Channel Estimation and Transmission for Intelligent Reflecting Surface Assisted THz Communications

TL;DR

This paper proposes new channel estimation and transmission methods for IRS-assisted THz communications, using beam training and hierarchical codebooks to improve efficiency and performance without full channel reconstruction.

Contribution

It introduces a low-complexity hierarchical beam training scheme and a cooperative channel estimation process tailored for IRS-assisted THz systems.

Findings

Hierarchical search codebook reduces beam training complexity.

Proposed methods achieve lower SNR requirements for effective communication.

Simulation results validate the efficiency and performance improvements.

Abstract

Intelligent reflecting surface (IRS) is envisioned as a promising technology to broaden signal coverage and enhance transmission in terahertz (THz) communications. Due to the passivity of IRS, the channel measurement can not be achieved by traditional pilot manner and the subsequent cooperative transmission design remains an open problem. This paper investigates the channel estimation and transmission solutions for massive multiple input multiple output (MIMO) IRS-assisted THz system. The channel estimation is realized by beam training and the quantization error is analyzed for evaluating performance. In addition, a novel hierarchical search codebook design is proposed as a low-complexity basis of beam training. Based on above foundations, we propose a cooperative channel estimation procedure to tactfully acquire the channel knowledge. Finally, by leveraging obtained channel information, the designs of IRS and transceivers are directly provided in closed form without reconstructing the full channel matrix or additional optimization. Simulation and numerical results are presented to illustrate the minimum signal to noise ratio (SNR) required for beam training and the efficacy of the proposed transmission solutions.