Two-Dimensional Channel Parameter Estimation for IRS-Assisted Networks

TL;DR

This paper introduces a low-complexity, two-dimensional channel parameter estimation method for IRS-assisted networks, leveraging pilot design and tensor structures to improve accuracy and reduce complexity.

Contribution

It develops a novel pilot decoupling-based estimation approach exploiting array geometry and tensor structures, outperforming existing methods in complexity and accuracy.

Findings

Proposed estimators achieve high accuracy in parameter estimation.

Method outperforms state-of-the-art in complexity and accuracy.

Performance-complexity tradeoffs are effectively managed.

Abstract

This paper proposes a pilot decoupling-based two-dimensional channel parameter estimation method for intelligent reflecting surface (IRS)-assisted networks. We exploit the combined effect of Terahertz sparse propagation and the geometrical structure of arrays deployed at the base station, the IRS, and the user equipment to develop a low-complexity channel parameter estimation method. By means of a new pilot design along the horizontal and vertical domains, the overall channel parameter estimation problem is decoupled into different domains. Furthermore, with this decoupling, it is possible to simultaneously sense/estimate the channel parameters and to communicate with the sensed node. Specifically, we derive two estimators by decoupling the global problem into sub-problems and exploiting the built-in tensor structure of the sensing/estimation problem by means of multiple rank-one approximations. The Cram\'er-Rao lower bound is derived to assess the performance of the proposed estimators. We show that the two proposed methods yield accurate parameter estimates and outperform state-of-the-art methods in terms of complexity. The tradeoffs between performance and complexity offered by the proposed methods are discussed and numerically assessed.