Deep learning based primary user classification in Cognitive Radios

Deep Belief Networks (DBN) is a very powerful algorithm in deep learning. The DBN has been effectively applied in many areas of machine learning, such as computer vision (CV) and natural language processing (NLP). With the help of deep architecture, their accuracy has been largely improved and their human annotation data which traditional machine learning algorithm extremely rely on could be reduced. In Cognitive Radios (CRs), learning is necessary for its cognition, while two of the key challenges are how to classify primary user agents with their performances and predict their behaviors. The CRs' performance has a positive correlation with the hit rate of learning algorithm's classification and prediction results. In this paper, we study the questions of classification and prediction of user agents. We apply the DBN model to improve accuracy rating of user agent's recognition in CRs with user-centered model, it's the first application of deep learning structure in CRs. The DBN model provides a primary user agent's classification, which is the foundation of the prediction to both idle frequency spectrums and time slots. Experimental results show that the cognitive engine finds a much better detection rate than the CRs engine with shallow learning and other traditional strategy. The simulation results are also tested on the WIFI channel with 5GHz and 2.4GHz.