Training Signal Design for Sparse Channel Estimation in Intelligent Reflecting Surface-Assisted Millimeter-Wave Communication

TL;DR

This paper proposes a novel IRS reflection pattern design method for sparse mmWave channel estimation, minimizing the Cramér-Rao bound to improve accuracy in intelligent reflecting surface-assisted communication.

Contribution

It derives a closed-form CRB for sparse mmWave channels and introduces a reflection pattern design method that minimizes this bound under practical constraints.

Findings

The proposed design reduces channel estimation error.

Numerical results show improved estimation accuracy.

The method is effective under constant modulus constraints.

Abstract

In this paper, the problem of training signal design for intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) communication under a sparse channel model is considered. The problem is approached based on the Cram$\acute{\text{e}}$r-Rao lower bound (CRB) on the mean-square error (MSE) of channel estimation. By exploiting the sparse structure of mmWave channels, the CRB for the channel parameter composed of path gains and path angles is derived in closed form under Bayesian and hybrid parameter assumptions. Based on the derivation and analysis, an IRS reflection pattern design method is proposed by minimizing the CRB as a function of design variables under constant modulus constraint on reflection coefficients. Numerical results validate the effectiveness of the proposed design method for sparse mmWave channel estimation.