Fast Beam Training for IRS-Assisted Multiuser Communications

TL;DR

This paper introduces a multi-beam training method for IRS-assisted multiuser systems that reduces training time while maintaining effective beamforming, by dividing IRS elements into sub-arrays for simultaneous steering.

Contribution

It proposes a novel multi-beam training approach that outperforms traditional single-beam methods in efficiency and effectiveness for IRS passive beamforming.

Findings

Multi-beam training reduces training time significantly.

The method achieves comparable beamforming performance to traditional methods.

Simulation results validate the efficiency and effectiveness of the proposed approach.

Abstract

In this letter, we consider an intelligent reflecting surface (IRS)-assisted multiuser communication system, where an IRS is deployed to provide virtual line-of-sight (LoS) links between an access point (AP) and multiple users. We consider the practical codebook-based IRS passive beamforming and study efficient design for IRS reflect beam training, which is challenging due to the large number of IRS reflecting elements. In contrast to the conventional single-beam training, we propose a new multi-beam training method by dividing the IRS reflecting elements into multiple sub-arrays and designing their simultaneous multi-beam steering over time. By simply comparing the received signal power over time, each user can detect its optimal IRS beam direction with a high probability, even without searching over all possible beam directions as the single-beam training. Simulation results show that our proposed multi-beam training significantly reduces the training time of conventional single-beam training and yet achieves comparable IRS passive beamforming performance for data transmission.