Bayesian User Localization and Tracking for Reconfigurable Intelligent Surface Aided MIMO Systems

Abstract

In this paper, we study the user localization and tracking problem in the\nreconfigurable intelligent surface (RIS) aided multiple-input multiple-output\n(MIMO) system, where a multi-antenna base station (BS) and multiple RISs are\ndeployed to assist the localization and tracking of a multi-antenna user. By\nestablishing a probability transition model for user mobility, we develop a\nmessage-passing algorithm, termed the Bayesian user localization and tracking\n(BULT) algorithm, to estimate and track the user position and the\nangle-of-arrival (AoAs) at the user in an online fashion. We also derive\nBayesian Cram\\'er Rao bound (BCRB) to characterize the fundamental performance\nlimit of the considered tracking problem. To improve the tracking performance,\nwe optimize the beamforming design at the BS and the RISs to minimize the\nderived BCRB. Simulation results show that our BULT algorithm can perform close\nto the derived BCRB, and significantly outperforms the counterpart algorithms\nwithout exploiting the temporal correlation of the user location.\n