# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
## 5 Discussion  
### Findings and Implications  
In this paper, OTFS was investigated for UWA communication with severe Doppler and multi-path effects. Specifically, a DL-based UWA OTFS receiver was designed, considering the characteristics of the UWA OTFS channel in the DD domain. The proposed SC-CNN-BiLSTM-based OTFS signal detection method aims to extract effective signal features with the existing ICI and ISI for reliable communication. The results, findings and implications of this paper are discussed below. This paper analyzed the OTFS system parameter design for UWA communication. The Doppler shift in UWA communication was much more severe than that of radio communication, as can be seen from Table 1. Even a slight movement of the transceiver would cause an obvious Doppler shift. For effective UWA OTFS communication, this paper discussed the system parameters that are applicable to the UWA channel for the expected performance. The discussed details are in Section 2. The small value of \(\Delta f\) made the interference severe, and the small value of \(N\) affected the detection accuracy. This paper proposed a UWA OTFS signal detection method based on the deep neural network. The conventional signal methods usually assume a linear system and a known distribution of noise whose performance will downgrade in the complex actual channel. In the research area of OTFS signal detection, the authors of [11,12] employed the conventional linear signal detection methods ZF and LMMSE. In [13], the Bayesian-based nonlinear algorithm MP was advocated for, and the interference term was assumed to be approximately Gaussian distributed noise. However, in realistic UWA communication, the interference term may not follow the Gaussian distribution. Although nonlinear algorithms can approximate optimal performance through a large number of iterations, their complexity is still much higher than that of LMMSE algorithms. For reliable receiver design, this paper proposed a data-driven, DL-based UWA OTFS receiver. It neither has a linear assumption nor requires prior knowledge of the UWA channel. The semi-experimental results with an experimental sea channel in Figures 9 and 12 showed that our proposed method outperformed the typical LMMSE and MP methods. The results indicate the power of deep learning in non-linear variation wireless communication channels without prior knowledge. This paper proposed an SC-CNN-BiLSTM-based OTFS signal detection method which takes advantage of both the CNN and RNN for feature extraction and sequential data processing. In the research field of DL-based OTFS signal detection, in [32], the 2D-CNN was used instead of the signal detection, but a single CNN can only extract local features and cannot process time series data efficiently. The authors of [33] conducted a preliminary exploration of FC-DNN-based signal detection for UWA OTFS. However, the FC-DNN has non-convex optimization and gradient vanishing problems, which limits its robustness. In this paper, a DL-based signal detection neural network is proposed for UWA OTFS communication, named SC-CNN-BiLSTM, which cascades the CNN with BiLSTM for UWA OTFS signal detection. Specifically, the proposed SC-CNN-BiLSTM-based OTFS signal detection method employs the CNN to extract signal features and learn the hidden relationship between its input and output. In addition, the SC structure provides information fusion from the interaction of the current layer and previous layers. Furthermore, BiLSTM in the network can continuously memorize and extract effective information with sequentially transmitted symbols from the past to the future. The cascaded neural network structure can extract signal features and effective information to recover distorted received symbols. As shown in Figures 6, 9 and 12, the proposed method had better BER performance than the FC-DNN- and 2D-CNN-based signal detection methods for UWA OTFS. As shown in Figure 13, the proposed method is most robust when the system has channel estimation errors.