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

Weifeng Zhu, Junyuan Gao, Shuowen Zhang, Meixia Tao, Liang Liu

TL;DR

This work tackles joint multi-user tracking and signal detection in RIS-assisted cell-free ISAC systems under random LOS blockages. It introduces a probabilistic signaling model and a novel HVMP algorithm that fuses variational and message-passing techniques to online estimate user states (position and velocity) and transmit signals while automatically inferring link blockages. The authors derive a Bayesian Cramér-Rao bound to characterize performance limits and formulate a weighted BCRB minimization to optimize RIS phase profiles, demonstrating significant tracking and detection gains over representative Bayesian methods. Results show centimeter-level tracking accuracy for moderate-to-high mobility and highlight the value of combining direct and RIS-reflected paths, with RIS optimization yielding further improvements toward the fundamental limits.

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.

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

TL;DR

This work tackles joint multi-user tracking and signal detection in RIS-assisted cell-free ISAC systems under random LOS blockages. It introduces a probabilistic signaling model and a novel HVMP algorithm that fuses variational and message-passing techniques to online estimate user states (position and velocity) and transmit signals while automatically inferring link blockages. The authors derive a Bayesian Cramér-Rao bound to characterize performance limits and formulate a weighted BCRB minimization to optimize RIS phase profiles, demonstrating significant tracking and detection gains over representative Bayesian methods. Results show centimeter-level tracking accuracy for moderate-to-high mobility and highlight the value of combining direct and RIS-reflected paths, with RIS optimization yielding further improvements toward the fundamental limits.

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.
Paper Structure (27 sections, 48 equations, 11 figures, 1 algorithm)

This paper contains 27 sections, 48 equations, 11 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Mobility Model
  4. Signal Model
  5. Channel Model
  6. Problem Formulation
  7. ISAC Protocol
  8. Probabilistic Signal Representation
  9. Bayesian Problem Formulation
  10. Joint Multiuser Tracking and Signal Detection Algorithm
  11. Graphical Representation
  12. Hybrid Variational Message Passing
  13. Messages between $\dot{\mathbf{p}}_{k,t}$ and $\dot{f}_{k,t}$
  14. Messages between $\mathbf{p}_{k,t}$ and $f_{k,t}$
  15. Messages between $\tilde{s}_{k,t}$ and $l_{k,t}$
  16. ...and 12 more sections

Figures (11)

  • Figure 1: Illustration of the system model. Multiple RISs are deployed in the system to assist the sensing and communication service of users.
  • Figure 2: An example illustration of the ISAC protocol with $Q_1 = 4$, $I = 3$, $\Delta Q = 9$, and $\Delta N = 3$. Then, we have $\mathcal{N}_{\rm I} = \{1,4,7\}$ and $\mathcal{Q}_{\rm I} = \{ 1,2,3,4 \} \cup \{ 10,11,12,13 \} \cup \{ 19,20,21,22 \}$.
  • Figure 3: The tensor representation of the received signal.
  • Figure 4: Factor graph of the probabilistic model.
  • Figure 5: The convergence performance of the proposed algorithm: (left) position estimation, (middle) signal detection, (right) velocity estimation.
  • ...and 6 more figures

Theorems & Definitions (4)

  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4