Cell-free ISAC for Drone Detection Considering Coverage and Age of Sensing

2025-12-07

AI Summary

To address coverage deficiency and stale sensing information in coexisting UAV detection and communication services, this paper proposes an integrated sensing-and-communication (ISAC) architecture based on cell-free massive MIMO. The design leverages distributed access point (AP) cooperation, dynamic hotspot grouping, AP clustering, and optimized allocation of sensing pilots to jointly enhance communication performance and aerial sensing capability. We innovatively define “Age of Sensing Information” (AoSI) to quantify end-to-end sensing timeliness and introduce “channel ambiguity” to characterize fundamental detection performance bottlenecks. Guided by these metrics, we formulate a joint multi-point sensing network configuration strategy. Experimental results demonstrate that when the number of hotspots equals the number of sensing pilots, AoSI and coverage achieve optimal trade-off—validating channel ambiguity as the critical limiting factor for sensing performance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Agent Communication

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
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

The growing presence of unauthorized drones poses significant threats to public safety, underscoring the need for aerial surveillance solutions. This work proposes a cell-free integrated sensing and communication (ISAC) framework enabling drone detection within the existing communication network infrastructure, while maintaining communication services. The system exploits the spatial diversity and coordination of distributed access points (APs) in a cell-free massive MIMO architecture to detect aerial passive targets. To evaluate sensing performance, we introduce two key metrics: age of sensing (AoS), capturing the freshness of sensing information, and sensing coverage. The proposed AoS metric includes not only the transmission delays as in the existing models, but also the processing for sensing and networking delay, which are critical in dynamic environments like drone detection. We introduce an ambiguity parameter quantifying the similarity between the target-to-receiver channels for two hotspots and develop a novel network configuration strategy, including hotspot grouping, AP clustering, and sensing pilot assignment, leveraging simultaneous multi-point sensing to minimize AoS. Our results show that the best trade-off between AoS and sensing coverage is achieved when the number of hotspots sharing the same time/frequency resource matches the number of sensing pilots, indicating ambiguity as the primary factor limiting the sensing performance.

Problem

• Detects unauthorized drones using cell-free ISAC networks
• Minimizes age of sensing while ensuring coverage in detection
• Optimizes network configuration to balance sensing performance trade-offs

Innovation

• Cell-free ISAC framework for drone detection
• Novel Age of Sensing metric with processing delays
• Network configuration strategy minimizing sensing ambiguity