate the approximate PDFs for the symmetric α ‐stable. In particular, a simplified version of Cauchy–Gaussian mixture (CGM) model, called bi‐parameter CGM (BCGM) model is detailed. This BCGM model has a concise closed‐form, and hence is more tractable than the classical Gaussian mixture model and CGM model. Then based on the preset false alarm ratio (FAR), the test threshold is adaptively evaluated by using BCGM to maintain a constant FAR. The authors further devise an algebraic‐tailed ZMNL (AZMNL) with a simplified form. Simulation results show that the detector with AZMNL outperforms the ones with classical Cauchy and GZMNL, and achieves near‐optimal performance in varying impulsive interference.

ABSTRACT We develop an efficient hard detector for multiple‐input multiple‐output (MIMO) channels, which adaptively combines maximum‐likelihood detection (MLD) and minimum‐mean‐square error with a successive interference canceler together. Unlike the conventional joint combination scheme, which may suffer from considerable degradation in bit‐error‐rate (BER) performance over correlated channels and where only one data stream is detected by MLD, our proposed scheme adaptively controls the number of data streams to be detected by MLD based on an analytical characterization of reliability for the detection. Simulation results illustrate that near‐optimal BER performance can be obtained at much lower computational complexity by the proposed method as compared with existing techniques, regardless of the spatial correlation of the MIMO channels. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we study the problem of resource allocation in orthogonal frequency division multiple access (OFDMA) based multi-user dual-hop uplink transmission where a single relay station is deployed between the mobile users and the base station which operates under amplify-and-forward (AF) relaying mode. The optimization targets to maximize the overall system throughput through joint optimization of sub-carrier allocation, sub-carrier pairing, and the power allocation, subject to individual power constraint at each node. The optimization results in mixed integer programming problem and a near optimal solution is obtained through dual decomposition approach. Further, we develop a suboptimal scheme to trade the performance for lower complexity. Finally, numerical examples are provided to demonstrate the performance gain of the proposed schemes.

In this paper, we consider the problem of precoding design for amplify-and-forward (AF) relay network with imperfect channel state information (CSI). We find a general rank precoding matrix at the relay such that the relay transmit power is minimized subject to quality of service (QoS) constraint as the worst case signal-to-noise ratio (SNR) at the destination. Since the direct optimization is nonconvex, we apply conservative methods to reformulate it as a semi-definite programming (SDP) problem which provides the upperbound of the original objective. Specifically, we suggest two SDP formulations that can be solved efficiently via convex optimization tools. We numerically compare the proposed suboptimal methods with the existing method, i.e., collaborative robust relay beamforming (CRBF), and show that the proposed schemes achieve a significant performance gain for a majority of feasible uncertainty sizes.

In this paper, we develop a simple blind channel estimation algorithm for two-way relay network (TWRN) that consists of two terminal nodes and one relay node. We consider the frequency selective channels and adopt the orthogonal frequency-division multiplexing (OFDM) modulation to compensate the inter-symbol interference (ISI). By applying a non-redundant linear precoding at both terminals, we propose an algorithm that is effective of estimating two cascaded channels and is consistent with the two-phase two-way transmission protocols. The method to remove the inherent ambiguity of the blind channel estimation is discussed. Finally, the numerical results are provided to corroborate the proposed studies.

We develop an efficient hard-detector for multiple-input multiple-output (MIMO) channels, which adaptively combines maximum-likelihood detection (MLD) and minimum-mean-square-error (MMSE) with a successive interference canceler (SIC) together. Unlike the conventional joint combination scheme which may suffer from considerable degradation in bit-error-rate (BER) performance over correlated channels and where only one data stream is detected by MLD, our proposed scheme adaptively controls the number of data streams to be detected by MLD based on the analytical characterization of reliability for the detection. Simulation results illustrate that near-optimal BER performance can be obtained at much lower computational complexity by the proposed method as compared to existing techniques, regardless of the spatial correlation of the MIMO channels.

This paper develops a joint maximum a posteriori (MAP) spatial filtering receiver by employing a joint parameter estimation approach for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) modulation systems, which can achieve promising bit-error-rate (BER) performance in the presence of co-channel interference (CCI). For a MAP receiver to suppress the CCI, conventionally, this can be done in two ways: 1) spatio-temporal filtering (STF) the received signals prior to the fast Fourier transform (FFT), which we refer to it as preFFT-STF; 2) spatial filtering (SF) the received signals posterior to FFT, which is referred to as postFFT-SF. Simulation results show that the proposed scheme outperforms significantly both the preFFT-STF and postFFT-SF systems.

This paper proposes joint maximum a posteriori (MAP) detection and spatial filtering for MIMO-OFDM mobile communications, which can maintain excellent receiver performance even over interference-limited channels. The proposed joint processor consists of a log likelihood generator and a MAP equalizer. The log likelihood generator suppresses cochannel interference by spatially filtering received signals and provides branch metrics of transmitted signal candidates. Using the branch metrics, the MAP equalizer generates log likelihood ratios of coded bits and performs channel decoding based on the MAP criterion. In the first step, the log likelihood generator performs spatio-temporal filtering (STF) of the received signals prior to the fast Fourier transform (FFT) and is referred to as preFFT-type STF. For further improvement, in iterative steps, the generator performs spatial filtering (SF) of the received signals posterior to FFT and is referred to as postFFT-type SF. Estimation of both tap coefficients of the spatial filters and channel impulse responses employs the recursive least squares (RLS) with smoothing. The reason for making a switch from preFFT-type STF into postFFT-type SF is that preFFT-type STF outperforms postFFT-type SF with a limited number of preamble symbols while postFFT-type SF outperforms preFFT- type STF when data symbols can be reliably detected and used for the parameter estimation. Computer simulations demonstrate that the proposed joint processing can achieve excellent BER performance under interference-limited channel conditions and that it can outperform the conventional joint processing of preFFT-type STF and the MAP equalizer.

This paper proposes a maximum likelihood (ML) detector with spatial-temporal filters prewhitening cochannel interferences for MIMO-OFDM mobile communications. The proposed detector consists of several branch metric generators and an ML detector using metrics which the generators produce. In the branch metric generator, the spatial-temporal filter for received signals suppresses interfering signal components while prewhitening them. Subtracting replica signals from the filter's outputs and squaring the result yield the metrics. Coefficients of the filters and channel impulse responses for the replicas are estimated by using the eigenvalue decomposition (EVD) of an autocorrelation matrix of both the received and transmitted signals. To select appropriate eigenvectors for the branch metric generators, the proposed detector adopts a channel capacity criterion, and avoids prohibitive complexity by using a fast search algorithm. Another problem is that it requires a large amount of computational complexity for channel tracking because it performs parameter estimation using the nonrecursive EVD. To solve such a problem, the recursive EVD is applied, which can track the time-varying fading channel while simultaneously reducing the complexity. Computer simulations demonstrate that the proposed scheme can maintain an excellent BER performance in correlated channels and track the fast fading channel with reduced complexity.