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.

我们在本论文中提出了一种数据驱动的框架，用于协同宽带频谱感知和调度网络化的无人机，其充当二级用户来机会性地利用检测到的"频谱空洞"。我们的框架包括三个主要阶段：模型训练阶段，协同频谱推断阶段以及频谱调度阶段。我们还提出了一个多联邦学习架构，将无线数据集生成直接集成到联邦学习训练过程中。我们利用强化学习解决方案动态分配检测到的频谱空洞给二级用户。通过建立综合的仿真框架来评估所提出的方法，我们能够生成近乎真实的合成数据集，用于发展基于机器学习/人工智能的航空设备频谱管理解决方案。

UTM系统中基于联邦学习的航空无人机协作宽带频谱感知和调度