Relay Beamforming Research Articles

Joint Relay Beamforming and Waveform Design for Dual-Functional Radar and Relaying Networks

Joint Relay Beamforming and Waveform Design for Dual-Functional Radar and Relaying Networks

IRS-Aided Secure Communications Over an Untrusted AF Relay System

In this paper, an intelligent reflecting surface (IRS) assisted wireless secure communication system is studied. A multi-antenna access point (AP) intends to send confidential information to a single-antenna user in the presence of an amplify-and-forward (AF) untrusted relay, which may eavesdrop the message when helping relay the signal. It is assumed that the direct link between the AP and the user is blocked by the obstacles. The achievable secrecy rate maximization problem is then formulated. To overcome the non-convexity of the formulated problem, an alternating iteration algorithm is proposed to jointly optimize the active and passive beamforming. Specifically, the transmit beamforming vector at the AP, the phase shift matrix at the IRS, and the relay beamforming matrix are jointly optimized to maximize the secrecy rate. Moreover, the asymptotic expressions for the maximum secrecy rates of the proposed scheme in the high and low transmitted signal-to-noise ratio (SNR) regimes are derived as well. Finally, numerical evaluations demonstrate the superiority of the proposed scheme compared with other benchmark schemes, highlight the importance of properly designed phase shifts at the IRS, and validate the theoretical analysis. It is also demonstrated that the number of antennas at the AP should be strictly larger than the number of antennas at the relay to harvest the benefits of increasing the transmit power at the AP in increasing the secrecy rate.

Security design for integrated communication and computation networks through cooperative cognitive radios

AbstractIn this letter, a novel integrated communication and computation framework is proposed through cooperative cognitive radios, which includes a communication primary network (PN) and a computing secondary network. Specifically, two primary communication users intend to exchange their confidential information with the relay assistance of cognitive access point (CAP), meanwhile, multiple sensors share the spectrum of PN to transmit their concurrent sensing data to CAP. The CAP is assumed to be a potential eavesdropper and to ensure the secure communication of primary users (PUs) and computational performance of the CAP, it is proposed to maximize the sum achievable secrecy rate of PUs by jointly designing the transmit precoding, relay beamforming matrix, transmit scaling factors and aggregation beamforming vector, while satisfying the constraint of the computational mean square error of the CAP. A weighted minimum mean square error (WMMSE)‐based block coordinate descent (BCD) algorithm is developed to solve the formulated problem, and simulation results are provided to validate the effectiveness of our proposed schemes.

Minimum secrecy rate maximization for multiple peer-to-peer communications in two-way relaying networks

We investigate physical layer security for multi-user peer-to-peer relay networks, where a secure user pair and other unclassified users exchange information only through a multi-antenna relay in the presence of a multi-antenna eavesdropper. Our aim is to obtain the optimal values for variables of user transmit power and the relay beamforming matrix by solving the minimum secrecy rate maximization problem under specific constraints. We assume scenarios in which the eavesdropper’s channel state information is and is not available. Mathematically, the optimization problem raised in the first scenario is nonlinear and non-convex. The relay adopts a null space beamforming technique to facilitate the problem and guarantee secrecy. Also, we decouple it into two subproblems and solve them using inequality semi-definite programming and nonlinear optimization techniques to find solutions for variables. Also, the complexity of the proposed algorithms is analyzed and its convergency is proved by simulation. In the second scenario, we utilize an artificial noise-aided secure relay beamforming scheme. The problem is equivalent to the problem of assigning maximum power to the artificial noise evaluated by the secrecy outage probability concept. Finally, simulations demonstrate the validity of the proposed methods.

Joint Channel Estimation and Robust Beamforming Design for AF Relaying Using IMM Kalman Filters

This paper addresses the joint problem of recursive channel estimation and robust beamformer design in peer-to-peer communication through a network of relays over time-varying radio channels. Using observed signal samples at the relay and receiver nodes, the Channel State Information (CSI) is estimated centrally by taking advantage of a Markov model for the transmitter-relay and relay-receiver channels, and employing either the Extended Kalman Filter (EKF) or the Cubature Kalman Filter (CKF). Based on the estimated CSI, two robust approaches are conceived for designing the relay beamforming where the aim is to minimize the total transmission power of the relays subject to Signal-to-Interference plus Noise Ratio (SINR) constraints at each of the receiver nodes. Furthermore, the Interacting Multiple Model (IMM) approach for mixing non-stationary and stationary Markov models is employed to extend the time-varying robust beamforming design to non-stationary environments. Through numerical simulations, the recursive CSI estimation methods are shown to be efficient, i.e., unbiased and converging to the Cramer-Rao Lower Bound (CRLB). Furthermore, the results confirm the better performance of the proposed robust relay beamforming design algorithms compared to existing methods in terms of relevant transmission metrics, including relay power consumption and spectral efficiency.

User-Driven Relay Beamforming for mmWave Massive Analog-Relay MIMO

Sixth-generation mobile communication (6G) aims to further improve capacity and reliability by controlling the radio propagation environment. Millimeter wave (mmWave) high-frequency band communication offers large bandwidth at the cost of high attenuation, even for smaller distances. Due to this, fewer multiple input multiple outputs (MIMO) multiplexing is possible at the base station (BS). Distributed analog relay nodes with beamforming capability improve the received power and MIMO multiplexing of mmWave communication. Due to limited signal processing, the analog relay node cannot perform beam search and tracking using these mmWave reference signals. The beam search and tracking are possible at BS or user equipment at the cost of increased control overhead. To reduce this overhead and provide relay-based 6G communication, we propose user-driven relay beamforming methods which can obtain the benefits of a massive analog relay MIMO. Assuming vehicular-to-everything (V2X) as a 6G application, we considered a relay-beam control method that uses the user information (location, velocity, acceleration, and direction of the terminal) contained in intelligent transport systems (ITS) messages called Cooperative Awareness Message (CAM). Simulation results show that the proposed method significantly reduces the overhead and the obtains benefits of the massive analog-relay MIMO. Furthermore, the accuracy of CAM's location information, the control period, and the effects of UE mobility are evaluated and presented. The results also show that the proposed method can work effectively in future V2X applications.

Joint Transceiver Design for Dual-Functional Full-Duplex Relay Aided Radar-Communication Systems

Driven by the demand for massive and accurate sensing data to achieve wireless network intelligence under a limited available spectrum, the coexistence between radar and communication systems has attracted public attention. In this paper, we investigate a novel dual-functional full-duplex relay aided radar-communication system where the phased-array radar is employed at the amplify-and-forward (AF) relay. A joint transceiver design is proposed to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all detection directions at the radar receiver under communication quality-of-service and total energy constraints. The formulated optimization problem is particularly challenging due to the highly nonconvex objective function and constraints. Based on the problem structure, we equivalently decompose it into the radar-energy and relay-energy minimization problems under SINR requirements. To solve the radar-energy minimization problem, we propose a low-complexity algorithm based on the alternating direction method of multipliers to optimize the radar transmit power and receiver. The relay-energy minimization problem can be simplified into an equivalent quadratic programming problem by introducing an insightful unitary matrix. Then, the closed-form expression for the AF relay beamforming matrix can be derived, which is jointly determined by the channel condition of relay communication and the detection direction of the radar. After that, we introduce the overall transceiver design algorithm to the original problem and discuss its optimality and computational complexity. Simulation results verify that the proposed algorithm significantly outperforms other benchmark algorithms.

On the secrecy rate balancing problem of two‐way multiple‐input multiple‐output relaying networks using rank‐1 beamforming strategy

AbstractThe physical layer security of a two‐way relay network (TWRN) in the presence of an eavesdropper is considered, where two multi‐antenna nodes try to exchange messages via a multi‐antenna two‐way relay node. It is assumed that the transmission occurs in two hops. Throughout the first hop, the messages are concurrently transmitted at the same time from two transmitting nodes to the relay, while an eavesdropper tries to overhear the signals. Throughout the second hop, to prevent eavesdropping, the relay broadcasts a combination of the received signals in the null‐space of the eavesdropper's channel gain vector. The objective is to explore the proper power allocation policy at transmitting ends as well as an effective beamforming strategy at the relay to address the secrecy rate balancing (SRB) problem. The radio frequency multiple‐input multiple‐output (RF‐MIMO) architecture design is proposed in which the relay can merely process one data stream; hence, the relay beamforming matrix is of rank‐one. The underlying problem is non‐convex. To tackle this issue, the original problem is divided into two sub‐problems, where using semi‐definite relaxation (SDR) and sequential parametric cone approximation (SPCA) techniques, are converted into convex forms, where suboptimal solutions are derived. Numerical results demonstrate effectiveness of the proposed method compared to the existing works addressed in the literature and also show that our proposed solution has better performance even for the secrecy energy efficiency (SEE) metric.

Optimal energy-harvesting design for AF and DF two-way relay beamforming in 6G

Energy consumption is an important point which is crucial for green communications in 6G era, especially for those networks with limited life span or affected by dangerous environments where batteries are inconvenient to be changed. Therefore, energy harvesting (EH) has become a very attractive research field in recent years. In this paper, a new type of two-way EH relay beamforming system with two transceivers and many single-antenna relays is designed. All the transmit power of relay nodes is not restricted by the total quota or the personal quota and can be obtained entirely by energy harvesting. We use power splitting (PS) method for EH. We first establish a two-way system model for the amplify-and-forward and decode-and-forward relaying and then analyze the sum rate, and the gradient descent algorithm is used to solve the nonlinear joint PS factor optimization problem. Finally, the results of our analysis are verified by simulation, and they show that our method can not only optimize the PS factor efficiently, but also the model can improve the performance of the system.

Distributed Learning for MIMO Relay Networks

This paper studies a multi-antenna multi-user and multi-relay network, where the radio frequency (RF) power amplifiers (PA) of the nodes are subject to instantaneous power constraints. To optimize the nonlinear transceivers of the distributed nodes, we introduce a novel perspective of relating a relay network to an artificial neural network (ANN). With this perspective, we propose a distributed learning-based relay beamforming (DLRB) scheme. Based on a set of pilot sequences, the DLRB scheme can optimize the transceivers to minimize the mean squared error (MSE) of the data stream in a distributed manner. It can effectively coordinate the distributed relay nodes to form a virtual array to suppress interferences, even assuming neither the channel state information (CSI) nor information exchange between the relay nodes or between the users. We also present a frame design to support the DRLB so that it can adapt well with time-varying channels. Extensive simulations verify the effectiveness of the proposed scheme.

User power allocation and relay beamforming design for secrecy sum rate maximization in two-way relay networks

In this paper, we investigate the physical-layer security (PLS) for multiple peer-to-peer communications in a two-way relaying networks involving 2 L single-antenna users, an N R -antenna relay, and an N E -antenna eavesdropper. Only a pair of peer-to-peer users demand security to exchange messages because the eavesdropper intends to wiretap them in both transmission phases. Our goal is to maximize the achievable secrecy sum rate (SSR) of specific peer-to-peer users, provided that restrictive constraints are satisfied. We endorse a null space beamforming technique at the relay to simplify the optimization problem and exclude private information leakage. The main problem is still very complicated due to coupled variables of the user transmit power with the relay beamforming matrix. Therefore, we convert the main problem into two subproblems, one in terms of the relay beamforming matrix and the other in terms of the user transmit power. To find a locally optimal solution for the relay beamforming, we turn the main problem into a difference of convex (DC) programming problem and for user power allocation, we drive the main problem into a constrained nonlinear optimization problem. Finally, simulations are performed to evaluate the performance benefits of the proposed method.

Reinforcement Learning for Motion Policies in Mobile Relaying Networks

We consider joint beamforming and relay motion control in mobile relay beamforming networks, operating in a spatio-temporally varying channel environment. A time slotted approach is adopted, where in each slot, the relays implement optimal beamforming and estimate their optimal positions for the next slot. We place the problem of relay motion control in a sequential decision-making framework. We employ Reinforcement Learning (RL) to guide the relay motion, with the goal of maximizing the cumulative Signal-to-Interference+Noise Ratio (SINR) at the destination. First, we present a model based RL approach, which predictively estimates the SINR and accordingly determines the relay motion, based on partial knowledge of the channel model along with channel measurements at the current relay positions. Second, we propose a model-free deep Q-learning approach, which does not rely on channel models. For the deep Q-learning approach, we propose two modified Multilayer Perceptron Neural Networks (MLPs) for approximating the value function Q. The first modification applies a Fourier feature mapping of the state before passing it through the MLP. The second modification constitutes a different neural network architecture that uses sinusoids as activations between layers. Both modifications enable the MLP to better learn the high frequency value function and have a profound effect on convergence speed and SINR performance. Finally, we conduct a comparative analysis of all the presented approaches and provide insights on advantages and drawbacks.

Security Optimization for an AF MIMO Two-Way Relay-Assisted Cognitive Radio Nonorthogonal Multiple Access Networks With SWIPT

This paper investigates the physical layer security issue in an amplify-and-forward (AF) multi-input multi-output (MIMO) two-way relay assisted cognitive radio (CR) nonorthogonal multiple access (NOMA) network, where the simultaneous wireless information and power transfer (SWIPT) technology is employed to improve network energy efficiency. We consider the scenario that a pair of primary users and two pairs of secondary users (SUs) exchange information via a MIMO two-way relay, where the edge SU of each SU pair is untrusted and tries to wiretap the central SU’s information. For ensuring security, we aim to maximize the sum achievable secrecy rate (SASR) by jointly optimizing the power allocation at all users, power splitting factor and relay beamforming subject to the quality of service (QoS), energy harvesting and transmit power constraints. The formulated optimization problem is highly nonconvex due to coupling variables, thus it is challenging to solve. An effective path-following (PF)-based algorithm is proposed, which is proven to converge to a stationary point. Theoretical and simulation results show that the proposed PF-based algorithm has lower complexity than the state-of-art algorithm. To further reduce complexity, we proposed a zero-forcing (ZF)-based scheme. Numerical simulations show that the proposed PF-based algorithm achieves the same SASR as the state-of-art algorithm with moderate complexity, while the proposed ZF-based scheme strikes a good balance between performance and complexity.

Achieving Covert Wireless Communication With a Multi-Antenna Relay

We investigate covert wireless communication in a multi-antenna relay network, where the relay transmits its own covert message to the destination when assisting the source’s information delivery, and the source acts as a warden to detect this covert transmission. Based on whether the channel state information of the relay-destination link is available at the source or not, we propose two relay beamforming schemes, namely random beamforming and maximum-ratio transmission (MRT) beamforming schemes, to guarantee the reception reliability at the destination while deliberately introducing uncertainty to the source to degrade its detection. Under the worst-case covert communication scenario where the source is capable of optimizing its detection threshold, analytical expressions for the minimum detection error probability achieved by each of the proposed schemes are derived to evaluate the detection limits of the source. By utilizing the above analytical results as the covertness constraint, an optimization problem of transmit power allocation for each scheme is formulated and solved to maximize the covert rate. The impact of imperfect channel state information on the covert communication performance is also examined. Simulation results are performed to confirm the accuracy of the derived analytical results and quantify the communication covertness enhancement of the proposed schemes. Our results also show that the MRT beamforming scheme offers a higher covert rate than that of the random beamforming scheme, especially when the covertness constraint becomes loose and/or the number of antennas at the relay increases.

Relay beamforming design for physical layer secure communication via line search algorithm

This paper considers a physical layer security model in wireless communications. Two legitimate users communicate through several relays with the presence of an eavesdropper. We jointly design the relay beamforming weights and minimize the total relay transmit power, while ensuring users' Quality of Services and preventing the information being eavesdropped at the same time. The problem is a robust optimization problem, because of the imperfect channel state information from users and relays to the eavesdropper. First the original problem is simplified, where the high order robust terms are omitted. Then we design an iterative algorithm based on line search, by solving two Quadratically Constrained Quadratic Programming subproblems and a one-dimensional subproblem. Simulation results indicate that the proposed algorithm outperforms the state of the arts.

Secrecy Energy Efficiency in Full-Duplex AF Relay Systems With Untrusted Energy Harvesters

In this letter, we investigate an artificial noise (AN) aided beamforming scheme for full-duplex amplify-and-forward relay network in the presence of multiple untrusted energy harvesting receivers (EHRs). We establish an optimization problem to maximize the secrecy energy efficiency (SEE), while meeting the self-interference nulling and transmit power constraints at relay as well as maintaining the energy harvesting threshold at the EHRs by jointly designing the relay beamforming (BF) and AN covariance matrices. Due to the non-convex structure of the SEE maximization problem, we first design a null-space BF scheme at relay to eliminate the SI and simplify the joint optimization and then propose an iterative algorithm to achieve a local optimum based on the penalty function and the successive convex approximation methods. Numerical results are provided to validate the effectiveness of our proposed design.

Secrecy rate maximization for hardware impaired untrusted relaying network with deep learning

This paper investigates the physical layer security (PLS) design of an untrusted relaying network where the source node coexists with a multi-antenna eavesdropper (Eve). While the communication relies on untrustworthy relay nodes to increase reliability, we aim to protect the confidentiality of information against combined eavesdropping attacks performed by both untrusted relay nodes and Eve. Considering the hardware impairments (HIs), both total power budget constraint for the whole network and the individual power constraint at each node, this paper presents a novel approach to jointly optimize relay beamformer and transmit powers aiming at maximizing average secrecy rate (ASR). To safeguard the first cooperative phase, destination-aided cooperative jamming (DACJ) is employed, while for the second phase, the relay beamformer is adjusted. The resultant optimization problem is non-convex, and a suboptimal solution is obtained through the sequential parametric convex approximation (SPCA) method. In order to prevent any failure due to infeasibility, we propose an iterative initialization algorithm to find the feasible initial point of the original problem instead of an arbitrary point, as in the conventional SPCA. To satisfy low-latency as one of the main key performance indicators (KPI) required in beyond 5G (B5G) communications, a computationally efficient data-driven approach is developed exploiting a deep learning model to evaluate the proposed scheme while the computational burden is significantly reduced. Simulation results assess the effect of different system parameters on the ASR performance as well as the effectiveness of the proposed deep learning solution in large-scale cases.

Joint source and relay beamforming design and time allocation for wireless powered multi-relay multi-user network

This paper studies joint source and relay beamforming optimization and time allocation for a wireless powered multi-relay multi-user network, where a multi-antenna base station (BS) transmits information to users with the assistance of multiple single-antenna relays. Considering that the relays are energy-constrained, which are powered by the BS wirelessly, we aim to maximize the minimum rate among the users by jointly optimizing the information and energy beamforming at the BS, the distributed beamforming at the relays, and the time allocation under the power constraints and the energy causality constraints at the BS and relays, respectively. In order to tackle the formulated optimization problem, we propose an iterative algorithm by alternatively solving two subproblems. Numerical results show that the proposed joint optimization scheme obviously outperforms other schemes.

DC programming and DCA for enhancing physical layer security via relay beamforming strategies

Beside of cryptography-the primary traditional methods for ensuring information security and confidentiality, the appearance of the physical layer security approach plays an important role for not only enabling the data transmission confidentially without relying on higher-layer encryption, but also enhancing confidentiality of the secret key distribution in cryptography. Many techniques are employed in physical layers to improve secure transmission including cooperative relaying and beamforming technique. In this paper, we consider the secrecy rate maximization problems using two techniques mentioned above with two different relaying protocols: Amplify-and-Forward and Decode-and-Forward. The optimization problems with the aim of maximizing secrecy rate subject to total and individual relay power constraints are formulated as nonconvex problems, which can be reformulated as DC (difference of two convex functions) programs and thus can be solved by DC Algorithms (DCA). The special structure of feasible set is exploited which results to an efficient DC decomposition in the sense that it leads to convex subproblems that can be explicitly solved. The numerical results show that the proposed DCA schemes are better than the existing methods in terms of both runtime and secrecy rate.

Min-SINR-Maximizing Wireless-Powered AF Relay for Multisource and Multidestination Networks

This study considers a wireless-powered relay assisting multisource multidestination (MSMD) communication networks. Specifically, a beamforming and power-allocation scheme for the relay node is proposed, where a multiple-input multiple-output amplify-and-forward relay node that operates with harvested energy mediates the communication between MSMD pairs. The joint optimization of the relay beamforming matrix and of the power-splitting ratio design for the purpose of maximizing the minimum signal-to-interference-plus-noise ratios (SINRs) is challenging owing to its nonconvexity. We therefore propose an efficient algorithm for finding the optimal solution. In addition, to reduce computational complexity, a zero-forcing beamforming-based suboptimal relay design method is also presented. Simulation results show the effectiveness of the proposed schemes in terms of both SINR and sum-rate performances.