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

• URL: http://arxiv.org/abs/2205.08788v1
• Date: Wed, 18 May 2022 08:25:36 GMT
• Title: Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems
• Authors: Wangyang Xu, Jiancheng An, Chongwen Huang, Lu Gan, and Chau Yuen
• Abstract summary: This letter offers a novel deep reinforcement learning (DRL) algorithm based on a location-aware imitation environment for the joint beamforming design. 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.
• Score: 17.713210541836155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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