# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 5 Conclusions and Prospect  
In this paper, the neural network model is used as the receiver structure of the DL-based CSK-SS UWA communication system to demodulate the signal. The neural network model is trained based on the training samples with distortion caused by the influence of UWA channels and noise in the offline stage.  
The simulation results show that the DL-based CSK-SS UWA communication system has better reliability than the conventional system in the complex shallow water acoustic channels with low SNR. The neural network model-based receiver module will reduce the impact of the multipath effect on the performance of the conventional CSK-SS system in fading channel. Furthermore, the neural network model has good generalization ability. When the online deployment conditions do not precisely agree with the offline training conditions, the neural network model can still work effectively to a certain extent. In other words, the model can analyze and memorize the complex characteristics of the UWA channel. In addition, the focusing gain brought by VTRM will introduce new beneficial features to the neural network, which will bring more reliable performance to this system.  
The performance of the DL-based CSK-SS UWA communication system is verified by a water tank experiment. The experimental results show that the performance of the DL-based system is improved compared with the conventional system. Especially for the DL-based system after channel equalization preprocessing, when SNR is greater than \(-8\) dB, the BER is less than \(2.5\times 10^{-3}\). It should be pointed out that the BER of the communication system can be further reduced with appropriate coding under the condition of low BER. In addition, some suggestions are put forward for the problems that scholars may encounter in future experiments.  
In the future, we can also consider using convolutional neural network, combined with transfer learning, few-shot learning, and other technologies to expand the application scope of the system further and reduce the cost and complexity of network training. Moreover, further exploration of the application of the system in practical engineering will be considered.  
Conceptualization, Y.L. ([PERSON]) and F.Z.; methodology, Y.L. ([PERSON]) and Y.Z.; software, Y.L. ([PERSON]); validation, Y.L. ([PERSON]), Y.Z., G.Y., X.L. and Y.L. ([PERSON]); formal analysis, Y.L. ([PERSON]) and F.Z.; investigation, G.Q.; resources, G.Q. and F.Z.; data curation, G.Y., X.L. and Y.L. ([PERSON]); writing--original draft preparation, Y.L. ([PERSON]); writing--review and editing, G.Q., Y.L. ([PERSON]), F.Z. and Y.Z.; visualization, Y.L. ([PERSON]); supervision, F.Z.; project administration, F.Z.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Grant Nos. 2018 YFC0308500), National Natural Science Foundation of China (Grant Nos. U1806201), the Science and Technology on Underwater Information and Control Laboratory (Grant No. 6142218200410).  
Not applicable.  
The authors would like to thank the editors and the anonymous reviewers.  
The authors declare no conflict of interest.