Channel Estimation for Wireless Communication Systems Assisted by Large Intelligent Surfaces

TL;DR

This paper proposes a novel channel estimation scheme for wireless systems aided by large intelligent surfaces, improving accuracy especially in low SNR conditions through a constrained optimization approach.

Contribution

It introduces a Lagrange multiplier-based estimation method tailored for intelligent surface-assisted channels, providing a closed-form iterative solution and performance bounds.

Findings

Estimation accuracy improved by up to 18% in low SNR conditions.

The proposed scheme outperforms traditional least squares estimation.

Cramer-Rao bounds are derived for performance benchmarking.

Abstract

In this letter, the channel estimation problem is studied for wireless communication systems assisted by large intelligent surface. Due to features of assistant channel, channel estimation (CE) problem for the investigated system is shown as a constrained estimation error minimization problem, which differs from traditional CE problems. A Lagrange multiplier and dual ascent-based estimation scheme is then designed to obtain a closed-form solution for the estimator iteratively. Moreover, the Cramer-Rao lower bounds are deduced for performance evaluation. Simulation results show that the designed scheme could improve estimation accuracy up to 18%, compared with least square method in low signal-to-noise ratio regime.