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.

翻译：本文提出了一种面向网络化无人机（作为次级用户）的数据驱动协同宽带频谱感知与调度框架，以机会式利用检测到的"频谱空洞"。我们的整体框架包含三个主要阶段。首先，在模型训练阶段，我们探索多小区环境下的数据集生成，并采用联邦学习架构训练机器学习模型。与现有无线联邦学习研究假定训练数据集立即可用不同，我们提出了一种新颖架构，将无线数据集生成（涉及在多小区环境中从空中信号捕获I/Q样本）直接集成到联邦学习训练流程中。其次，在协同频谱推理阶段，我们提出了一种兼容无人机系统交通管理生态系统的协同频谱融合策略。最后，在频谱调度阶段，我们利用强化学习解决方案动态地将检测到的频谱空洞分配给次级用户。为评估所提方法，我们建立了综合仿真框架，通过整合目标区域的基站位置、执行射线追踪以及基于I/Q样本仿真主用户信道使用情况，利用MATLAB LTE工具箱生成接近真实的合成数据集。该评估方法提供了可生成大规模频谱数据集的灵活框架，可用于开发面向空中设备的基于机器学习/人工智能的频谱管理解决方案。