Deep Reinforcement Learning-Based Beamforming Design for Multi-Irs Systems

Le résumé fourni par la source

Intelligent Reflecting Surfaces (IRS) enable the smart configuration of wireless environments via software control of a larger number of programmable passive reflective elements. A multi-IRS system has been shown to exhibit superior performance when compared to a single-IRS system. However, challenges such as reflection optimization, channel estimation, and IRS deployment are becoming increasingly complex. In this paper, we investigate the beamforming optimization problem of a massive multiple-input multiple-output (MIMO) system aided by multi-IRS scenarios. In the scenario, a multi-antenna base station (BS) transmits orthogonal beams toward the user equipment (UE), which are received after being reflected through multiple different IRS along the Line-Of-Sight (LoS) link. As the number of reflections increases, the power attenuation of the signal will be greater. To identify the optimal beamforming routing path so that maximize the received signal, an reasonable balance of the path has become the key to solving the problem. To address this issue, first, beamforming is performed for the base station IRS system, and then the problem is approximated as a shortest path problem by combining the obtained channel information and location information, and processed according to the principles of graph theory. Subsequently, apply the deep reinforcement learning algorithm for the IRS to the UE part to determine the optimal beamforming path. The simulation results demonstrate that the outcomes obtained through our algorithm for optimizing the cooperative beamforming of multiple IRS are significantly better than those without optimization or without considering multi-hop, thus confirming the effectiveness of this algorithm.

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