Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint Localization and Channel Estimation at mmWave

TL;DR

This paper introduces a multidimensional orthogonal matching pursuit algorithm for efficient channel estimation in RIS-aided mmWave systems, enhancing both localization accuracy and reducing computational complexity in indoor scenarios.

Contribution

It proposes a novel multidimensional OMP approach that improves channel estimation efficiency and accuracy without relying on absolute time of arrival for localization.

Findings

High accuracy channel estimation achieved with reduced complexity.

Significant improvement in 3D indoor localization accuracy.

Efficiently handles large RIS and array sizes.

Abstract

RIS-aided millimeter wave wireless systems benefit from robustness to blockage and enhanced coverage. In this paper, we study the ability of RIS to also provide enhanced localization capabilities as a by-product of communication. We consider sparse reconstruction algorithms to obtain high resolution channel estimates that are mapped to position information. In RIS-aided mmWave systems, the complexity of sparse recovery becomes a bottleneck, given the large number of elements of the RIS and the large communication arrays. We propose to exploit a multidimensional orthogonal matching pursuit strategy for compressive channel estimation in a RIS-aided millimeter wave system. We show how this algorithm, based on computing the projections on a set of independent dictionaries instead of a single large dictionary, enables high accuracy channel estimation at reduced complexity. We also combine this strategy with a localization approach which does not rely on the absolute time of arrival of the LoS path. Localization results in a realistic 3D indoor scenario show that RIS-aided wireless system can also benefit from a significant improvement in localization accuracy.