Reconfigurable Intelligent Surface-Assisted Multiuser Tracking and Signal Detection in ISAC

2025-09-17

AI Summary

To address the performance degradation in tracking and detection caused by state update latency in high-mobility multi-user RIS-aided ISAC systems, this paper establishes a joint probabilistic signal model and proposes a Bayesian framework for concurrent estimation of user states (position/velocity) and communication signals. We innovatively design a hybrid variational message-passing algorithm enabling low-complexity online inference, thereby jointly optimizing tracking accuracy and signal detection performance. By integrating RIS channel control, probabilistic graphical modeling, and variational inference, we formulate an end-to-end joint sensing-communication signal processing paradigm. Experimental results demonstrate that the proposed method significantly outperforms conventional Bayesian filters—including the Extended Kalman Filter (EKF) and Particle Filter (PF)—in both root-mean-square position error and bit error rate, particularly exhibiting superior robustness under high-speed mobility scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization

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 multiuser tracking and signal detection problem in integrated sensing and communication (ISAC) systems with the assistance of reconfigurable intelligent surfaces (RISs). Due to the diverse and high user mobility, the tracking and signal detection performance can be significantly deteriorated without choreographed user state (position and velocity) updating principle. To tackle this challenge, we manage to establish a comprehensive probabilistic signal model to 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 algorithm for the online estimation of user states, which can iteratively update the posterior probabilities of user states during each tracking frame with computational efficiency. Numerical results are provided to demonstrate that the proposed algorithm can significantly improve both of the tracking and signal detection performance over the representative Bayesian estimation counterparts.

Problem

• Multiuser tracking and signal detection in ISAC systems
• Addressing performance deterioration from high user mobility
• Establishing probabilistic signal model for state interdependencies

Innovation

• Reconfigurable intelligent surfaces assist ISAC
• Probabilistic signal model characterizes state interdependencies
• Hybrid variational message passing enables online estimation