This paper studies a novel wireless information surveillance scenario, where the legitimate party aims to eavesdrop on multiple suspicious communication links with the help of multiple unmanned aerial vehicles (UAVs). Each suspicious link is comprised of a UAV (transmitter) and its fixed destination. To improve the eavesdropping ability, cooperative legitimate UAVs emit jamming signals to reduce the capacities of suspicious channels and plan the flight trajectory to enhance the capacity of the eavesdropping channels. Considering the system dynamics, it is natural to model this sequential decision-making problem as a Markov Decision Process (MDP), which might be solved by reinforcement learning (RL). However, it is difficult to design a policy in RL that determines jamming powers satisfying the considered eavesdropping constraints. Therefore, we decompose the optimization process into two phases, 1) obtaining the non-learning-based optimal solver for jamming power allocation under each state, and 2) optimizing the policy of moving action by RL. We will show this decoupled optimization process also holds the optimality. Considering the flying safety, we will determine the individual moving policy for each legitimate UAV rather than a centralized policy that controls all UAVs. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed solution.

In this article, we consider a cloud-radio access network (C-RAN) with multiple enhanced remote radio heads (RRHs), which support for wireless charging, computation offloading and information transmission. The employment of enhanced RRHs requires a redesign for efficient resource allocation. Our goal is to minimize the weighted sum of energy transfer time and computation time by jointly optimizing energy transmission time, energy beamforming vectors, offloading factors, information transmission time, and combining vectors, while satisfying the communication performance requirements of terminal nodes. The problem is formulated as a mixed-integer nonlinear programming, which can be solved by alternating optimization (AO) utilizing semidefinite relaxation (SDR) and nonlinear transformation. To reduce the online computation delay of AO algorithm, we propose an optimization method based on the deep learning framework, which can determine an optimal solution with a trained neural network in real-time application. To improve the performance of the learning algorithm, we use two methods to set labels for training deep neural network to obtain the optimal offloading decision: 1) preoptimization and 2) self-evolution. Simulation results show that with the trained neural network, the algorithms based on deep learning can achieve a similar performance to the AO algorithm with less computation time.

This paper considers an adversarial scenario between a legitimate eavesdropper and a suspicious communication pair. All three nodes are equipped with multiple antennas. The eavesdropper, which operates in a full-duplex model, aims to wiretap the dubious communication pair via proactive jamming. On the other hand, the suspicious transmitter, which can send artificial noise (AN) to disturb the wiretap channel, aims to guarantee secrecy. More specifically, the eavesdropper adjusts jamming power to enhance the wiretap rate, while the suspicious transmitter jointly adapts the transmit power and noise power against the eavesdropping. Considering the partial observation and complicated interactions between the eavesdropper and the suspicious pair in unknown system dynamics, we model the problem as an imperfect-information stochastic game. To approach the Nash equilibrium solution of the eavesdropping game, we develop a multi-agent reinforcement learning (MARL) algorithm, termed neural fictitious self-play with soft actor-critic (NFSP-SAC), by combining the fictitious self-play (FSP) with a deep reinforcement learning algorithm, SAC. The introduction of SAC enables FSP to handle the problems with continuous and high dimension observation and action space. The simulation results demonstrate that the power allocation policies learned by our method empirically converge to a Nash equilibrium, while the compared reinforcement learning algorithms suffer from severe fluctuations during the learning process.

The utilisation and transfer of renewable energy and grid energy in the downlink of multiuser communication systems is studied. In the considered multiuser system, the base station (BS) is powered by both harvested energy and grid. When the BS transmits data to one user terminal, other terminals can replenish energy opportunistically from received radio‐frequency signals, which is called simultaneous wireless information and power transfer (SWIPT). The objective is to maximise the average throughput by multiuser scheduling and energy allocation utilising imperfect causal channel state information while satisfying the requirement for harvested energy and the average power constraint of the grid. With channel dynamics and energy arrival modelled as Markov processes, the authors characterise the problem as a Markov decision process (MDP). The standard reinforcement learning framework is considered as an effective solution to MDP. If the transition probability of MDP is known, the policy iteration (PI) algorithm is used to solve the problem; otherwise, the R‐learning algorithm is adopted. Simulation results show that the proposed algorithm can improve the average throughput of the system and increase the energy harvested by idle user terminals compared with existing works. Also, R‐learning can achieve performance close to the PI algorithm under the condition that the channel transition probability is unknown.

The weighting subspace fitting (WSF) algorithm performs better than the multi-signal classification (MUSIC) algorithm in the case of low signal-to-noise ratio (SNR) and when signals are correlated. In this study, we use the random matrix theory (RMT) to improve WSF. RMT focuses on the asymptotic behavior of eigenvalues and eigenvectors of random matrices with dimensions of matrices increasing at the same rate. The approximative first-order perturbation is applied in WSF when calculating statistics of the eigenvectors of sample covariance. Using the asymptotic results of the norm of the projection from the sample covariance matrix signal subspace onto the real signal in the random matrix theory, the method of calculating WSF is obtained. Numerical results are shown to prove the superiority of RMT in scenarios with few snapshots and a low SNR.

Heterogeneous multi-core is an effective method to realize high performance computing. To integrate CPU and GPU via Network on Chip (NoC), taking advantages of concurrent communication and scalability, the expensive conventional off-chip data transmission can be avoided. The electric NoC (ENoC) has the advantage of low latency and low power consumption in the local short-distance communication, however, the hop to hop communication mechanism in traditional electric NoC causes higher power consumption and latency. Optical NoC (ONoC) provides an alternative to traditional interconnection scheme to reduce communication latency but the static power consumption of ONoC increases nonlinearly with the increase in number of interconnected nodes. So it is vital to design a hybrid electrical-optical NoC (HNoC) to reduce latency and power consumption taking advantages of both electrical and optical interconnections. We propose Poet, a HNoC architecture for heterogeneous system, which is used to alleviate the bandwidth bottleneck caused by data-intensive communication in intelligent applications and reduce static power consumption in order to adapt to the different communication demands. Poet uses modified Reservation-based Single Write Multiple Reader (R-SWMR) optical bus for long distance communication and low latency data communication. Use electric layer for short distance communication reduced number of microrings. Experimental results depict that power consumption has been reduced by 12.5% in comparison to heterogeneous architecture called Sharp and the latency has been reduced 76.61% compared to the E-Mesh under the same scale architecture of uniform random traffic.

The side-channel attack (SCA) poses a great threat to the information security of the Internet of things (IoT). However, the excessive resource cost caused by the corresponding countermeasure will hinder the extensive application of SCA-defendable technology in resource-limited devices. In this paper, a compact hardware structure of PRESENT cipher combining Threshold Implementation (TI) technique with multiple-bit serial architecture is presented. The 16-bit serial PRESENT is implemented mainly by reducing the width of datapath and building a direction-alterable register array. The implementation of TI-based substitution box is optimized by reusing common circuit, which makes slices utilization on Xilinx Kintex-7 FPGA reduced by 14.0% for 16-bit serial PRESENT with TI. Compared to the typical implementation of TI-based PRESENT, the proposed work saves resource consumption by 59.1%, which is available to resource-constrained scenarios.

Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by using a proper reference of the group-based sparse coding (GSC), which is desired to be as close to the truth as possible. Previous researches utilized the estimations from other algorithms (i.e., GMM or BM3D), which are either not accurate or too slow. In this paper, we propose to use the Non-Local Samples (NL-S) as reference in the GSR regime for image denoising, thus termed GSR-NLS. More specifically, we first obtain a good estimation of the group sparse coefficients by the image nonlocal self-similarity, and then solve the GSR model by an effective iterative shrinkage algorithm. Experimental results demonstrate that the proposed GSR-NLS not only outperforms many state-of-the-art methods, but also delivers the competitive advantage of speed.

In recent years, Industrial Internet of Things (IIoT) has attracted growing attention from both academia and industry. Meanwhile, when traditional wireless sensor networks are applied to complex industrial field with high requirements for real time and robustness, how to design an efficient and practical cross-layer transmission mechanism needs to be fully investigated. In this paper, we propose a Q-learning-based dynamic spectrum access method for IIoT by introducing cognitive self-learning technical solution to solve the difficulty of distributed and ordered self-accessing for unlicensed terminals. We first devise a simplified MAC access protocol for unlicensed users to use single available channel. Then, a Q-learning-based multi-channels access scheme is raised for the unlicensed users migrating to other lower cells. The channel with most Q value will be considered to be selected. Every mobile terminals store and update their own channel lists due to distributed network mode and non-perfect sensing ability. Numerical results are provided to evaluate the performances of our proposed method on dynamic spectrum access in IIoT. Our proposed method outperforms the traditional simplified accessing methods without self-learning capability on channel usage rate and conflict probability.

This letter addresses multisensor data fusion under the Gaussian noise. Under the Gauss-Markov model assumptions, data fusion based on maximum likelihood estimation (MLE) is the minimum variance unbiased estimator. Nonetheless, we propose a linear fusion algorithm based on the random matrix theory, which yields a biased estimator. The proposed estimator has a lower mean squared error (MSE) than the MLE fusion method when the dimensionality of signal is larger than the number of sensors, which is the typical use case in modern fusion systems. The fusion coefficients are directly solved in the proposed method without iteration, and this method can be considered as an approximate implementation of the linear minimum MSE (LMMSE) estimator. Numerical simulations demonstrate the performance gain of the proposed fusion method.

Currently, localization has been one of the research hot spots in Wireless Sensors Networks (WSNs). However, most localization methods focus on the device‐based localization, which locates targets with terminal devices. This is not suitable for the application scenarios like the elder monitoring, life detection, and so on. In this paper, we propose a device‐free wireless localization system using Artificial Neural Networks (ANNs). The system consists of two phases. In the off‐line training phase, Received Signal Strength (RSS) difference matrices between the RSS matrices collected when the monitoring area is vacant and with a professional in the area are calculated. Some RSS difference values in the RSS difference matrices are selected. The RSS difference values and corresponding matrix indices are taken as the inputs of an ANN model and the known location coordinates are its outputs. Then a nonlinear function between the inputs and outputs can be approximated through training the ANN model. In the on‐line localization phase, when a target is in the monitoring area, the RSS difference values and their matrix indices can be obtained and input into the trained ANN model, and then the localization coordinates can be computed. We verify the proposed device‐free localization system with a WSN platform. The experimental results show that our proposed device‐free wireless localization system is able to achieve a comparable localization performance without any terminal device.

Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated into one that reduces the group sparsity residual. To reduce the residual, we first obtain some good estimation of the group sparse coefficients of the original image by the first-pass estimation of noisy image, and then centralize the group sparse coefficients of noisy image to the estimation. Experimental results have demonstrated that the proposed method not only outperforms many state-of-the-art denoising methods such as BM3D and WNNM, but results in a faster speed.

Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, due to a majority of reconstruction methods only exploit the nonlocal self-similarity (NSS) prior of the degraded observation image, it is very challenging to reconstruct the latent clean image directly from the noisy observation. In this paper we propose a novel model for image denoising via group sparsity residual and external NSS prior. To boost the performance of image denoising, the concept of group sparsity residual is proposed, and thus the problem of image denoising is transformed into one that reduces the group sparsity residual. Due to the fact that the groups contain a large amount of NSS information of natural images, we obtain a good estimation of the group sparse coefficients of the original image by the external NSS prior based on Gaussian Mixture model (GMM) learning and the group sparse coefficients of noisy image are used to approximate the estimation. Experimental results demonstrate that the proposed approach not only outperforms many state-of-the-art methods, but also delivers the best qualitative denoising results with finer details and less ringing artifacts.

Sparse coding has achieved a great success in various image processing studies. However, there is not any benchmark to measure the sparsity of image patch/group because sparse discriminant conditions cannot keep unchanged. This paper analyzes the sparsity of group based on the strategy of the rank minimization. Firstly, an adaptive dictionary for each group is designed. Then, we prove that group-based sparse coding is equivalent to the rank minimization problem, and thus the sparse coefficients of each group are measured by estimating the singular values of each group. Based on that measurement, the weighted Schatten p-norm minimization (WSNM) has been found to be the closest solution to the real singular values of each group. Thus, WSNM can be equivalently transformed into a non-convex ℓp-norm minimization problem in group-based sparse coding. Experimental results on two applications: image in painting and image compressive sensing (CS) recovery show that the proposed scheme outperforms many state-of-the-art methods.

Nonlocal image representation or group sparsity has attracted considerable interest in various low-level vision tasks and has led to several state-of-the-art image denoising techniques, such as BM3D, learned simultaneous sparse coding. In the past, convex optimization with sparsity-promoting convex regularization was usually regarded as a standard scheme for estimating sparse signals in noise. However, using convex regularization cannot still obtain the correct sparsity solution under some practical problems including image inverse problems. In this letter, we propose a nonconvex weighted ℓpminimization based group sparse representation framework for image denoising. To make the proposed scheme tractable and robust, the generalized soft-thresholding algorithm is adopted to solve the nonconvex ℓpminimization problem. In addition, to improve the accuracy of the nonlocal similar patch selection, an adaptive patch search scheme is proposed. Experimental results demonstrate that the proposed approach not only outperforms many state-of-the-art denoising methods such as BM3D and weighted nuclear norm minimization, but also results in a competitive speed.

In this paper, we consider a three-point relay system, in which the relay has no fixed energy supplies and thus needs to replenish energy from RF signals transmitted by the source via wireless energy transfer (WET). We propose optimal selection relaying scheme when the source knows partial channel state information (CSI). To maximize the ergodic throughput under the peak/total power constraints of source and energy causality constraint of relay, the joint optimization of power control and information/energy transfer scheduling is formulated as a non-convex optimization of functional. By introducing new variables and constraints into the problem, the problem is solved by combining the fractional programming, convex optimization and linear search. Our results provide useful guidelines for the efficient design of relay systems with relay powered by WET.

Wireless energy transfer is a promising solution to provide convenient and steady energy supplies for low-power relays. This paper investigates the simultaneous information and energy transfer in fading relay channels, where the relay has no fixed energy supply and replenishes energy from radio frequency signals transmitted by the source. Assume that the relay can switch among energy harvesting, information decoding, and information retransmission in each channel fading state. Our objective is to maximize the ergodic throughput by optimizing the mode switching rule and transmit power jointly under the data and energy causality constraints. When the source knows channel state information (CSI) of all links, to make the problem tractable, for the relay, we neglect the causality constraints during the transmission, and only consider the total data and energy constraints. We thus obtain an upper bound on the ergodic throughput by solving a convex optimization problem. Numerical results show that the achievable rate is very close to the upper bound when we apply the optimized parameters to a practical system. When the source only knows CSI of partial links, the whole transmission process is divided into two phases: the source transmits in the first phase and the relay decodes and forwards received bits using the harvested energy in the second phase. The throughput maximization problem is solved by combing convex optimization, fractional programming, and linear search. We also consider the simplified network topology when a direct link between the source and destination is unavailable. In this network, we propose algorithms based on bisection method to obtain the optimal parameters in information/energy transfer scheduling and power control when the source knows full or partial CSI. The simulation results reveal that the throughput gain brought by wireless powered relaying in different system configurations when the source knows full or partial CSI. Moreover, the effect of the relay position is discussed.