Sensing the living airspace: city-wide bird flight patterns revealed by communication networks

Monitoring low-altitude bird movement is crucial for ecological research and aviation safety, yet a critical data gap exists due to the limitations of conventional radar and sparse sensor coverage in urban environments. Here, we demonstrate that existing cellular communication networks can be repurposed as a dense, city-scale biosensor network to fill this gap. We deploy a sub-6 GHz integrated sensing and communication (ISAC) system using two base stations (BSs), featuring a novel frame design that reserves only 1.4% of resources for sensing, thus maintaining uninterrupted communication services. To extract the weak micro-Doppler signal of birds from heavy urban interference, we develop a two-stage signal processing pipeline that recovers their characteristic micro-Doppler signatures. Using this system, we collect and analyze a large-scale dataset of \(\:110\hspace{0.25em}GB\) from hundreds of free-flying pigeons. Analysis of these signals first reveals significant variability in the time-frequency domain across individuals, a direct result of differences in their body size, flight posture, and angle relative to the BS. Despite this individual-level diversity, we extract a unifying biomechanical signature that represents a population-level commonality, providing a new layer of ecological information: a stable and consistent wingbeat frequency distribution for pigeons, with a mean of \(\:6.42\hspace{0.25em}Hz\) and a variance of \(\:1.55\hspace{0.25em}H{z}^{2}\). We show that this signature serves as a robust feature to distinguish birds from other aerial targets, such as unmanned aerial vehicles (UAVs), and we integrate it into a multi-station framework to map continuous, city-scale flight trajectories. Our work provides a practical pathway for transforming ubiquitous cellular infrastructure into a global tool for large-scale ecological monitoring and the mitigation of human-wildlife conflicts.