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Integrated Massive Communication and Target Localization in 6G Cell-Free Networks

Junyuan Gao, Weifeng Zhu, Shuowen Zhang, Yongpeng Wu, Jiannong Cao, Giuseppe Caire, Liang Liu

TL;DR

The paper tackles joint activity detection, channel estimation, and passive target localization in a 6G cell-free ISAC network with a large user set and multiple targets. It introduces a probabilistic system model and a Turbo-HyMP algorithm that couples TL and ADCE via two interacting modules, employing von Mises and Gaussian approximations along with GAMP to manage complexity. Theoretical guarantees are provided through Bayesian Cramér–Rao bounds and state evolution analyses, while extensive simulations show substantial localization (≈6 dB RMSE improvement) and ADCE gains (≈1.5 dB NMSE improvement) over competitive baselines. The approach demonstrates the feasibility and performance benefits of embedding localization functionality into massive communication systems in future 6G networks, paving the way for networked sensing with centralized processing in cell-free deployments.

Abstract

This paper presents an initial investigation into the combination of integrated sensing and communication (ISAC) and massive communication, both of which are largely regarded as key scenarios in sixth-generation (6G) wireless networks. Specifically, we consider a cell-free network comprising a large number of users, multiple targets, and distributed base stations (BSs). In each time slot, a random subset of users becomes active, transmitting pilot signals that can be scattered by the targets before reaching the BSs. Unlike conventional massive random access schemes, where the primary objectives are device activity detection and channel estimation, our framework also enables target localization by leveraging the multipath propagation effects introduced by the targets. However, due to the intricate dependency between user channels and target locations, characterizing the posterior distribution required for minimum mean-square error (MMSE) estimation presents significant computational challenges. To handle this problem, we propose a hybrid message passing-based framework that incorporates multiple approximations to mitigate computational complexity. Numerical results demonstrate that the proposed approach achieves high-accuracy device activity detection, channel estimation, and target localization simultaneously, validating the feasibility of embedding localization functionality into massive communication systems for future 6G networks.

Integrated Massive Communication and Target Localization in 6G Cell-Free Networks

TL;DR

The paper tackles joint activity detection, channel estimation, and passive target localization in a 6G cell-free ISAC network with a large user set and multiple targets. It introduces a probabilistic system model and a Turbo-HyMP algorithm that couples TL and ADCE via two interacting modules, employing von Mises and Gaussian approximations along with GAMP to manage complexity. Theoretical guarantees are provided through Bayesian Cramér–Rao bounds and state evolution analyses, while extensive simulations show substantial localization (≈6 dB RMSE improvement) and ADCE gains (≈1.5 dB NMSE improvement) over competitive baselines. The approach demonstrates the feasibility and performance benefits of embedding localization functionality into massive communication systems in future 6G networks, paving the way for networked sensing with centralized processing in cell-free deployments.

Abstract

This paper presents an initial investigation into the combination of integrated sensing and communication (ISAC) and massive communication, both of which are largely regarded as key scenarios in sixth-generation (6G) wireless networks. Specifically, we consider a cell-free network comprising a large number of users, multiple targets, and distributed base stations (BSs). In each time slot, a random subset of users becomes active, transmitting pilot signals that can be scattered by the targets before reaching the BSs. Unlike conventional massive random access schemes, where the primary objectives are device activity detection and channel estimation, our framework also enables target localization by leveraging the multipath propagation effects introduced by the targets. However, due to the intricate dependency between user channels and target locations, characterizing the posterior distribution required for minimum mean-square error (MMSE) estimation presents significant computational challenges. To handle this problem, we propose a hybrid message passing-based framework that incorporates multiple approximations to mitigate computational complexity. Numerical results demonstrate that the proposed approach achieves high-accuracy device activity detection, channel estimation, and target localization simultaneously, validating the feasibility of embedding localization functionality into massive communication systems for future 6G networks.
Paper Structure (27 sections, 115 equations, 9 figures, 1 table, 1 algorithm)

This paper contains 27 sections, 115 equations, 9 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivation
  3. Related Works
  4. Main Contributions
  5. System Model
  6. Problem Statement
  7. Joint Activity Detection, Channel Estimation, and Target Localization via Hybrid Message Passing
  8. Factor Graph Representation
  9. Turbo Hybrid Message Passing Algorithm
  10. Message from $\mathbf{p}_{{\rm t},m}$ to $f_{m,q}$
  11. Message from $f_{m,q}$ to $\Delta_{m,q}$
  12. Message from $\Delta_{m,q}$ to $g_{l,n,q}$
  13. Message from $g_{l,n,q}$ to $c_{l,m,q}$
  14. Message from $c_{l,m,q}$ to $g_{l,n,q}$
  15. Message from $g_{l,n,q}$ to $\Delta_{m,q}$
  16. ...and 12 more sections

Figures (9)

  • Figure 1: System model.
  • Figure 2: Factor graph representation of the joint distribution \ref{['eq:joint_PDF']}.
  • Figure 3: Convergence behavior versus the turbo iteration number with $M = 3$ and $\xi = 0.9$: (a) Location RMSE of the proposed scheme; (b) Channel estimation NMSE of the proposed scheme; and (c) MSE for effective channel attenuation coefficient estimation of the proposed scheme and its SE.
  • Figure 4: Target localization, activity detection, and channel estimation performance versus $P$ with $L = 200, M = 3, \xi = 0.9$, and $N = 8$: (a) RMSE of location; (b) $P_{\rm MD} = P_{\rm FA}$; and (c) NMSE of channel estimation.
  • Figure 5: Target localization, activity detection, and channel estimation performance versus $N$ with $L = 200, M = 3, \xi = 0.9$, and $P = 10$ dBm: (a) RMSE of location; (b) $P_{\rm MD} = P_{\rm FA}$; and (c) NMSE of channel estimation.
  • ...and 4 more figures

Theorems & Definitions (2)

  • Remark 1
  • Remark 2