Intelligent Spectrum Sensing: An Unsupervised Learning Approach Based on Dimensionality Reduction

In Cognitive radio (CR), users take advantage of vacant licensed bands to transmit their data as they become available. Cognitive users employ an autonomous perception-action decision cycle that starts with sensing the activity of licensed users. By using machine learning techniques, CR users can attain their full cognitive potential and smartly detect empty frequency bands. Learning-based CR systems that utilize supervised learning for spectrum sensing require labeled data for model training. Having readily accessible labeled data is a challenging task for CR networks, since it necessitates cooperation between licensed and unlicensed users. In interweave CR networks, such cooperation is not feasible and imposes a significant communication overhead. Motivated by the above, we address the practical limitation of labeled data scarcity in learning-based CR networks by designing a novel unsupervised learning framework for cooperative spectrum sensing based on a Gaussian mixture model (GMM) and principal component analysis (PCA) that uses a small amount of unlabeled data for training and requires no prior knowledge of the radio environment. The system is mathematically simulated, and its performance is evaluated based on various detection performance metrics. According to our findings, our proposed approach outperforms the GMM algorithm and is on par with supervised learning algorithms such as SVM, RF, and DT. Furthermore, the proposed approach is shown to be robust to low SNRs.