Deep Learning Based Reliable and Intelligent Chaotic OFDM Communications for Cognitive Radio System

In this paper, we present a deep learning based modulation scheme for chaotic orthogonal frequency division multiplex (OFDM) transmissions over non-contiguous frequency bands of cognitive radio systems. In cognitive radio systems, the users access the spectrum bands dynamically and the corresponding channel characteristics also changes. Different from the traditional modulation scheme that uses the fixed mapping pattern to modulate the signals, we propose to apply the deep learning method to build up the constellations intelligently. Based on the autoencoder architecture of deep learning, we construct the constellation mapping and demapping patterns adaptively with the aim to minimize the bit error rate (BER) over the dynamically changing non-contiguous channels. Simulation results over additive white Gaussian noise (AWGN) channel and Rayleigh fading channel show that our proposed system achieves better BER performances for legitimate receivers when compared with the conventional modulation schemes. In addition, the presented scheme remains the high security performance thanks to the usage of the chaotic sequences.