Compressive Near-Field Localization for Multipath RIS-Aided Environments

• Published in: IEEE communications letters 2022-06, Vol.26 (6), p.1268-1272
• Main Authors: Rinchi, Omar, Elzanaty, Ahmed, Alouini, Mohamed-Slim
• Format: Article
• Language: English
• Subjects: 6G mobile communication; Algorithms; Atomic measurements; Cellular communication; Channel estimation; compressed sensing; Covariance matrices; Localization; Location awareness; Near fields; near-field; positioning; Reconfigurable intelligent surface (RIS); Signal to noise ratio; Sparse matrices
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2022.3151036

Reconfigurable intelligent surfaces (RISs) are considered among the key techniques to be adopted for sixth-generation cellular networks (6G) to enhance not only communications but also localization performance. In this regard, we propose a novel single-anchor localization algorithm for a state-of-the-art architecture where the position of the user equipment (UE) is to be estimated at the base station (BS) with the aid of a RIS. We consider a practical model that accounts for both near-field propagation and multipath environments. The proposed scheme relies on a compressed sensing (CS) technique tailored to address the issues associated with near-field localization and model mismatches. Also, the RIS phases are optimized to enhance the positioning performance, achieving more than one order of magnitude gain in the localization accuracy compared to RISs with non-optimized phases.