Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model

TL;DR

This paper proposes a cost-effective, sparse Bayesian learning method for joint estimation of THz channel and beam-split, addressing challenges like severe path loss and beam-split, with near-field considerations and federated learning enhancements.

Contribution

It introduces a novel array perturbation model for beam-split, a low-complexity estimation approach leveraging LOS dominance, and a federated learning-based model-free technique for THz channel estimation.

Findings

SBCE outperforms existing methods in accuracy.

The approach reduces hardware costs.

Theoretical bounds for near- and far-field parameters are derived.

Abstract

For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks. However, the acquisition of the THz channel entails several unique challenges such as severe path loss and beam-split. Prior works usually employ ultra-massive arrays and additional hardware components comprised of time-delayers to compensate for these loses. In order to provide a cost-effective solution, this paper introduces a sparse-Bayesian-learning (SBL) technique for joint channel and beam-split estimation. Specifically, we first model the beam-split as an array perturbation inspired from array signal processing. Next, a low-complexity approach is developed by exploiting the line-of-sight-dominant feature of THz channel to reduce the computational complexity involved in the proposed SBL technique for channel estimation (SBCE). Additionally, based on federated-learning, we implement a model-free technique to the proposed model-based SBCE solution. Further to that, we examine the near-field considerations of THz channel, and introduce the range-dependent near-field beam-split. The theoretical performance bounds, i.e., Cram\'er-Rao lower bounds, are derived both for near- and far-field parameters, e.g., user directions, beam-split and ranges. Numerical simulations demonstrate that SBCE outperforms the existing approaches and exhibits lower hardware cost.