Joint Deployment Design and Phase Shift of IRS-Assisted 6G Networks: An Experience-Driven Approach

Abstract

The performance of wireless networks is constrained by the dynamic and random nature of the wireless channels. Intelligent reflecting surface (IRS) is a promising approach that can smartly reconfigure wireless propagation environment to increase the spectral efficiency in 6G networks. However, IRS deployment optimization in a complex and random 6G environment remains a limiting factor in improving the performance. To address the issue, we propose a deep reinforcement learning (DRL) network empowered by a generative adversarial network (GAN) to jointly optimize the IRS placement and reflecting beamforming matrix of IRS as well as the transmit beamforming at the base station (BS) in an IRS-assisted wireless network. Simulation results show that the proposed technique outperforms the benchmark scheme in terms of achievable rate and signal-to-noise ratio (SNR) by learning the optimal IRS locations in an IRS-aided wireless network.