Hierarchical Codebook Design and Analytical Beamforming Solution for IRS Assisted Communication

TL;DR

This paper introduces a novel NCPD beamforming algorithm for IRS-assisted communication, enabling efficient hierarchical codebook design and beam training with reduced complexity and improved accuracy for large IRS systems.

Contribution

It proposes a continuous phase shift model and a flexible NCPD algorithm, along with hierarchical codebooks and two beam training schemes, enhancing beamforming accuracy and stability.

Findings

NCPD algorithm achieves smaller side lobes and higher accuracy.

Beam misalignment rate is significantly reduced.

Time complexity remains constant regardless of IRS size.

Abstract

In intelligent reflecting surface (IRS) assisted communication, beam search is usually time-consuming as the multiple-input multiple-output (MIMO) of IRS is usually very large. Hierarchical codebooks is a widely accepted method for reducing the complexity of searching time. The performance of this method strongly depends on the design scheme of beamforming of different beamwidths. In this paper, a non-constant phase difference (NCPD) beamforming algorithm is proposed. To implement the NCPD algorithm, we first model the phase shift of IRS as a continuous function, and then determine the parameters of the continuous function through the analysis of its array factor. Then, we propose a hierarchical codebook and two beam training schemes, namely the joint searching (JS) scheme and direction-wise searching (DWS) scheme by using the NCPD algorithm which can flexibly change the width, direction and shape of the beam formed by the IRS array. Simulation results show that the NCPD algorithm is more accurate with smaller side lobes, and also more stable on IRS of different sizes compared to other wide beam algorithms. The misalignment rate of the beam formed by the NCPD method is significantly reduced. The time complexity of the NCPD algorithm is constant, thus making it more suitable for solving the beamforming design problem with practically large IRS.