# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
###### Abstract
replaced the channel equalization and demodulation blocks of the receiver with a five-layer fully connected deep neural network (FC-DNN) in the OFDM system [22]. The experiment results show that the DNN-based receiver was more reliable than the conventional methods. In the UWA OFDM communication system, an FC-DNN is used to realize the whole signal processing at the receiver [23; 24], and simulation results show that the FC-DNN offered better bit error rate (BER) performance than conventional algorithms. In [25], the long short-term memory (LSTM) neural network architecture was employed as the receiving module of the cyclic shift keying spread spectrum UWA communication system. The neural network is fed the communication signals passing through known channel impulse responses in the offline stage and then used to demodulate the received signal in the online stage. For UWA communication, the receiver in [26] jointly employed a CNN for channel equalization and an FC-DNN for demodulation. Compared with a single DNN-based OFDM receiver, this joint network model can better extract channel information for data recovery. In [27], DenseNet was proposed to replace the entire information recovery process of block-based MIMO receivers. DeseNet takes multiple modules as one system for joint optimization, and its BER outperforms block-by-block receivers. A signal detection scheme based on LSTM was proposed in [28]. The authors utilized an RNN with the BiLSTM architecture for signal detection in [29]. The simulation results show that the trained model can trace the characteristics of wireless time-varying channels and achieve accurate and robust signal recovery performance. For using DL in OTFS systems, [PERSON] et al. conducted a preliminary exploration of FC-DNN-based signal detection [30]. [PERSON] et al. proposed a receiver with CNN-based signal detection for OTFS [31]. [PERSON] et al. proposed a 2D-CNN-based OTFS signal detection scheme [32] and utilized data augmentation to improve the overall performance. In [33], an FC-DNN was used to replace the signal detection in the UWA OTFS system. It can be seen that the DL-based methods outperformed the conventional methods under complex channels. Whereas the DNN and CNN can only extract local features, this paper proposes an OTFS signal detection scheme based on the joint CNN and RNN to utilize both local and sequential features. The main contributions of this paper are summarized as follows:  
* We propose an UWA OTFS signal detection method based on the deep neural network. The UWA channel has severe transmission loss, time-varying multi-path propagation and the severe Doppler effect, which are extremely challenging for signal detection. Conventional signal detection methods not only have high computational complexity but also require prior knowledge of the noise. DL-based signal detection has the advantage of recovering signals with complex nonlinear interference and noise by training and learning, and it does not have to assume any prior knowledge. In this paper, we propose a DL-based signal detection method for UWA OTFS in the complex, nonlinear UWA channel to improve system performance. To the best of our knowledge, this is the first DL-based signal detector proposed for UWA OTFS communication. * The SC-CNN-BiLSTM network is designed for OTFS signal detection in the complex UWA channel, which takes the advantages of both CNNs and RNNs for feature extraction and sequential data processing. Different from our previous work [33], a totally new neural network structure is proposed for performance improvement. The CNN in the proposed network can extract data features and learn the potential relationship between its input and output. Furthermore, the skip connection (SC) in a CNN can provide the flexibility of data feature fusion for performance improvement. The cascaded BiLSTM in the network can memorize and extract the effective information from sequential transmitted symbols from the past to the future, which can mitigate the ICI and ISI.