Direct Localization for Massive MIMO

TL;DR

This paper introduces a direct localization method for massive MIMO systems that jointly processes signals from multiple base stations using a novel compressed sensing framework, significantly improving accuracy over traditional AOA-based methods in multipath environments.

Contribution

It proposes a new direct localization approach leveraging compressed sensing to distinguish LOS from NLOS paths, enhancing accuracy in multipath scenarios.

Findings

Improved localization accuracy in multipath environments.

Effective joint processing of distributed MIMO signals.

Superior performance compared to traditional AOA-based methods.

Abstract

Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization thanks to their high angular resolution. A difficult problem arising indoors and outdoors is localizing users over multipath channels. Localization based on angle of arrival (AOA) generally involves a two-step procedure, where signals are first processed to obtain a user's AOA at different base stations, followed by triangulation to determine the user's position. In the presence of multipath, the performance of these methods is greatly degraded due to the inability to correctly detect and/or estimate the AOA of the line-of-sight (LOS) paths. To counter the limitations of this two-step procedure which is inherently sub-optimal, we propose a direct localization approach in which the position of a user is localized by jointly processing the observations obtained at distributed massive MIMO base stations. Our approach is based on a novel compressed sensing framework that exploits channel properties to distinguish LOS from non-LOS signal paths, and leads to improved performance results compared to previous existing methods.