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

TL;DR

This paper introduces a location-aware deep reinforcement learning algorithm for joint beamforming in RIS-aided mmWave MIMO systems, utilizing an imitation environment based on user location to improve robustness and reduce interaction overhead.

Contribution

It presents a novel DRL approach that leverages a location-aware imitation environment for efficient beamforming in RIS-assisted mmWave systems, enhancing robustness and reducing interaction complexity.

Findings

The proposed DRL algorithm outperforms existing methods in robustness.

It reduces interaction overhead compared to traditional DRL approaches.

Simulation results confirm improved performance in beamforming tasks.

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.