Reliable and Secure Deep Learning-Based OFDM-DCSK Transceiver Design Without Delivery of Reference Chaotic Sequences

Abstract

Chaos-based communications can be applied to high-speed vehicular information transmissions thanks to the anti-jamming and anti-interference capabilities of chaotic transmissions. In traditional practical chaotic systems, reference chaotic signals are required to be delivered to remove complex chaotic synchronization circuits. However, the direct transmission of reference signals will degrade the security performances, while interferences and noises imposed on the reference signals due to imperfect channel conditions will deteriorate the reliability performances. In order to enhance the reliability and security performances over vehicular channels such as the railway channel and the channels undergoing fast fadings, in this paper, we propose a deep learning (DL) aided intelligent OFDM-DCSK transceiver. In this design, no reference chaotic signals are delivered, and we propose to utilize the time-delay neural network (TDNN) to learn the chaotic maps, followed by the long short-term memory (LSTM) units to extract and exploit the correlations between chaotic modulated signals, and multiple fully connected layers (FCLs) to estimate the user bit data. With the aid of the constructed deep neural network (DNN), after the offline neural network training, the receiver can recover the transmitted information with lower bit error rate (BER) and enhance security performances. Theoretical performance is then analyzed for the proposed intelligent transceiver. Simulation results validate the proposed design, and demonstrate that the intelligent DL-based OFDM-DCSK system can achieve better BER and security performances over fast fading and railway channels compared with the benchmark systems.