PAPIR: Practical RIS-aided Localization via Statistical User Information

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.

I. Introduction

The radio environment represents the main propagation means that has been deeply analyzed to make wireless communications efficient and reliable. In particular, such a blackbox model has been lumped together with advance coding solutions to properly tackle uncontrolled fading issues while still facilitating reasonably-stable channels. Recently, reconfigurable intelligent surfaces (RISs) appear as the revolutionary and emerging technology bringing the ability of controlling— with passive devices—such propagation environment, via, e.g, backscattering or phase-shifting the incoming electromagnetic waves: this overcomes the traditional adversary perception of the channel thereby turning it into an optimization variable and, in turn, tunable parameter [1]–[5].