Work

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

Year: 2022

Type: article

Abstract: In this paper, we study the user localization and tracking problem in the reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) system, where a multi-antenna base station (BS) and multiple RISs are deployed to assist the localization and tracking of a multi-antenna user. By establishing a probability transition model for user mobility, we develop a message-passing algorithm, termed the Bayesian user localization and tracking (BULT) algorithm, to estimate and track the user position and the angles-of-arrivals (AoAs) at the user in an online fashion. We also derive the Bayesian Cramér Rao bound (BCRB) to characterize the fundamental performance limit of the considered tracking problem. To improve the tracking performance, we optimize the beamforming design at the BS and the RISs to minimize the derived BCRB. Simulation results show that our BULT algorithm can perform close to the derived BCRB, and significantly outperforms the counterpart algorithms without exploiting the temporal correlation of the user location.

Source: IEEE Journal of Selected Topics in Signal Processing

DOI: 10.1109/jstsp.2022.3173747

Authors: Boyu Teng, Xiaojun Yuan, Rui Wang, Shi Jin

Institutions: University of Electronic Science and Technology of China, National Key Laboratory of Science and Technology on Communications, Tongji University, Southeast University

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Award: 智能反射面辅助的无线MIMO系统的物理层关键技术研究