Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems

Intelligent reflecting surface (IRS) has recently been envisioned to offer unprecedented massive multiple-input multiple-output (MIMO)-like gains by deploying large-scale and low-cost passive reflection elements. By adjusting the reflection coefficients, the IRS can change the phase shifts on the impinging electromagnetic waves so that it can smartly reconfigure the signal propagation environment and enhance the power of the desired received signal or suppress the interference signal. In this paper, we consider downlink multigroup multicast communication systems assisted by an IRS. We aim for maximizing the sum rate of all the multicasting groups by the joint optimization of the precoding matrix at the base station (BS) and the reflection coefficients at the IRS under both the power and unit-modulus constraint. To tackle this non-convex problem, we propose two efficient algorithms. Specifically, a concave lower bound surrogate objective function has been derived firstly, based on which two sets of variables can be updated alternately by solving two corresponding second-order cone programming (SOCP) problems.Then, in order to reduce the computational complexity, we further adopt the majorization—minimization (MM) method for each set of variables at every iteration, and obtain the closed form solutions under loose surrogate objective functions. Finally, the simulation results demonstrate the benefits of the introduced IRS and the effectiveness of our proposed algorithms

Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks

In this paper, the problem of maximizing the sum of data rates of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 28.6% gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS

Performance Optimization for Intelligent Reflecting Surface Assisted Multicast MIMO Networks

In this paper, the problem of maximizing the sum rate of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 13.3 % gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS

Weighted Sum Secrecy Rate Maximization using Intelligent Reflecting Surface

This paper aims to investigate the benefit of using intelligent reflecting surface (IRS) in multi-user multiple-input single-output (MU-MISO) systems, in the presence of eavesdroppers. We maximize the weighted sum secrecy rate by jointly designing the secure beamforming (BF), the artificial noise (AN), as well as the phase shift of the IRS. An alternating optimization (AO) method is proposed to deal with the formulated non convex problem. In particular, the secure beamforming and AN jamming matrix are optimally designed via the successive convex approximation (SCA) approach for given phase shift, which can be derived by considering the alternating direction method of multiplier (ADMM) and element-wise block coordinate decent (EBCD) methods. Finally, simulation results are presented to show the benefit of the IRS in terms of improving the secrecy performance, when compared to other methods

Transmission Design for Reconfigurable Intelligent Surface-Aided Wireless Systems

The performance benefits promised by Reconfigurable Intelligent Surface (RIS) are strongly dependent on the availability of highly accurate and up-to-date Channel State Information (CSI), which, however, is challenging to obtain. This thesis proposes efficient transceiver designs for a variety of CSI challenges such as worst channel condition in multicast systems, channel uncertainties caused by the presence of random blockages in millimeter wave systems, by the channel estimation error in downlink systems and by the presence of eavesdropper in security systems. First, a low-complexity transceiver design scheme in the multicast systems is proposed. In order to ensure the quality of service of the user with the worst channel condition, this thesis deploys an RIS to enhance signal coverage, and proposes two novel and efficient algorithms to jointly design the Base Station (BS) and RIS beamformings. The low-complexity algorithm with closed-form solutions is proved to have the same performance as the general second-order cone programming based algorithm. Second, novel fairness-oriented robust transceiver design schemes are proposed in RIS-aided millimeter wave systems. The channel uncertainty caused by the random blockages is analyzed, and the metric of maximum outage probability minimization is proposed. To address this problem, stochastic optimization techniques are adopted and closed-form solutions of the BS and RIS beamformings are then obtained. The proposed stochastic optimization algorithms are proved to converge to the set of stationary points. Third, a framework of robust transceiver design scheme is proposed to address the channel uncertainty caused by the cascaded BS-RIS-user channel estimation error. Two cascaded channel error models are analyzed, and the correspondingly two robust beamforming design problems are proposed. The optimization theory is used to address the complex non-convex optimization problems. The numerical results show that the proposed robust scheme can effectively resist channel uncertainty. Finally, robust transceiver design schemes are proposed in RIS-aided physical layer security systems. The schemes analyze the channel uncertainties caused by the eavesdropper who launches an active attack, and by the eavesdropper conducting passive eavesdropping. Numerical results show that the negative effect of the eavesdropper’s channel error is larger than that of the legitimate user

Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications: A Deep Reinforcement Learning Approach

In this paper, the intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communication system is studied, where an UAV is deployed to serve the user equipments (UEs) with the assistance of multiple IRSs mounted on several buildings to enhance the communication quality between UAV and UEs. We aim to maximize the overall weighted data rate and geographical fairness of all the UEs via jointly optimizing the UAV's trajectory and the phase shifts of reflecting elements of IRSs. Since the system is complex and the environment is dynamic, it is challenging to derive low-complexity algorithms by using conventional optimization methods. To address this issue, we first propose a deep Q-network (DQN)-based low-complex solution by discretizing the trajectory and phase shift, which is suitable for practical systems with discrete phase-shift control. Furthermore, we propose a deep deterministic policy gradient (DDPG)-based solution to tackle the case with continuous trajectory and phase shift design. The experimental results prove that the proposed solutions achieve better performance compared to other traditional benchmarks.Comment: 12 pages, 13 figure