Integrating sensing capabilities into base stations (BSs) represents a crucial research direction for 6G wireless communications. Given limited transmit power and the complex perception environment, it is necessary to enhance the sensing performance of BSs in low signal-to-noise ratio (SNR) conditions. Aiming at this challenge, this paper proposes a track-before-detect (TBD) scheme based on the extended particle filter (EPF) and multi-BS collaboration. In this scheme, the likelihood ratio derived from the incoherent fusion of multi-BS signals is utilized in the EPF to jointly perform target number estimation and trajectory tracking. In the EPF, to improve tracking accuracy and accelerate the detection of newborn targets, sampling approaches that leverages the characteristics of OFDM signals are introduced to generate independent measurements. Then, an efficient importance density is designed for managing target birth, survival, and death respectively to enhance the effectiveness of particles. Finally, the particle parameter sharing (PPS) strategy is proposed to reduce computational complexity, addressing key challenges in PF algorithms. With the well-designed measurements and importance density, the proposed EPF-TBD exhibits excellent detection and tracking performance under low SNR conditions, outperforming existing DBT and TBD algorithms in simulations.

This paper investigates the passive multiple trajectories tracking with multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) signals transmitted by the base station (BS) in the rich scattering environment. In the proposed framework, multiple radar receivers track multiple trajectories cooperatively with the assistance of a fusion center. To cope with high dimensional and superimposed echoes received by each radar receiver, we propose a parameter estimation method based on Bayesian learning to obtain the coarse estimations of targets and clutter. Then, these estimations are utilized as inputs for the trajectory probability hypothesis density (TPHD) filter for trajectory tracking. Considering the potential for a target to generate multiple measurements in overlapping areas of sweeping beams and non-Poisson characteristics of the estimated clutter/false measurements, we derive the multi-detection TPHD (MD-TPHD) filter with non-Poisson clutter and its Gaussian mixture implementation, where the clutter is separated into a Poisson and a discrete distributed component. As the field of view (FoV) of each radar receiver is finite, we fuse the trajectories in a fusion center utilizing Arithmetic Average (AA) method. The fused TPHDs are then fed back to each radar receiver to assist more accurate tracking. Simulation results demonstrate the superiority of MD-TPHD filter assisted by trajectory fusion in the multi-detection and FoV limited scenarios.

This paper presents a passive multiple trajectories tracking system with multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) signals transmitted by the base station (BS). Firstly, we propose a Bayesian learning method to obtain the coarse estimations of targets and clutter, which are utilized for trajectories tracking. Considering the potential for a target to generate multiple measurements in overlapping areas of sweeping beams and non-Poisson clutter, we extend the trajectory probability hypothesis density (TPHD) filter to multi-detection TPHD (MD-TPHD) filter with non-Poisson clutter and provide its Gaussian mixture implementation. Simulation results show the performance of the algorithm.

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 studies the performance of multi-antenna proactive eavesdropping in 5G uplink systems with spatially correlated Rayleigh fadings, where the base station (BS) serves an illegal user and a multi-antenna legitimate monitor eavesdrops the suspicious link with the help of jamming attack. Based on a practical assumption of channel state information (CSI) in 5G uplink, i.e., imperfect instantaneous CSI of the suspicious link at the BS and that of eavesdropping link at the monitor, and only jamming channel statistics at the monitor, we first give a statistical jamming beamforming design. Then, semi-closed form expressions of eavesdropping non-outage probability and relative average eavesdropping rate are, respectively, derived for delay-sensitive and delay-tolerant scenarios. Via reasonable approximations and asymptotic analysis, we gain many insights on the effect of key system parameters, e.g., location-dependent channel path loss and angular spread, jamming energy and the number of antennas. Further, we provide the optimal energy allocation between pilot and data jamming and the optimal antenna allocation between jamming and eavesdropping under total energy constraint and total antenna number, respectively, which are both explicit functions of system parameters. Finally, simulation results validate our analytical results.

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.

Multiple-input multiple-output (MIMO) has been used in wireless communications to increase data rates via multiplexing or improve performance via diversity. Additionally, in radar systems, MIMO promises improved parameter identifiability and target resolution. Frequency diverse (FD)-MIMO radar can effectively distinguish targets that are closely spaced in the same angle cell by exploiting its range-angle-dependent transmit beamform. Therefore, in this paper, we first propose an integrated waveform design by embedding weighted, phase-modulated communication signals in the FD-MIMO radar waveform. Then, we derive the Cramer-Rao lower bound (CRLB) of the location estimation and analyze the communication performance achieved when the communication receiver extracts information from the integrated signals. Next, transmit beamforming is optimized to achieve the best tradeoff between the radar and communication performances of the system. Due to the nonconvexity of the optimization problem, we apply sequential parametric convex approximation (SPCA) and semidefinite relaxation (SDR) methods to solve the problem. The simulation results reveal the effects of some parameters, such as the frequency interval, symbol period, and communication symbol sequence, on radar and communication performance and demonstrate the tradeoff between radar and communication obtained by optimizing the transmit beamforming vector.

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.

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.

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.

Uplink–downlink duality has emerged as an attractive approach to optimise the downlink beamforming problem with fixed cooperation clusters where either all base stations serve all terminals or each base station serves only its own terminals. Although easily implementable for co-located base stations, the performance is still limited by out-of-cluster interference. To address these concerns, this study establishes an uplink–downlink duality for the multi-cell multi-user system with dynamic cooperation clusters where each base station has responsibility for the interference leaked to a set of terminals while only serving a subset of them with data. The multi-cell downlink problem of minimising the total transmit power subject to individual signal-to-interference-and-noise ratio requirements under per-base station power constraints is solved via a dual uplink problem. Conditions for beamforming optimality and the optimal downlink beamforming design are derived using Lagrange duality theory. The convergence behaviour of proposed algorithm is shown. The percentage of power saved by proposed algorithm is calculated subject to the user-specific SINR value achieved by zero-forcing (ZF), maximum-ratio transmit (MRT), virtual SINR (VSINR) and layered virtual SINR (LVSINR) under different cooperation scenarios and the sum rate performance is compared.

This paper investigates the issue of minimizing the detection time while guaranteeing the desired detection accuracy in cooperative primary user detection for cognitive radio. To achieve this goal, the sequential cooperative energy detection (SCED) scheme is firstly introduced. This scheme implements sequential probability ratio test to make final decisions and can guarantee the desired detection accuracy with less detection time compared with other schemes. Then, detection time in the SCED scheme is minimized by choosing an optimum sample number. Expressions for the optimum sample number and the minimum detection time in four typical scenarios are also derived. Simulation results are presented to illustrate the benefits of the SCED scheme and to examine the optimum sample number as well as the minimum detection time.

We study the symbol error rate (SER) of maximum ratio transmission, transmit antenna selection, and codebook-based beamforming in correlated Rayleigh fading channels. Assuming maximum ratio combining is performed at the receiver, we derive a universal upper bound on the average SER of the three schemes, and prove that the bound is asymptotically tight in high signal-to-noise ratio (SNR) regions. However, numerical results show that at medium SNR, the tightness of the bound depends on the condition number of the channel correlation matrix.

This paper addresses a per user unitary precoding and rate control (PU2RC) system with adaptive multi-level quadrature amplitude modulation (QAM) modulation, where both the PU2RC operation and adaptive modulation are based on finite rate feedback. The system supports two transmission modes (single user transmit beamforming and multiuser transmission with orthogonal beamforming), and switches between the two modes to maximize the average throughput. We first study single user transmit beamforming, and derive closed-form expressions for the average throughput and bit-error rate (BER) with outdated channel information. Then, multiuser transmission with orthogonal beamforming is investigated. We analyze the throughput and BER performance without feedback delay in the normal signal noise ratio (SNR) regime, and the performance with feedback delay in the interference-limited regime. Simulation results demonstrate the effect of time delay, and the preferred regions of the single user transmission mode and multiuser transmission mode.

This paper investigates a quantized beamforming system with feedback delay. A linear channel predictor is used to cope with the feedback delay. We derive an upper bound on the symbol error rate (SER) of phase shift keying (PSK) signal. Based on the bound, we design a predictor that provides good error performance. We also demonstrate that the beamformer design methods developed in a delay-free scenario are applicable to the system with feedback delay.

Codebook-based transmit beamforming with receive combining is a simple and efficient strategy for multiple- input multiple-output (MIMO) wireless systems. However, as a closed-loop technique, codebook-based transmit beamforming suffers from the performance degradation due to feedback delay. In this paper, we design quantizers robust to the feedback delay for codebook-based beamforming systems. Aiming at maximizing the average receive signal-to-noise ratio (SNR) or minimizing the average bit error rate (BER), optimal codeword selection rules are derived for the quantizer. Numerical results reveal that the proposed quantizers well compensate the performance loss due to feedback delay.

In a multiple-input multiple-output (MIMO) beam- forming system with finite rate feedback, the receiver sends back quantized channel information to the transmitter via a feedback channel. The overall system performance is degraded due to feedback errors. In this paper, we treat the feedback of the beamforming vector as a generalized vector quantizer (VQ), and adopt index assignment (IA) technique to cope with feedback errors. A lower bound to the average symbol error rate (SER) is derived, and an IA design criterion is proposed to minimize this bound, which is in accord with the pseudo-Gray coding principle in conventional IA design literature. Numerical results show that well-designed IA schemes improve the SER considerably.

In a single-input single-output (SISO) downlink with multiple users, to obtain the multiuser diversity gain, the full channel state information (CSI) for all users is required for selecting the 'best' user and transmission mode. However, feedback channels are often capable of carrying only a limited number of bits. With such rate-limited feedback links, we intend to maximize the sum-rate of systems with average power and bit-error rate (BER) constraints by varying the transmission mode according to feedback information. Althrough in multiuser systems with finite-rate feedback, the scheduled user is not necessarily the 'best' user, this work turns out to be a design of transmission modes based on CSI of the 'best' user. A nested iterative algorithm is proposed to obtain optimal thresholds and discrete transmission modes. Simulation results demonstrate the throughput with finite-rate feedback approaches that with perfect CSI when the feedback rate increases. As the number of users increases, the multiuser diversity gain is achieved and the average feedback load is reduced without the decrease of throughput.

In a single-input single-output (SISO) downlink with multiple users, to obtain the multiuser diversity gain, the full channel state information (CSI) of all users is required for selecting the desired user and transmission mode. However, feedback channels are often capable of carrying only a limited number of bits. With such rate limited feedback links, we intend to maximize the average throughput of each user by designing transmit power levels, signal constellations and feedback thresholds jointly. Both un-coded adaptive M-ary quadrature amplitude modulation (MQAM) and adaptive trellis-coded modulation (TCM) are investigated. A nested iterative algorithm is proposed to obtain a finite number of transmission modes and thresholds. Optimization in the heterogeneous network is also considered. Simulation results demonstrate a small number of feedback bits make the throughput approach to that with perfect CSI. As the number of users increases, the multiuser diversity gain is achieved and the average feedback load of each user is reduced.

Index assignment (IA) technique is introduced to quantized beamforming systems. The feedback channel in these systems is modeled as a discrete memoryless channel (DMC), and the diversity and array gains with feedback errors are derived. Based on the analytical results, IA scheme is designed to provide a redundancy-free protection against feedback errors. Simulation results show that good IA scheme improves the array gain and symbol error rate (SER).

In this paper, we consider transmit beamforming systems with finite rate feedback and feedback errors. We model the feedback channel as a uniform symmetric channel and analyze the effects of feedback errors on outage probability, bit error rate (BER), diversity gain, and array gain. Both analytical and simulation results show that feedback error with small probability will make the system behave badly at high signal-to-noise ratio (SNR).

Since the accuracy of channel prediction influences the performance of adaptive multiple-input-multiple-output (MIMO) systems in Rayleigh fading channels, we analyze the performance of adaptive V-BLAST systems with minimum-mean-square-error (MMSE) channel predictor in this paper. Considering the channel prediction error, we obtain the closed- form expressions for bit-error-rate (BER) and throughput of adaptive V-BLAST systems, and maximize the system transmission rate by choosing optimum block length under the BER constraint. The numerical results reveal the critical value of channel prediction error below which the systems can achieve the similar transmission rate to that of the systems with perfect channel prediction.

An adaptive modulation technique for multiuser MIMO systems with multiuser diversity is proposed. This technique maximizes the sum of the instantaneous bit rate under a target BER constraint by feeding back sub-channel SNRs only. By generating a random transmit beamforming matrix with a water-filling configuration, each user estimates and feeds back the effective SNRs for its sub-channels. With each user's sub-channel SNRs, the transmitter then schedules the transmission to a particular one selected by a proportional fair scheduling algorithm. Finally, adaptive modulation is applied to each sub-channel of the selected user based on the BER constraint. Simulation results show that the throughput of the proposed method converges to that of eigen-beamforming when the number of users in a cell is large.