In the United States, the Federal Communications Commission has adopted rules
permitting commercial wireless networks to share spectrum with federal
incumbents in the 3.5~GHz Citizens Broadband Radio Service band. These rules
require commercial systems to vacate the band when sensors detect radars
operated by the U.S. military; a key example being the SPN-43 air traffic
control radar. Such sensors require highly-accurate detection algorithms to
meet their operating requirements. In this paper, using a library of over
14,000 3.5~GHz band spectrograms collected by a recent measurement campaign, we
investigate the performance of thirteen methods for SPN-43 radar detection.
Namely, we compare classical methods from signal detection theory and machine
learning to several deep learning architectures. We demonstrate that machine
learning algorithms appreciably outperform classical signal detection methods.
Specifically, we find that a three-layer convolutional neural network offers a
superior tradeoff between accuracy and computational complexity. Last, we apply
this three-layer network to generate descriptive statistics for the full
3.5~GHz spectrogram library. Our findings highlight potential weaknesses of
classical methods and strengths of modern machine learning algorithms for radar
detection in the 3.5~GHz band.

本文研究了在 3.5 GHz 频段内使用 13 种不同的算法（包括信号检测理论和机器学习）以及深度学习架构进行 SPN-43 雷达检测的性能表现，并发现机器学习算法优于传统信号检测方法。