Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems

2022/05/18 by Xu, Wangyang, An, Jiancheng, Huang, Chongwen +2 · 1 citation
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2205.08788

Abstract

Reconfigurable intelligent surface (RIS) has recently gained popularity as a promising solution for improving the signal transmission quality of wireless communications with less hardware cost and energy consumption. This letter offers a novel deep reinforcement learning (DRL) algorithm based on a location-aware imitation environment for the joint beamforming design in an RIS-aided mmWave multiple-input multiple-output system. Specifically, we design a neural network to imitate the transmission environment based on the geometric relationship between the user's location and the mmWave channel. Following this, a novel DRL-based method is developed that interacts with the imitation environment using the easily available location information. Finally, simulation results demonstrate that the proposed DRL-based algorithm provides more robust performance without excessive interaction overhead compared to the existing DRL-based approaches.

Cited by

• Near-optimal Reconfigurable Intelligent Surface Configuration: Blind Beamforming with Sensing

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