Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

• Published in: IEEE journal on selected areas in communications Vol. 38; no. 8; pp. 1839 - 1850
• Main Authors: Huang, Chongwen, Mo, Ronghong, Yuen, Chau
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antenna arrays; Array signal processing; Beamforming; beamforming matrix; Computer simulation; Deep learning; deep reinforcement learning; Machine learning; Massive MIMO; Metamaterials; MIMO (control systems); MIMO communication; MISO (control systems); MISO communication; Neural networks; Optimization; Optimization techniques; phase shift matrix; Power consumption; Receivers; Reconfigurable intelligent surface; Reconfigurable intelligent surfaces; smart radio environment; Wireless communication; Wireless communication systems; Wireless communications
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2020.3000835

Recently, the reconfigurable intelligent surface (RIS), benefited from the breakthrough on the fabrication of programmable meta-material, has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology to achieve smart radio environments. Employed as reflecting arrays, RIS is able to assist MIMO transmissions without the need of radio frequency chains resulting in considerable reduction in power consumption. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL). We first develop a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action. Unlike the most reported works utilizing the alternating optimization techniques to alternatively obtain the transmit beamforming and phase shifts, the proposed DRL based algorithm obtains the joint design simultaneously as the output of the DRL neural network. Simulation results show that the proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks. It is also observed that, appropriate neural network parameter settings will improve significantly the performance and convergence rate of the proposed algorithm.