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

2025-03-09

AI Summary

This paper addresses the integrated sensing and communication (ISAC) challenge in cell-free massive MIMO systems, where distributed access points (APs) at arbitrary locations must simultaneously support multi-user communications and target detection/tracking under strict synchronization constraints. To this end, we propose a generalized likelihood ratio test (GLRT)-based joint communication-sensing framework, featuring a scalable AP–user/target dynamic association rule and an ISAC-oriented fractional power control mechanism. We further introduce multi-zone sensing modeling, distributed association optimization, and successive convex approximation (SCA) to enhance scalability. Compared with state-of-the-art approaches, the proposed framework significantly improves multi-target detection probability and tracking accuracy, enables controllable trade-offs between communication rate and sensing QoS, quantifies interference impact, and demonstrates both computational efficiency and performance scalability in systems with up to one thousand APs.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Develops a unified framework for multi-target detection and tracking in cell-free massive MIMO systems.
• Proposes scalable algorithms for handling unknown target responses and interference in ISAC tasks.
• Introduces a scalable power control mechanism balancing communication and sensing performance.

Innovation

• Unified framework for ISAC in cell-free MIMO
• Scalable power control for ISAC tasks
• Generalized Likelihood Ratio Test for detection