Detecting Unauthorized Drones with Cell-Free Integrated Sensing and Communication

TL;DR

This paper introduces a cell-free massive MIMO-ISAC framework for detecting unauthorized drones, optimizing sensing and communication trade-offs to improve timeliness and coverage.

Contribution

It proposes a novel joint sensing blocklength and power optimization algorithm along with an adaptive weight selection method for enhanced drone detection performance.

Findings

Increasing communication requirements reduces sensing coverage and timeliness.

The adaptive weight algorithm reduces AoS by 45% compared to fixed weights.

The framework effectively balances sensing accuracy and communication needs.

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.