Joint Multi-User Tracking and Signal Detection in Reconfigurable Intelligent Surface-Assisted Cell-Free ISAC Systems

TL;DR

This paper presents a novel Bayesian approach with a hybrid variational message passing algorithm for joint multi-user tracking and signal detection in RIS-assisted cell-free ISAC systems, addressing high mobility challenges.

Contribution

It introduces a probabilistic signal model and a hybrid variational message passing algorithm for efficient online joint estimation in high-mobility RIS-assisted systems.

Findings

Significant improvement in tracking accuracy

Enhanced signal detection performance

Effective RIS phase profile optimization

Abstract

This paper investigates the cell-free multi-user integrated sensing and communication (ISAC) system, where multiple base stations collaboratively track the users and detect their signals. Moreover, reconfigurable intelligent surfaces (RISs) are deployed to serve as additional reference nodes to overcome the line-of-sight blockage issue of mobile users for accomplishing seamless sensing. Due to the high-speed user mobility, the multi-user tracking and signal detection performance can be significantly deteriorated without elaborated online user kinematic state updating principles. To tackle this challenge, we first manage to establish a probabilistic signal model to comprehensively characterize the interdependencies among user states, transmit signals, and received signals during the tracking procedure. Based on the Bayesian problem formulation, we further propose a novel hybrid variational message passing (HVMP) algorithm to realize computationally efficient joint estimation of user states and transmit signals in an online manner, which integrates VMP and standard MP to derive the posterior probabilities of estimated variables. Furthermore, the Bayesian Cramer-Rao bound is provided to characterize the performance limit of the multi-user tracking problem, which is also utilized to optimize RIS phase profiles for tracking performance enhancement. Numerical results demonstrate that the proposed algorithm can significantly improve both tracking and signal detection performance over the representative Bayesian estimation counterparts.