Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers

TL;DR

This paper introduces an algorithm to learn optimal phase-shifts for holographic metasurface transceivers, improving wireless communication coverage and rate by accurately estimating channel information even in noisy environments.

Contribution

It presents a novel algorithm that exploits channel gain structure to learn phase-shifts, with proven exponential decay in estimation error probability and validated through extensive simulations.

Findings

Algorithm achieves accurate phase-shift estimation in noisy conditions.

The probability of deviation from true phase-shifts decays exponentially with more pilot signals.

Significant performance gains over existing policies are demonstrated.

Abstract

Holographic metasurface transceivers (HMT) is an emerging technology for enhancing the coverage and rate of wireless communication systems. However, acquiring accurate channel state information in HMT-assisted wireless communication systems is critical for achieving these goals. In this paper, we propose an algorithm for learning the optimal phase-shifts at a HMT for the far-field channel model. Our proposed algorithm exploits the structure of the channel gains in the far-field regions and learns the optimal phase-shifts in presence of noise in the received signals. We prove that the probability that the optimal phase-shifts estimated by our proposed algorithm deviate from the true values decays exponentially in the number of pilot signals. Extensive numerical simulations validate the theoretical guarantees and also demonstrate significant gains as compared to the state-of-the-art policies.