Terahertz Multi-User Massive MIMO with Intelligent Reflecting Surface: Beam Training and Hybrid Beamforming

TL;DR

This paper introduces a novel joint beam training and hybrid beamforming scheme for THz multi-user MIMO systems with IRS, addressing channel estimation challenges and achieving near-optimal performance with reduced complexity.

Contribution

It proposes a cooperative beam training scheme with hierarchical codebooks and cost-efficient hybrid beamforming designs for THz IRS-assisted multi-user MIMO systems.

Findings

Achieves near-optimal performance close to fully digital beamforming.

Develops hierarchical codebooks balancing robustness and complexity.

Demonstrates effectiveness through simulations.

Abstract

Terahertz (THz) communications open a new frontier for the wireless network thanks to their dramatically wider available bandwidth compared to the current micro-wave and forthcoming millimeter-wave communications. However, due to the short length of THz waves, they also suffer from severe path attenuation and poor diffraction. To compensate the THz-induced propagation loss, this paper proposes to combine two promising techniques, viz., massive multiple input multiple output (MIMO) and intelligent reflecting surface (IRS), in THz multi-user communications, considering their significant beamforming and aperture gains. Nonetheless, channel estimation and low-cost beamforming turn out to be two main obstacles to realizing this combination, due to the passivity of IRS for sending/receiving pilot signals and the large-scale use of expensive RF chains in massive MIMO. In view of these limitations, this paper first develops a cooperative beam training scheme to facilitate the channel estimation with IRS. In particular, we design two different hierarchical codebooks for the proposed training procedure, which are able to balance between the robustness against noise and searching complexity. Based on the training results, we further propose two cost-efficient hybrid beamforming (HB) designs for both single-user and multi-user scenarios, respectively. Simulation results demonstrate that the proposed joint beam training and HB scheme is able to achieve close performance to the optimal fully digital beamforming (FDB) which is implemented even under perfect channel state information (CSI).