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.

本文研究了利用可重构智能表面 (RIS) 反射阵列所协助的 MIMO 传输，通过深度强化学习 (DRL) 基于试错交互获得基站传输波束和 RIS 相位移位的联合设计。仿真结果表明，该算法学习能力逐渐提高，与两个最新的基准算法相比表现相当。