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.

翻译：未经授权无人机的日益增多对公共安全构成重大威胁，凸显了对空中监测解决方案的需求。本研究提出一种无蜂窝集成感知与通信（ISAC）框架，可在现有通信网络基础设施内实现无人机检测，同时维持通信服务。该系统利用无蜂窝大规模MIMO架构中分布式接入点（AP）的空间分集与协同能力，以探测空中被动目标。为评估感知性能，我们引入两个关键指标：感知时效性（AoS），用于捕捉感知信息的新鲜度；以及感知覆盖范围。所提出的AoS指标不仅包含现有模型中的传输延迟，还纳入了感知处理与网络延迟，这在无人机检测等动态环境中至关重要。我们引入了一个模糊度参数，用于量化两个热点区域之间目标-接收器信道的相似性，并提出一种新颖的网络配置策略，包括热点分组、AP聚类和感知导频分配，通过利用多点同步感知来最小化AoS。实验结果表明，当共享相同时频资源的热点数量与感知导频数量相匹配时，AoS与感知覆盖范围达到最佳平衡，这表明模糊度是限制感知性能的主要因素。