DEAP Learning: A Data-Driven Approach to Unsupervised Cooperative Spectrum Sensing

In this paper, we present DEAP learning, an unsupervised approach for cooperative spectrum sensing. DEAP learning uses a sparse autoencoder (SAE) to learn a useful representation of the sensing data, followed by unsupervised clustering using affinity propagation (AP) algorithm for identifying primary user activity. Our suggested approach does not require prior information about the channel characteristics or the sensing data for training. Moreover, in contrast to other unsupervised clustering methods, the performance of AP is not dependent on cluster centroids initialization. DEAP learning leverages a few cooperating secondary users to minimize cooperation costs while achieving a high detection performance. Our numerical results suggest that our proposed sensing approach achieves comparable performance to supervised and state-of-the-art unsupervised deep learning-based sensing, without requiring a substantial amount of training data. To demonstrate the merits of DEAP learning, extensive simulations have been conducted under a wide range of primary and secondary network settings. Moreover, we evaluate DEAP learning while considering communication channel impairments. Overall, our DEAP learning approach illustrates the potential for robust performance in cooperative spectrum sensing.