DeepSense: An Unsupervised Deep Clustering Approach for Cooperative Spectrum Sensing

Cognitive radio (CR) users can transmit data as vacant licensed bands become available. By using machine learning, CR users can intelligently sense channel activity and determine the availability of empty channels. Learning-based CR systems that use supervised learning for spectrum sensing require labeled training data. Furthermore, the majority of existing deep learning-based detectors are supervised, requiring a lot of labeled training data to achieve adequate performance. On the other hand, obtaining a large amount of labeled data in practical CR may be difficult. To address this gap, we propose DeepSense, which is an unsupervised cooperative sensing approach that uses representation learning by a sparse autoencoder (SAE) and unsupervised clustering by a Gaussian mixture model (GMM). DeepSense does not rely on cooperation among many SUs. Instead, it uses the learned representation to improve the detection performance, which significantly decreases the network's cooperation overhead. DeepSense does not require any prior knowledge, such as noise characteristics or channel state information, to operate. Furthermore, only a small amount of unlabeled data is needed for training. Extensive simulations have been conducted, which suggest that the proposed detector is able to learn hidden features in the sensing data that allows it to achieve an excellent detection performance. Moreover, our results show that DeepSense outperforms pure GMM, and attains comparable detection performance to benchmark deep supervised learning-based cooperative sensing.