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

• Published in: IEEE internet of things journal 2023-10, Vol.10 (20), p.1-1
• Main Authors: Naeem, Faisal, Qaraqe, Marwa, Celebi, Hasari
• Format: Article
• Language: English
• Subjects: 6G mobile communication; Array signal processing; Beamforming; Deep learning; Generative adversarial network; Generative adversarial networks; Intelligent reflecting surface; Optimization; Radio equipment; Receivers; Reinforcement learning; Signal to noise ratio; Transmitters; Wireless networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3278384

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.