# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
###### Abstract
In the process of demodulation, the conventional CSK-SS system performs correlation processing on the received baseband signal and the local cyclic shifted spreading sequence during the demodulation process and selects the maximum correlation value for decision. The position of the peak value is the information modulated on the code phase to realize the recovery of the source information. However, it needs to be pointed out that the improvement of data rate comes at the expense of destroying the partial autocorrelation function (PACF) characteristics of the sequence in the DSSS system [20]. Due to the influence of the noise and UWA multipath fading channel, the size of the correlation peak in the demodulation process of the CSK-SS UWA communication system changes, resulting in inaccurate decision results, making demodulation results wrong and affecting the performance of the CSK-SS system. Inspired by the application of DL-based methods in the field of acoustic signal processing [16; 17], in this paper, a DL-based CSK-SS UWA communication system is proposed, which innovatively applies DL to complete the demodulation of CSK-SS UWA communication signals and obtain several times higher communication rates than DSSS. More importantly, it avoids the performance limitations of conventional CSK-SS systems due to UWA multipath channels and noise environments. Furthermore, compared with the conventional system, this system can directly demodulate the received signal at the receiving end without completing the de-carrier and despreading operation of the received signal, which simplifies the processing flow of the received signal to a certain extent. By sufficiently training the LSTM architecture-based neural network model in the offline stage, a large number of random data samples modulated by CSK-SS and a large number of channel impulse responses (CIRs) generated under the ray acoustics model are used as the training datasets of the network, which gives the trained model the ability to remember and analyze CSK-SS time domain signals affected by noise and multipath fading. This paper is aimed at the application scenario of signal transmission and command control between fixed nodes of the underwater communication network [21]. It is based on the actual measured sound speed profile (SSP), adjusts the horizontal distance and vertical depth of transceiver position for a specific sea area, and uses BELLHOP [22] to generate multiple groups CIRs through spatial position change. It provides a prerequisite guarantee for the sufficient training of LSTM architecture-based neural network models. Therefore, when the trained model is deployed online in a specific sea area practical application scenario, by inputting the time domain waveform of the unknown random CSK-SS signal affected by multipath fading into the neural network model, the LSTM architecture-based neural network completes the information processing through the internal LSTM cell according to the input received signal time sequence data. Lastly, the classifier completes the output of its category to realize the demodulation of the signal. The main contribution of this paper is to propose a new DL-based CSK-SS UWA communication system for the application scenario of underwater fixed node acoustic communication. Taking the LSTM architecture-based neural network model as the receiving module of the system, the CSK-SS communication system can overcome the influence of low signal-to-noise ratio (SNR) and complex shallow water acoustic channels. Meanwhile, the system uses a shorter spreading sequence and allowing each spreading sequence to carry multiple bits. While increasing the communication rate of the system, it avoids the degradation of the CSK-SS system performance under the influence of complex multipath fading. In addition, the robustness of the DL-based system is evaluated to analyze the impact on the system performance when the change of marine environment causes the sample mismatch in the training and test stages.