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

2026-02-20

AI Summary

This work addresses the severe performance degradation in integrated sensing and communication (ISAC) under high-mobility multi-user scenarios, where line-of-sight link blockages and the absence of online user motion state updates critically impair both sensing and communication. To overcome these challenges, the authors formulate a probabilistic signal model that captures the coupling between user states and transceived signals, and for the first time introduce a reconfigurable intelligent surface (RIS) as a reference node into a cell-free ISAC system. Within a Bayesian framework, they propose a hybrid variational message passing (HVMP) algorithm to enable online joint estimation of multi-user states and signals, while simultaneously optimizing the RIS phase configuration via the Bayesian Cramér–Rao bound to enhance tracking accuracy. Experimental results demonstrate that the proposed approach significantly outperforms existing Bayesian estimation methods in both tracking precision and signal detection performance, validating the efficacy of RIS-aided HVMP joint optimization.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Tracking, profiling, and countermeasures against them

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.

Problem

• multi-user tracking
• signal detection
• reconfigurable intelligent surface
• cell-free ISAC
• user mobility

Innovation

• Reconfigurable Intelligent Surface (RIS)
• Integrated Sensing and Communication (ISAC)
• Hybrid Variational Message Passing (HVMP)
• Bayesian Cramér-Rao Bound
• Cell-Free Massive MIMO