Seek and Classify: End-to-end Joint Spectrum Segmentation and Classification for Multi-signal Wideband Spectrum Sensing

The rise in the use of wireless communication has led to the problem of spectrum scarcity in licensed bands. The popularity of the Internet of Things (IoT) requires innovative solutions that maximize the use of the available spectrum to support the increasing number of connected devices. The ability to detect and classify modulation of the signals efficiently can enable a cognitive radio to monitor the spectrum activity in real-time and utilize unused frequencies. In this work, Seek and Classify, an end-to-end framework for joint spectrum segmentation and classification for narrowband signals from wideband IQ samples, is developed. Seek and Classify includes a novel intersection of unions-based training methodology and machine learning architectures that advances this unique area. Evaluations performed through both synthetically generated radio signals and over-the-air experiments with software defined radios reveal that the proposed training strategy and models increase the classification accuracy from 41% to 99%. Moreover, the end-to-end framework reduces the sensing time for narrowband signals by 2-10 times, depending on hardware capabilities. The extensive evaluations provide guidance for the choice of training methods, machine learning architectures, and preprocessing tools for the most effective joint segmentation and classification performance.