  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 (SUs) to opportunistically utilize detected
"spectrum holes". Our overall framework consists of three main stages. Firstly,
in the model training stage, we explore dataset generation in a multi-cell
environment and training a machine learning (ML) model using the federated
learning (FL) architecture. Unlike the existing studies on FL for wireless that
presume datasets are readily available for training, we propose a novel
architecture that directly integrates wireless dataset generation, which
involves capturing I/Q samples from over-the-air signals in a multi-cell
environment, into the FL training process. Secondly, in the collaborative
spectrum inference stage, we propose a collaborative spectrum fusion strategy
that is compatible with the unmanned aircraft system traffic management (UTM)
ecosystem. Finally, in the spectrum scheduling stage, we leverage reinforcement
learning (RL) solutions to dynamically allocate the detected spectrum holes to
the secondary users. 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.
