The Citizens Broadband Radio Service (CBRS) band is pivotal for expanding next-generation wireless services, but its success hinges on robustly protecting incumbent users, such as naval radar systems, from interference. This task is delegated to a network of Environmental Sensing Capability (ESC) sensors, which must detect faint radar signals amidst heavy co-channel interference from commercial LTE and 5G users. Traditional centralized detection models raise significant data privacy concerns and are ill-suited for the Non-Independent and Identically Distributed (non-IID) nature of data from geographically dispersed sensors. To overcome these limitations, we propose a novel Federated Learning (FL) framework PERFECT that leverages ESC level personalization for robust and efficient radar detection. PERFECT preserves privacy by training models locally on ESC sensors. Furthermore, our framework is the first to effectively handle non-IID scenarios through model personalization where different ESCs observe distinct radar types. We demonstrate through extensive simulations that PERFECT achieves the mandated 99% recall for radar detection, matching centralized performance while significantly enhancing privacy, efficiency, and scalability for dynamic spectrum sharing.

翻译：公民宽带无线服务（CBRS）频段对于扩展下一代无线服务至关重要，但其成功依赖于有效保护现有用户（如海军雷达系统）免受干扰。这项任务由环境感知能力（ESC）传感器网络执行，这些传感器需在商用LTE和5G用户的强同频干扰中检测微弱的雷达信号。传统的集中式检测模型存在严重的数据隐私问题，且不适用于地理分散传感器数据的非独立同分布（non-IID）特性。为克服这些限制，我们提出了一种新颖的联邦学习（FL）框架PERFECT，该框架利用ESC级别的个性化实现稳健高效的雷达检测。PERFECT通过在ESC传感器本地训练模型来保护隐私。此外，我们的框架首次通过模型个性化有效处理了non-IID场景——不同ESC观测到不同雷达类型。通过大量仿真，我们证明PERFECT能够达到雷达检测要求的99%召回率，在匹配集中式性能的同时显著提升了动态频谱共享的隐私性、效率与可扩展性。