Deep Learning-Based Code Index Modulation Spread Spectrum Underwater Acoustic Communication

Abstract

This paper proposes a deep learning-based code index modulation-spread spectrum (CIM-SS) underwater acoustic (UWA) communication system. The system is characterized by the variant model of the recurrent neural network at the receiver of the communication system, which can directly demodulate the received signal after the synchronization without de-carrier and de-spreading operation. To verify the performance of the deep learning-based CIM-SS UWA communication system, the channel impulse response will be used to simulate the UWA channel. The signals passing through the UWA channel will be used for offline training and online testing of the neural network model. The bit error rate performance of the system with different model structures under different signal-to-noise ratio (SNR) conditions is compared. The simulation results show that the deep learning-based system can achieve better performance than the conventional system under the conditions of low SNR and severe UWA channels.