Intelligent Angle Map-based Beam Alignment for RIS-aided mmWave Communication Networks

TL;DR

This paper introduces an intelligent, machine learning-based beam alignment scheme for RIS-aided mmWave systems that leverages angle maps derived from location data, eliminating the need for traditional beam scanning.

Contribution

It proposes a novel angle map-based beam alignment method using Transformer models to learn location-angle relationships, enabling fast and accurate alignment in RIS-assisted mmWave networks.

Findings

Achieves high-precision beam alignment without beam scanning.

Demonstrates improved system performance using the proposed scheme.

Utilizes ray-tracing data to validate effectiveness.

Abstract

Recently, reconfigurable intelligent surface (RIS) has been widely used to enhance the performance of millimeter wave (mmWave) communication systems, making beam alignment more challenging. To ensure efficient communication, this paper proposes a novel intelligent angle map-based beam alignment scheme for both general user equipments (UEs) and RIS-aided UEs simultaneously in a fast and effective way. Specifically, we construct a beam alignment architecture that utilizes only angular information. To obtain the angle information, the currently hottest seq2seq model - the Transformer - is introduced to offline learn the relationship between UE geographic location and the corresponding optimal beam direction. Based on the powerful machine learning model, the location-angle mapping function, i.e., the angle map, can be built. As long as the location information of UEs is available, the angle map can make the acquisition of beam alignment angles effortless. In the simulation, we utilize a ray-tracing-based dataset to verify the performance of the proposed scheme. It is demonstrated that the proposed scheme can achieve high-precision beam alignment and remarkable system performance without any beam scanning.