PAPIR: Practical RIS-aided Localization via Statistical User Information

TL;DR

PAPIR is a practical RIS-based localization system that uses statistical beamforming and iterative likelihood updates to accurately estimate user equipment positions in next-generation networks.

Contribution

It introduces a two-stage localization method leveraging RISs and statistical information, enhancing accuracy in 3D positioning for B5G/6G networks.

Findings

Effective 3D localization using RISs and statistical beamforming.

Iterative likelihood updates improve position estimation accuracy.

Applicable to next-generation communication networks.

Abstract

The integration of advanced localization techniques in the upcoming next generation networks (B5G/6G) is becoming increasingly important for many use cases comprising contact tracing, natural disasters, terrorist attacks, etc. Therefore, emerging lightweight and passive technologies that allow accurately controlling the propagation environment, such as reconfigurable intelligent surfaces (RISs), may help to develop advance positioning solutions relying on channel statistics and beamforming. In this paper, we devise PAPIR, a practical localization system leveraging on RISs by designing a two-stage solution building upon prior statistical information on the target user equipment (UE) position. PAPIR aims at finely estimating the UE position by performing statistical beamforming, direction-of-arrival (DoA) and time-of-arrival (ToA) estimation on a given three-dimensional search space, which is iteratively updated by exploiting the likelihood of the UE position.