Intelligent resource allocation for transmission security on IRS-assisted spectrum sharing systems with OFDM

In this paper, intelligent reflecting surface (IRS) technology is employed to enhance physical layer security (PLS) for spectrum sharing communication systems with orthogonal frequency division multiplexing (OFDM). Aiming to improve the secondary users’ secrecy rates, a design problem for jointly optimizing the transmission beamforming of secondary base station (SBS), the IRS’s reflecting coefficient and the channel allocation is formulated under the constraints of the requirements of minimum data rates of primary users and the interference between users. As the scenario is highly complex, it is quite challenging to address the non-convexity of the optimization problem. Thus, a deep reinforcement learning (DRL) based approach is taken into consideration. Specifically, we use dueling double deep Q networks (D3QN) and soft Actor–Critic (SAC) to solve the discrete and continuous action space optimization problems, respectively, taking full advantage of the maximum entropy RL algorithm to explore all possible optimal paths. Finally, simulation results show that our proposed approach has a great improvement in security transmission rate compared with the scheme without IRS and OFDM, and our proposed D3QN-SAC approach is more effective than other approaches in terms of maximum security transmission rate.     

Spectral efficiency maximization for IRS-assisted wireless communication in cognitive radio networks

In this paper, we consider the Intelligent reflecting surface (IRS)-assisted cognitive radio (CR) network, in which the IRS is applied to improve the spectral efficiency of secondary users. In specific, the advantages of applying IRS is double: it can not only improve the transmission rate of the secondary users, but also reduce the interference from the secondary users to the primary users, which allow the secondary users to increase the transmission power correspondingly. The original problem is formulated by simultaneously consider the maximum power limitation of the secondary users and the Quality of Service (QoS) requirement of the primary users, which expressed by the interference temperature (IT). The formulated problem is non-convex and difficult to solve directly. We apply the alternating optimization (AO) algorithm to split the original problem into two sub-problems. For the first sub-problem, we transform it into a convex problem and for the second sub-problem, we use the successive convex approximation method to obtain the corresponding results. The simulation experiments show that the performance of our proposed scheme is better than existing schemes.     

Intelligent radio resource management in reconfigurable IRS-enabled NOMA networks

Intelligent reflecting surfaces (IRSs) are anticipated to provide reconfigurable propagation environment for next generation communication systems. In this paper, we investigate a downlink IRS-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, where the IRS is deployed to especially assist the blocked users to establish communication with the base station (BS). To maximize the system sum rate under network quality-of-service (QoS), rate fairness and successive interference cancellation (SIC) constraints, we formulate a problem for joint optimization of IRS elements, sub-channel assignment and power allocation. The formulated problem is mixed non-convex. Therefore, a novel three stage algorithm is proposed for the optimization of IRS elements, sub-channel assignment and power allocation. First, the IRS elements are optimized using the bisection method based iterative algorithm. Then, the sub-channel assignment problem is solved using one-to-one stable matching algorithm. Finally, the power allocation problem is solved under the given sub-channel and optimal number of IRS elements using Lagrangian dual-decomposition method based on Lagrangian multipliers. Moreover, in an effort to demonstrate the low-complexity of the proposed resource allocation scheme, we provide the complexity analysis of the proposed algorithms. The simulated results illustrate the various factors that impact the optimal number of IRS elements and the superiority of the proposed resource allocation approach in terms of network sum rate and user fairness. Furthermore, we analyze the proposed approach against a new performance metric called computational efficiency (CE).     

Fairness optimization in IRS-assisted MEC computational offloading

Nowadays, more and more multimedia services are supported by Mobile Edge Computing (MEC). However, the instability of the wireless environment brings a lot of uncertainty to the computational offloading. Additionally, intelligent reflecting surface (IRS) is considered as a potential technology to enhance Quality of Service (QoS). Therefore, in this paper, we establish a framework for IRS-assisted MEC computational offloading to solve this problem and take fairness optimization as a key point involving communication and computing resources. Minimize user consumption by optimizing bandwidth allocation, task offloading ratio, edge computing resources, transmission power and IRS phase shifts. Firstly, we decompose the problem into three aspects, such as bandwidth allocation, computing resource allocation, transmission power and IRS phase shifts. Then, an alternative optimization algorithm is proposed to find the optimum solution and its convergence is proved. Secondly, since the optimization problem on transmission power and IRS phase shifts is non-convex, we propose Riemann gradient descent (R-SGD) algorithm to solve it. Finally, numerical results show that our proposed algorithm performs better than other algorithms and achieves a superiority in the framework.     

AN-aided beamforming for IRS-assisted SWIPT systems

This paper studies artificial noise (AN)-aided beamforming design in an intelligent reflecting surface (IRS)-assisted system empowered by simultaneous wireless information and power transfer (SWIPT) technique. Multiple power splitting (PS) single-antenna receivers simultaneously receive information and energy from a multi-antenna base station (BS). Although all users are legitimate, in each transmission interval only one receiver is authorized to receive information and the others are only allowed to harvest power which are considered as unauthorized receivers (URs). To prevent information decoding by URs, AN signal is transmitted from the BS. We adopt a non-linear model for energy harvesting. In the optimization problem, we minimize the total transmit power, and for this purpose, we utilize an alternating optimization (AO) algorithm. For the non-convex rank-one constraint for IRS phase shifts, we utilize a sequential rank-one constraint relaxation (SROCR) algorithm. In addition to single antenna URs scenario, we investigate multi-antenna URs scenario and evaluate their performance. Simulation results validate the effectiveness of using IRS.     

Computation rate maximization for IRS-enhanced dynamic spectrum access assisted MEC system

Intelligent reflecting surface (IRS)-enhanced dynamic spectrum access (DSA) is a promising technology to enhance the performance of the mobile edge computing (MEC) system. In this paper, we consider the integration of the IRS enhanced DSA technology to a MEC system, and study the pertinent joint optimization of the phase shift coefficients of the IRS, the transmission powers, the central processing unit (CPU) frequencies, as well as the task offloading time allocations of the secondary users (SUs) to maximize the average computation bits of the SUs. Due to the non-convexity, the formulated problem is difficult to solve. In order to tackle this difficulty, we decompose the optimization problem into tractable subproblems and propose an alternating optimization algorithm to optimize the optimization variables in an iterative fashion. Numerical results are provided to show the effectiveness and the correctness of the proposed algorithm.     

New generalized zero forcing beamforming for serving more users in energy-harvesting enabled networks

This paper considers a multi-user wireless communication network supported by a multiple-antenna base station (BS), where the users who are located sufficiently close to the BS employ wireless energy harvesting (EH) to replenish their energy needs. The objective of this work is to design an efficient beamforming to maximize the minimum throughput among all the information users (IUs), subject to EH constraints. In this regard, transmit time-switching approach is employed, where energy and information are transmitted over different fractions of a time-slot. To achieve efficient EH, a conjugate beamforming (matched filtering) is applied. To design efficient information beamforming for max–min throughput optimization, conventional zero-forcing (ZF) beamforming can be adopted, however, it will not suppress multi-user interference if the number of users is greater than the number of antennas at the BS. To this end, different from the existing works which employ regularized zero-forcing (RZF) beamforming, this work proposes a new generalized zero-forcing (GZF) beamforming, which promises better max–min throughput compared to that achieved by the RZF beamforming. A new path-following algorithm is developed to achieve max–min throughput optimization by GZF beamforming, which is based on a simple convex quadratic program over each iteration.     

Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications

Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links to enhance the intended signal power. We aim to maximize the sum-rate of all the terrestrial users by jointly optimizing the satellite’s precoding matrix and IRS’s phase shifts. However, it is difficult to directly acquire the instantaneous channel state information (CSI) and optimal phase shifts of IRS due to the high mobility of LEO and the passive nature of reflective elements. Moreover, most conventional solution algorithms suffer from high computational complexity and are not applicable to these dynamic scenarios. A robust beamforming design based on graph attention networks (RBF-GAT) is proposed to establish a direct mapping from the received pilots and dynamic network topology to the satellite and IRS’s beamforming, which is trained offline using the unsupervised learning approach. The simulation results corroborate that the proposed RBF-GAT approach can achieve more than 95% of the performance provided by the upper bound with low complexity.     

Throughput maximization of an IRS-assisted wireless powered network with interference: A deep unsupervised learning approach

We consider an intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) in which a multi antenna power beacon (PB) sends a dedicated energy signal to a wireless powered source. The source first harvests energy and then utilizing this harvested energy, it sends an information signal to destination where an external interference may also be present. For the considered system model, we formulated an analytical problem in which the objective is to maximize the throughput by jointly optimizing the energy harvesting (EH) time and IRS phase shift matrices. The optimization problem is high dimensional non-convex, thus a good quality solution can be obtained by invoking any state-of-the-art algorithm such as Genetic algorithm (GA). It is well-known that the performance of GA is generally remarkable, however it incurs a high computational complexity. To this end, we propose a deep unsupervised learning (DUL) based approach in which a neural network (NN) is trained very efficiently as time-consuming task of labeling a data set is not required. Numerical examples show that our proposed approach achieves a better performance–complexity trade-off as it is not only several times faster but also provides almost same or even higher throughput as compared to the GA.     

Energy-efficient beamforming optimization for MISO communication based on reconfigurable intelligent surface

Reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiency (EE) by reconfiguring the wireless propagation environment. In this paper, we propose a base station (BS) beamforming and RIS phase shift optimization technique that maximizes the EE of a RIS-aided multiple-input–single-output system. In particular, considering the system circuits’ energy consumption, an EE maximization problem is formulated by jointly optimizing the active beamforming at the BS and the passive beamforming at the RIS, under the constraints of each user’ rate requirement, the BS’s maximal transmit power budget and unit-modulus constraint of the RIS phase shifts. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. To overcome this obstacle, we divide the problem into active and passive beamforming optimization subproblems. For the first subproblem, the active beamforming is given by the maximum ratio transmission optimal strategy. For the second subproblem, the optimal phase shift matrix at the RIS is obtained by exploiting sine cosine algorithm (SCA). Moreover, for this case where each reflection element’s working state is controlled by a circuit switch, each reflection element’s switch value is optimized with the aid of particle swarm optimization algorithm. Finally, numerical results verify the effectiveness of our proposed algorithm compared to other algorithms.     

Robust secrecy rate maximization in IRS-assisted cognitive radio networks

In this paper, we focus on the secrecy rate maximization problem in intelligent reflecting surface (IRS)-assisted cognitive radio (CR) networks. In order to improve the security, there is a common scheme to add artificial noise (AN) to the transmitted signal, which is also applied in this paper. Further, in CR networks, the secondary users always cannot obtain accurate channel state information (CSI) about the primary user and eavesdropper. By taking jointly design for the IRS phase shift matrix, the transmitted beamforming of the secondary base station (BS), and the covariance matrix of AN, our objective is to maximize the minimal secrecy rate of all secondary users. Due to the serious coupling among the designed variables, it cannot be solved by conventional methods. We propose an alternating optimization (AO) algorithm. In simulation results, we apply primary users and secondary users randomly distributed in the communication area, which numerically demonstrate the superiority of our proposed scheme.     

无线携能通信系统中基于能量获取比例公平的波束成形设计

针对无线携能通信系统中存在能量获取不均衡的问题, 提出了一种基于能量获取比例公平的波束成形设计方案. 该方案在满足信息接收者的信干噪比以及发送端的最大发送功率等约束条件的基础上, 通过优化波束矢量实现能量获取的比例公平. 此设计在数学上是一个很难直接求解的非凸优化问题.为此, 本文首先利用半定松弛技术将其转换为半定规划问题, 然后结合二分法提出了可以获得最优波束矢量的迭代算法.此外, 在发送端仅知道部分信道状态信息且知道信道误差范围的情况下, 采用最差性能最优的方法对原优化问题进行了鲁棒波束成形设计, 并提出了相应的迭代算法. 仿真结果表明所提算法均可实现能量获取的比例公平且性能达到全局最优.     

System throughput maximization in IRS-assisted phase cooperative NOMA networks

This work investigates performance of system throughput in intelligent reflecting surfaces (IRSs)-enabled phase cooperative non-orthogonal multiple access (NOMA) framework. By exploiting heterogeneous cognitive radio networks concept the aim is to maximize the sum rate of secondary users in the proposed phase cooperative downlink network configuration via optimization solutions. However, the optimization problem comes out to be NP-hard and precludes direct solution. Hence, an alternating optimization is applied at the primary network to solve the maximization problem by exploiting the transmit beamforming (BF) at the power station (PS) and phase shift optimization at the IRS. Later, sum rate maximization for secondary network is performed by utilizing phase shifts of primary network via phase cooperation. In order to find global optimal solutions for active beamformers at both PSs, a branch-reduce-and-bound (BRnB) method is used whereas, passive phase shift optimization at the primary PS is performed via a simple iterative solution, i.e., the element-wise block coordinate descent method. For the proposed framework, Monte-Carlo simulations are performed where the optimality of the global solution is compared with heuristic BF methods including minimum-mean-square-error/regularized zero-forcing-beamforming (ZFBF) and ZFBF. The BRnB algorithm sets an upper performance bound by improving the sum rate of users in comparison with the conventional heuristic BF schemes. This work signifies the utilization of phase cooperation in IRS-assisted NOMA networks for a multi-user environment.     

Secure efficiency maximization for UAV-assisted mobile edge computing networks

This paper considers an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, in which the intelligent reflecting surface (IRS) is applied to enhance the performance of the wireless transmission. The role of the UAV is twofold: (1) It is equipped with a MEC server and receives computing tasks from ground users and IRS at the same time; (2) It sends interference signals to counter the potential eavesdropper. Here, the UAV is working as a full duplex equipment, i.e., sending and receiving meanwhile. We comprehensively considered the flight speed constraint of the UAV, the total mission data constraint and the minimum security rate constraint of multiple users on the ground. The phase matrix constraints of IRS are also considered. Our system is dedicated to maximizing the efficiency of secure computing. The formulated problem is highly non-convex, we consider to propose an alternative optimization algorithm. The simulation results show that the proposed scheme not only achieves higher safe computing efficiency, but also has better performance in terms of energy consumption and security rate.     

基于半定规划和秩-1分解的稳健波束形成

针对标准Capon波束形成器在存在导向矢量失配时性能急剧下降问题, 提出了一种基于半定规划和秩-1分解的稳健波束形成算法. 该方法通过对实际导向矢量的估计提高自适应波束形成算法稳健性. 首先分别从干扰抑制和噪声抑制两个方面推导了新导向矢量应满足的约束条件, 并证明了利用矩阵滤波器构造约束条件的合理性; 构造了估计最优导向矢量的优化问题并将其转化为易于求解的松弛半定规划问题, 同时引入秩-1分解理论用于优化问题的求解. 仿真分析表明, 与目前较为常见的算法相比, 本文算法只需利用期望信号可能入射区间这一先验信息, 能获得更高输出信干噪比和功率估计精度. 关键词： 稳健自适应波束形成 半定规划 秩-1分解 导向矢量估计     

Cooperative beamforming design for double-IRS-assisted MISO communication system

Intelligent reflecting surface (IRS) has arisen as a promising technology to reconfigure the wireless propagation environment cost-effectively. Most of the existing works on IRS focused on the passive beamforming (PB) optimization and performance enhancement without considering the multiple inter-IRS links cooperation that did not reveal the full preponderance of the multi-IRS-assisted reconfigurable communication system. In this work, we investigate a double-IRS-assisted multiple input single output (MISO) downlink communication system with the active beamforming (AB) and the cooperative PB design in the absence of direct link. Taking both the double-reflection links and the single-reflection links into account, the AB at the base station (BS) and the cooperative PB at two IRSs are jointly optimized to maximize the weight sum rate (WSR) under the constraint of the transmit power. To tackle the problem, we propose the double-IRS-assisted fractional programming block coordinate descent (D-FPBCD) method to find the sub-optimal solution with low complexity. We first reconstruct the original issue as a tractable one by the closed-form fractional programming (FP) approach, then, the prox-linear block coordinate descent (BCD) and successive convex approximation (SCA) techniques are used to find the sub-optimal solution. Finally, simulation results demonstrate the effectiveness of the proposed double-IRS-assisted wireless communication scheme.     

基于时频分析的多目标盲波束形成算法

针对现有盲波束形成算法适用范围较窄, 多目标信号分离级联模式结构复杂、并联模式稳定性较差等问题, 提出一种基于时频分析的多目标盲波束形成算法. 该算法首先利用时频分析技术给出信号导向矢量的不确定集, 然后优化求解导向矢量的最优估计, 最后利用Capon方法实现多目标信号的并行输出. 理论分析及仿真结果表明, 该算法对信号特性没有特殊要求, 适用性较广, 性能稳定, 且输出信干噪比高于其他盲波束形成算法, 接近于最优Capon波束形成器.     

Intelligent Reflecting Surface–Assisted Wireless Secret Key Generation against Multiple Eavesdroppers

In this paper, we propose an improved physical layer key generation scheme that can maximize the secret key capacity by deploying intelligent reflecting surface (IRS) near the legitimate user aiming at improving its signal-to-noise ratio (SNR). We consider the scenario of multiple input single output (MISO) against multiple relevant eavesdroppers. We elaborately design and optimize the reflection coefficient matrix of IRS elements that can improve the legitimate user’s SNR through IRS passive beamforming and deteriorate the channel quality of eavesdroppers at the same time. We first derive the lower bound expression of the achievable key capacity, then solve the optimization problem based on semi-definite relaxation (SDR) and the convex–concave procedure (CCP) to maximize the secret key capacity. Simulation results show that our proposed scheme can significantly improve the secret key capacity and reduce hardware costs compared with other benchmark schemes.     

Impact of direct links on Intelligent Reflect Surface-aided MEC networks

This paper studies an intelligent reflect surface (IRS) aided mobile edge computing (MEC) network, where the direct link exists in the network can assist the task transmission for computing with the help of multiple elements in the IRS. We perform the performance evaluation by instigating the impact of direct link on the outage probability. Specifically, Firstly, we analyze the system outage probability (SOP) with a different number of reflecting elements and energy consumption constraints. Moreover, we propose two selection methods for the case of multiple reflecting elements. In particular, Method I maximizes the first-hop reflecting channel while Method II maximizes the dual-hop product channel. In further, for the two different methods, we estimate the outage probability of the system by considering the reflecting channel information and providing the analytic expression of the outage probability, respectively. Finally, the numerical results verify the correctness of our results. The results show that increasing the number of reflecting elements can effectively reduce the SOP.     

A double constrained robust capon beamforming based imaging method for early breast cancer detection

Ultra-wideband （UWB） microwave images are proposed for detecting small malignant breast tumors based on the large contrast of electric parameters between a malignant tumor and normal breast tissue. In this study, an antenna array composed of 9 antennas is applied to the detection. The double constrained robust capon beamforming （DCRCB） algorithm is used for reconstructing the breast image due to its better stability and high signal-to-interference-plus-noise ratio （SINR）. The successful detection of a tumor of 2 mm in diameter shown in the reconstruction demonstrates the robustness of the DCRCB beamforming algorithm. This study verifies the feasibility of detecting small breast tumors by using the DCRCB imaging algorithm.