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In this paper, we propose a data-driven framework for collaborative widebandspectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs),which act as the secondary users to opportunistically utilize detected spectrumholes. To this end, we propose a multi-class classification problem forwideband spectrum sensing to detect vacant spectrum spots based on collectedI/Q samples. To enhance the accuracy of the spectrum sensing module, theoutputs from the multi-class classification by each individual UAV are fused ata server in the unmanned aircraft system traffic management (UTM) ecosystem. Inthe spectrum scheduling phase, we leverage reinforcement learning (RL)solutions to dynamically allocate the detected spectrum holes to the secondaryusers (i.e., UAVs). To evaluate the proposed methods, we establish acomprehensive simulation framework that generates a near-realistic syntheticdataset using MATLAB LTE toolbox by incorporating base-station~(BS) locationsin a chosen area of interest, performing ray-tracing, and emulating the primaryusers channel usage in terms of I/Q samples. This evaluation methodologyprovides a flexible framework to generate large spectrum datasets that could beused for developing ML/AI-based spectrum management solutions for aerialdevices.

Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems