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

Signal Processing;Machine Learning;Multiagent Systems;Networking and Internet Architecture