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

Zinat Behdad, Ozan Alp Topal, Cicek Cavdar

TL;DR

This work addresses drone detection within existing networks using a cell-free ISAC framework. It introduces Age of Sensing (AoS) as a key timeliness metric, a sensing-coverage measure, and a novel network configuration strategy (hotspot grouping and sensing-pilot assignment) to minimize AoS while preserving coverage. A MAPRT-based detector for target presence is used in a multi-static sensing setup, and AoS is modeled with transmission, processing, and networking delays via GOP-based computations. Results show that grouping hotspots to reduce observation periods, while constraining hotspot ambiguity, yields the best AoS–coverage trade-off, enabling rapid, wide-area aerial surveillance with limited pilots.

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.

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

TL;DR

This work addresses drone detection within existing networks using a cell-free ISAC framework. It introduces Age of Sensing (AoS) as a key timeliness metric, a sensing-coverage measure, and a novel network configuration strategy (hotspot grouping and sensing-pilot assignment) to minimize AoS while preserving coverage. A MAPRT-based detector for target presence is used in a multi-static sensing setup, and AoS is modeled with transmission, processing, and networking delays via GOP-based computations. Results show that grouping hotspots to reduce observation periods, while constraining hotspot ambiguity, yields the best AoS–coverage trade-off, enabling rapid, wide-area aerial surveillance with limited pilots.

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.

Paper Structure

This paper contains 9 sections, 22 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Configuration for Aerial Surveillance
  4. Hotspot Grouping
  5. Sensing Pilot Assignment
  6. Target Detection
  7. Age of Sensing (AoS)
  8. Numerical Results
  9. Conclusion

Figures (5)

  • Figure 1: Aerial surveillance system with cell-free ISAC
  • Figure 2: A helicopter view of the simulation scenario.
  • Figure 3: Sensing coverage vs. communication SE per UE.
  • Figure 4: Age of Sensing vs number of hotspot groups.
  • Figure 5: Heatmap sensing coverage for a) $P_s=0.3$ and $N_a=20$, b)$P_s=0.7$ and $N_a=10$, and c)$P_s=1$ and $N_a=5$.