Activity Pattern Aware Spectrum Sensing: A CNN-Based Deep Learning Approach

In cognitive radio, most spectrum sensing algorithms are model-based and their detection performance relies heavily on the accuracy of the assumed statistical model. In this letter, we propose a convolutional neural network-based deep learning algorithm for spectrum sensing. Compared with model-based spectrum sensing algorithms, our proposed deep learning approach is data-driven and requires neither signal-noise probability model nor primary user (PU) activity pattern model. The proposed algorithm simultaneously takes in the present sensing data and historical sensing data, with which the inherent PU activity pattern can be learned to benefit the detection of PU activity. With extensive numerical simulations, results show that the proposed algorithm outperforms the estimator-correlator detector and the hidden Markov model-based detector in terms of correct detection probability.