Spec-SCAN: Spectrum Learning in Shared Channel using Neural Networks

The capability to detect radar signals autonomously, without reliance on radar transmitters, is pivotal for the advancement of contemporary shared-spectrum wireless networks like the Citizens Broadband Radio Service (CBRS). Recent trends underscore the integration of AI-driven methodologies to address this challenge effectively. In this paper, we present a novel supervised deep learning framework for radar detection, denoted as Spec-SCAN. We design Spec-SCAN to efficiently identify low-power radar signals amidst interference within a condensed timeframe and over a narrower frequency spectrum compared to existing benchmarks. Our approach employs a YOLO-based training strategy tailored for the detection of radar signals and prevalent interference patterns within the CBRS band. We perform rigorous experiments encompassing scenarios involving LTE, 5G, and DSSS signals as interfering signals to evaluate Spec-SCAN. Our findings indicate that Spec-SCAN attains a radar detection recall of 99% for type 1 radar signals, even at Signal-to-Interference-Noise Ratios (SINR) as low as 15 dB, while scanning a 100MHz spectrum within a 15ms timeframe-demonstrating superior performance compared to alternative methodologies. Spec-SCAN framework also offers comparable performance while scanning 50MHz spectrum for 15 milliseconds.