Joint Beamforming and Phase Shift Design for IRS-Aided Vehicular Networks

Abstract

Vehicular networks require massive communication connections between vehicles and infrastructure to support the high data rate vehicular service applications. However, due to the obstruction of buildings in urban areas, the channel capacity of vehicle-to-infrastructure (V2I) links will be deteriorated. Thus, intelligent reflecting surface (IRS) is introduced to aid vehicular communications to increase the channel capacity of V2I links. In this paper, we aim to maximize the sum V2I capacity by jointly optimizing the transmit beamforming matrix at the base station (BS) and the phase shifts at the IRS. Most of the existing works adopt alternating optimization-based iterative algorithms to tackle the joint beamforming and phase shift optimization problem, which suffer from high computational complexity. Therefore, we propose an unsupervised learning (UL)-based algorithm with a two-stage network architecture to address the joint optimization problem. The network architecture consists of a two-stage transformer network, which can implicitly learn the spatial and temporal features of historical channels to further improve the learning performance. Simulation results show that the proposed UL-based algorithm can obtain the comparable performance with much lower computational complexity compared with the conventional alternating optimization-based iterative algorithm.