DeepRadar: A Deep-Learning-based Environmental Sensing Capability Sensor Design for CBRS

We present DeepRadar, a novel deep-learning-based environmental sensing capability system for detecting radar signals and estimating their spectral occupancy. DeepRadar makes decisions in real-time and maintains continuous operability by adapting its computations based on the available computing resources. We thoroughly evaluate DeepRadar using a variety of test data at different signalto- interference ratio (SIR) levels. Our evaluation results show that at 20 dB peak-to-average SIR, per MHz, DeepRadar detects radar signals with 99% accuracy and misses only less than 2 MHz, on average, while estimating their spectral occupancy. Our implementation of DeepRadar using a commercial-off-the-shelf software-defined radio also achieves a similarly high detection accuracy.