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

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.