Unsupervised Deep Spectrum Sensing: A Variational Auto-Encoder Based Approach

In cognitive radio (CR), the test statistics of most spectrum sensing algorithms are generated from the model-based features such as the signal energy and the eigenvalues from the sample covariance matrix (CM). Despite their low complexity, their detection performance depends very much on the accuracy of the presumed model. Also, these model-based statistics may not be able to exploit the full potential of the signal samples. To this end, the data-driven deep learning-based detectors have been proposed, with test statistics generated directly from signal samples in an automatic manner. However, existing deep learning-based detectors are all supervised learning-based and they usually require a massive amount of labeled training data to achieve decent detection performance. In practical CR scenarios, however, obtaining a large amount of labeled training data may be difficult. To address this issue, in this paper, we propose an unsupervised deep learning based spectrum sensing method named unsupervised deep spectrum sensing (UDSS). The UDSS algorithm requires no prior information such as the noise power or the signal's statistical CM. Moreover, the UDSS only requires a small amount of samples collected in absence of the primary user's (PU) signals (H-0 labeled data). Simulation results show that the proposed UDSS algorithm is able to approach the performance of the benchmark deep supervised learning-based spectrum sensing algorithm and outperforms the model-based benchmark algorithms under both Gaussian noise and Laplace noise.