RIS-Aided MmWave Hybrid Relay Network Based on Multi-Agent Deep Reinforcement Learning

In millimeter wave (mmWave) communication, the utilization of multi-hop relay technology has been regarded as a promising approach to overcome the significant path loss encountered during signal transmission. However, the traditional active relay network suffers from low energy efficiency (EE) and uneven resource distribution. To address these challenges, we introduce Reconfigurable Intelligent Surface (RIS) as a passive relay to the mmWave communication system and create a hybrid relay system that combines passive and active relays, which aims to improve EE through the multi-hop relay. Additionally, with the development of Artificial Intelligence, deep Q-learning (DQN) is applied to optimize the hybrid relay system in this paper, where every transmitted signal of the base station (BS) is considered an agent. In this approach, the network is trained based on the interaction between the collected environment information and the users’ relay allocation strategy. Considering competition-cooperation relationships of multiple users, we propose a multi-agent DQN (MADQN) algorithm to allocate the relay resource where the primary goal is maximizing EE. Simulation results demonstrate that our proposed scheme can effectively converge to the optimal relay link, further improving EE and reducing energy consumption in comparison with conventional schemes.