Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication

Journal of Marine Science and Engineering

Feng Zhou¹  Guang Yang¹  Gang Qiao¹  Yufei Liu¹  Xinyu Liu¹  Yinheng Lu¹  Yunjiang Zhao² 

[1] Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China;Yichang Testing Technique Research Institute, Yichang 443003, China;

关键词: cyclic shift keying spread spectrum;    low signal-to-noise ratio;    multipath effects;    neural network model;    long- and short-term memory;   

DOI  :  10.3390/jmse9111252

来源: DOAJ

【 摘 要 】

A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and short-term memory (LSTM) architecture-based neural network model as the receiving module of the system. The neural network is fed with the communication signals passing through known channel impulse responses in the offline stage, and then directly used to demodulate the received signal in the online stage to reduce the influence of the above factors. Numerical simulation and actual data results suggest that the deep learning-based CSK-SS UWA communication system is more reliable communication than a conventional system. In particular, the collected experimental data show that after preprocessing, when the communication rate is less than 180 bps, a bit error rate of less than 10⁻³ can be obtained at a signal-to-noise ratio of −8 dB.

【 授权许可】

Unknown