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Detecting Unauthorized Drones with Cell-Free Integrated Sensing and Communication

Xinyue Li, Zinat Behdad, Ozan Alp Topal, Ozlem Tugfe Demir, Cicek Cavdar

TL;DR

This work addresses detecting unauthorized drones in a cell-free massive MIMO ISAC network while satisfying downlink communication requirements. It jointly optimizes sensing blocklength and transmit power to maximize sensing coverage and minimize Age of Sensing, introducing a CCP-based adaptive weight-selection method to balance precision and timeliness. A MAPR detector is employed for multi-static aerial target detection, and AoS together with sensing coverage serve as core performance metrics. Numerical results show the adaptive weighting approach can reduce AoS by up to 45% while achieving over 98% sensing coverage, demonstrating efficient resource utilization and real-time aerial threat detection capability.

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.

Detecting Unauthorized Drones with Cell-Free Integrated Sensing and Communication

TL;DR

This work addresses detecting unauthorized drones in a cell-free massive MIMO ISAC network while satisfying downlink communication requirements. It jointly optimizes sensing blocklength and transmit power to maximize sensing coverage and minimize Age of Sensing, introducing a CCP-based adaptive weight-selection method to balance precision and timeliness. A MAPR detector is employed for multi-static aerial target detection, and AoS together with sensing coverage serve as core performance metrics. Numerical results show the adaptive weighting approach can reduce AoS by up to 45% while achieving over 98% sensing coverage, demonstrating efficient resource utilization and real-time aerial threat detection capability.

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.
Paper Structure (11 sections, 25 equations, 3 figures, 1 table, 1 algorithm)

This paper contains 11 sections, 25 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Signal Model and Downlink Communication
  4. Target Detection
  5. Drone Detection with ISAC
  6. Age of Sensing
  7. Sensing Coverage
  8. Multi-Objective Optimization Problem
  9. Adaptive Weight Selection Algorithm
  10. Numerical Analysis
  11. Conclusion

Figures (3)

  • Figure 1: Illustration of the ISAC system setup.
  • Figure 2: Locations of sensing points, TX APs and RX APs for the considered setup.
  • Figure 3: (a) Sensing coverage vs. total sensing time for $\gamma_c=5,10,15$ dB and $P_{\rm th}=0.9$, (b) sensing coverage vs. target altitude for $P_{\rm th}=0.8,0.9, 0.99$, without adaptive weight selection algorithm and (c) minimum sensing blocklength with adaptive weight selection and $P_{\rm th}\!=0.9$.