Detecting Unauthorized Drones with Cell-Free Integrated Sensing and Communication

2025-01-25

AI Summary

In cell-free massive MIMO systems, jointly ensuring passive detection of unauthorized UAVs and communication service quality remains challenging. Method: This paper proposes an integrated sensing and communication (ISAC) framework, introducing for the first time the metrics of Age of Sensing (AoS) and sensing coverage to formulate a joint optimization model of sensing blocklength and power allocation. An adaptive weighting balancing algorithm is designed to overcome traditional ISAC performance trade-off limitations. Distributed signal detection and concave–convex procedure (CCP)-based optimization enable low-overhead real-time sensing. Results: Compared with fixed-weight schemes, the proposed method reduces AoS by 45%, significantly improving sensing timeliness and coverage. Furthermore, it quantitatively reveals the inhibitory effect of increasing communication demands on sensing performance, providing both theoretical foundations and practical guidance for ISAC resource co-optimization.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

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

Integrated sensing and communication (ISAC) boosts network efficiency by using existing resources for diverse sensing applications. In this work, we propose a cell-free massive MIMO (multiple-input multiple-output)-ISAC framework to detect unauthorized drones while simultaneously ensuring communication requirements. We develop a detector to identify passive aerial targets by analyzing signals from distributed access points (APs). In addition to the precision of the sensing, timeliness of the sensing information is also crucial due to the risk of drones leaving the area before the sensing procedure is finished. We introduce the age of sensing (AoS) and sensing coverage as our sensing performance metrics and propose a joint sensing blocklength and power optimization algorithm to minimize AoS and maximize sensing coverage while meeting communication requirements. Moreover, we propose an adaptive weight selection algorithm based on concave-convex procedure to balance the inherent trade-off between AoS and sensing coverage. Our numerical results show that increasing the communication requirements would significantly reduce both the sensing coverage and the timeliness of the sensing. Furthermore, the proposed adaptive weight selection algorithm can provide high sensing coverage and reduce the AoS by 45% compared to the fixed weights, demonstrating efficient utilization of both power and sensing blocklength.

Problem

• Unmanned Aerial Vehicle Detection
• Signal Tower Independence
• Communication Service Quality

Innovation

• Massive MIMO-ISAC
• Optimized Perception Metrics
• Efficient Signal and Energy Utilization