Self-supervised Learning for Clustering of Wireless Spectrum Activity

In recent years, much work has been done on processing of wireless spectral data involving machine learning techniques in domain-related problems for cognitive radio networks, such as anomaly detection, modulation classification, technology classification and device fingerprinting. Most of the solutions are based on labeled data, created in a controlled manner and processed with supervised learning approaches. Labeling spectral data is a laborious and expensive process, being one of the main drawbacks of using supervised approaches. In this paper, we introduce self-supervised learning for exploring spectral activities using real-world, unlabeled data. We show that the proposed model achieves superior performance regarding the quality of extracted features and clustering performance. We achieve reduction of the feature vectors size by 2 orders of magnitude (from 3601 to 20), while improving performance by 2 to 2.5 times across the evaluation metrics, supported by visual assessment. Using 15 days of continuous narrowband spectrum sensing data, we found that 17% of the spectrogram slices contain no or very weak transmissions, 36% contain mostly IEEE 802.15.4, 26% contain coexisting IEEE 802.15.4 with LoRA and proprietary activity, 12% contain LoRA with variable background noise and 9% contain only dotted activity, representing LoRA and proprietary transmissions.