PAPIR: Practical RIS-aided Localization via Statistical User Information

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.