Direct Localization for Massive MIMO

TLDR

Large‑scale MIMO offers high angular resolution, enabling very accurate localization, but multipath channels degrade angle‑of‑arrival based two‑step localization by obscuring line‑of‑sight paths. The authors propose a direct localization method that jointly processes observations from distributed massive MIMO base stations to overcome the suboptimality of the traditional two‑step approach. Their method employs a novel compressed‑sensing framework that exploits channel properties to separate line‑of‑sight from non‑line‑of‑sight paths. This approach yields improved localization performance compared to existing 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 suboptimal, 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.