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.

翻译：本文提出了一种数据驱动的框架，用于联网无人机（UAV）的协作宽带频谱感知与调度，其中无人机作为次要用户，伺机利用检测到的频谱空洞。为此，我们提出一个多分类问题，基于采集的I/Q样本实现宽带频谱感知，以检测空闲频谱位置。为提升频谱感知模块的准确性，各无人机多分类输出结果在无人航空系统交通管理（UTM）生态系统的服务器上进行融合。在频谱调度阶段，我们采用强化学习（RL）方案，动态地将检测到的频谱空洞分配给次要用户（即无人机）。为评估所提方法，我们构建了一个综合仿真框架，利用MATLAB LTE工具箱，通过整合选定感兴趣区域内的基站（BS）位置、执行射线追踪、并以I/Q样本形式模拟主用户信道占用情况，生成近乎真实的合成数据集。该评估方法提供了一个灵活框架，可用于生成大规模频谱数据集，以开发基于ML/AI的航空设备频谱管理解决方案。