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In the United States, the Federal Communications Commission has adopted rulespermitting commercial wireless networks to share spectrum with federalincumbents in the 3.5~GHz Citizens Broadband Radio Service band. These rulesrequire commercial systems to vacate the band when sensors detect radarsoperated by the U.S. military; a key example being the SPN-43 air trafficcontrol radar. Such sensors require highly-accurate detection algorithms tomeet their operating requirements. In this paper, using a library of over14,000 3.5~GHz band spectrograms collected by a recent measurement campaign, weinvestigate the performance of thirteen methods for SPN-43 radar detection.Namely, we compare classical methods from signal detection theory and machinelearning to several deep learning architectures. We demonstrate that machinelearning algorithms appreciably outperform classical signal detection methods.Specifically, we find that a three-layer convolutional neural network offers asuperior tradeoff between accuracy and computational complexity. Last, we applythis three-layer network to generate descriptive statistics for the full3.5~GHz spectrogram library. Our findings highlight potential weaknesses ofclassical methods and strengths of modern machine learning algorithms for radardetection in the 3.5~GHz band.

Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing