Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for MISO Communication Systems

• Published in: IEEE wireless communications letters Vol. 9; no. 5; pp. 745 - 749
• Main Authors: Feng, Keming, Wang, Qisheng, Li, Xiao, Wen, Chao-Kai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Communications systems; Complexity theory; Convexity; Deep learning; deep reinforcement learning; Intelligent reflecting surface; MISO (control systems); MISO communication; non-convex optimization; Optimization; phase shift design; Reconfigurable intelligent surfaces; Reinforcement learning; Signal to noise ratio; Training; Upper bounds; Wireless communication
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2020.2969167

This letter investigates the intelligent reflecting surface (IRS)-aided multiple-input single-output wireless transmission system. Particularly, the optimization of the passive phase shift of each element at IRS to maximize the downlink received signal-to-noise ratio is considered. Inspired by the huge success of deep reinforcement learning (DRL) on resolving complicated control problems, we develop a DRL based framework to solve this non-convex optimization problem. Numerical results reveal that the proposed DRL based framework can achieve almost the upper bound of the received SNR with relatively low time consumption.