Scalable Integrated Sensing and Communications for Multi-Target Detection and Tracking in Cell-Free Massive MIMO: A Unified Framework

TL;DR

The paper tackles scalable integrated sensing and communications in cell-free massive MIMO by proposing a unified framework that enables simultaneous user data transmission and multi-target detection/tracking with arbitrary target positions. It introduces GLRT-based detectors for unknown target responses and clutter, along with scalable AP-UE and AP-target associations and a generalized fractional power control tailored for ISAC. A non-scalable benchmark based on SCA is provided for comparison. Extensive simulations demonstrate robust detection/tracking performance and reveal essential trade-offs between sensing and communication, highlighting the practical impact of interference from other targets and the benefits of a scalable, distributed approach.

Abstract

This paper investigates a cell-free massive MIMO (multiple-input multiple-output) system where distributed access points (APs) perform integrated sensing and communications (ISAC) tasks, enabling simultaneous user communication and target detection/tracking. A unified framework and signal model are developed for the detection of potential targets and tracking of previously detected ones, even in arbitrary positions. Leveraging the Generalized Likelihood Ratio Test technique, novel detection/tracking algorithms are proposed to handle unknown target responses and interference. Scalable AP-user and AP-target association rules are evaluated, explicitly considering multi-zone sensing scenarios. Additionally, a scalable power control mechanism extends fractional power control principles to ISAC, balancing power allocation between communication and sensing tasks. For benchmarking, a non-scalable power control optimization problem is also formulated to maximize the minimum user data rate while ensuring a Quality of Service constraint for sensing, solved via successive convex approximation. Extensive numerical results validate the proposed framework, demonstrating its effectiveness in both communication and sensing, revealing the impact of interference from other targets, and highlighting fundamental trade-offs between sensing and communication performance.

Figures (5)

• Figure 1: Illustrative setup. Several APs cooperate to perform communication and sensing tasks. The sets ${\cal M}_{\mathbf{p}}^{\rm tx}$ and ${\cal M}_{\mathbf{p}}^{\rm rx}$ refer to the transmit and receive APs inspecting position $\mathbf{p}$, respectively. The set ${\cal K}_m$ of UEs is served by AP $m$ and the set ${\cal M}_k$ of APs is serving UE $k$.
• Figure 2: Miss probability $p_{\rm M}$ vs. RCS variance $\sigma_{\alpha}^2$ for different clutter percentages $\varsigma$ with (full) ISAC, w/o sensing, and w/o clutter stats.
• Figure 4: CDF of the SCNR vs. number of UEs $K$ (left) and number of sensing samples $\tau_s$ (right). The solid blue line indicates the baseline.
• Figure 6: CDF of the SCNR (left) and data rate (right) vs. FPC. Dashed and dotted lines indicate the absence of sensing or communication.
• Figure 8: CDF of the SICNR (left) and data rate (right) vs. FPC. Dashed and dotted lines indicate the absence of sensing or communication.