Joint Detection and Classification of RF Signals Using Deep Learning
Adela Vagollari, Viktoria Schram, Wayan Wicke, Martin Hirschbeck, Wolfgang Gerstacker
Abstract
With the rapid expansion of wireless technologies, monitoring and regulating the Radio Frequency (RF) spectrum usage becomes more important than ever. In this paper, we present a Deep Learning (DL) based approach to analyze the RF spectrum by detecting, localizing, and classifying active signals in RF frequency bands. We represent the radio signals in wideband spectrograms and formulate the signal detection and classification problem as an object detection task related to the computer vision field. To this end, You Only Look Once (YOLO), a state-of-the-art object detector, is adapted and optimized to detect, localize, and classify signals in spectrograms. For the experimental evaluation of YOLO as a signal detector, a rich dataset was simulated, consisting of diverse signals modulated with digital and analog modulation schemes and transmitted over channels with realistic propagation conditions. Our proposed method achieves an Average Precision (AP) of almost 87% and an average Intersection over Union (IoU) of 90%, thus demonstrating significant potential for analyzing RF spectral activity with high accuracy.