# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
###### Abstract  
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. cyclic shift keying spread spectrum; low signal-to-noise ratio; multipath effects; neural network model; long- and short-term memory +
Footnote †: journal: Marine Science and Engineering  
0000-0001-939-7880]Y. [PERSON]  
0000-0002-2891-7880]F. [PERSON]  
0000-0002-333-3333-3333-3333-3333-3333-3333-3333-3333-3333-3333-33333-33333-33333-33333-33333-33333-33333-333333-33  
have been widely used in natural language processing [9; 10; 11]. In addition, DL is also good at discovering intricate structures in high-dimensional data, so it has gradually become widely used in acoustic signal processing and other fields [12; 13; 14; 15]. At present, DL has been successfully applied in the field of communication, such as in orthogonal frequency division multiplexing (OFDM) communication systems. [PERSON] et al. proposed a channel estimation and symbol detection method using a deep neural network in the OFDM communication system [16]. They pointed out that in OFDM wireless communication with complex channel distortion and interference, the DL-based method can solve the problem of channel distortion and detect transmission symbols. [PERSON] et al. proposed a model-driven DL method, which combines DL with expert knowledge to replace the existing wireless OFDM communication receiver [17]. They explained that a bidirectional LSTM (BiLSTM) recurrent neural network could utilize the internal relationship of intersymbol interference (ISI) between sequence data. [PERSON] et al. proposed a DL-based OFDM UWA communication system, which replaces the receiving module in the conventional OFDM UWA communication system with the deep neural network (DNN) [18]. They pointed out that after the neural network has been sufficiently trained, the transmitted symbols can be recovered directly through the neural network. There is no need to use explicit channel estimation and equalization as in conventional UWA communication. However, there are few applications of DL in spread spectrum UWA communication. [PERSON] et al. proposed an autonomous underwater vehicles communication scheme based on DL coding index modulation spread spectrum (CIM-SS) [19]. In this system, the DNN model is used as the de-mapper to demodulate the baseband signal, avoids the deterioration in the performance of CIM-SS over long tap delay UWA channel, and improves the system data rate as well as the energy efficiency and the bit error rate (BER) performance. For the spread spectrum communication system, the spreading gain will severely affect the communication rate of the DSSS UWA communication system. However, the CSK-SS UWA communication system uses a circular cyclic shift of the spreading sequence to carry the information, which breaks the limitation of spreading gain on data rate [4].