  In recent years, much work has been done on processing of wireless spectrum
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. However, spectrum data measured in real-world
environment is highly nondeterministic, making its labeling a laborious and
expensive process, requiring domain expertise, thus being one of the main
drawbacks of using supervised learning approaches in this domain. In this
paper, we investigate the use of self-supervised learning (SSL) for exploring
spectrum activities in a real-world unlabeled data. In particular, we compare
the performance of two SSL models, one based on a reference DeepCluster
architecture and one adapted for spectrum activity identification and
clustering, and a baseline model based on K-means clustering algorithm. We show
that SSL models achieve superior performance regarding the quality of extracted
features and clustering performance. With SSL models we achieve reduction of
the feature vectors size by two orders of magnitude, while improving the
performance by a factor of 2 to 2.5 across the evaluation metrics, supported by
visual assessment. Additionally we show that adaptation of the reference SSL
architecture to the domain data provides reduction of model complexity by one
order of magnitude, while preserving or even improving the clustering
performance.
