# Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation
## 5 Discussion  
This paper focuses on addressing the challenge of achieving accurate channel estimation in the UWA OTFS system operating in the presence of severe Doppler and multipath effects. To tackle this issue, this paper has developed a DL-based UWA OTFS channel estimation method, taking into account the distinctive attributes of the UWA OTFS channel within the DD domain. The proposed method incorporates a DnCNN-based approach that combines the OMP algorithm with DnCNN to achieve precise channel estimation and reliable communication. The results, findings, and implications of this research are as follows.  
To enable reasonable UWA OTFS communication, this paper discusses the design of the system parameters according to the features of the UWA channel. UWA communication has more severe multipath and Doppler effects compared with radio communication. Multipath delays in UWA communication typically range from tens of milliseconds to a hundred of milliseconds. Doppler shifts in UWA communication can reach several Hz, even without any movement of the transmitter. Consequently, it is crucial to configure the parameters of UWA communication in accordance with the specific characteristics of the UWA channel. The parameters are designed considering both limitations imposed by the channel and the expected performance. Due to the large multipath delay and Doppler shift of the UWA channel, \(\Delta f\) is only as small as several Hz, and the corresponding symbol duration \(T\) is tens of milliseconds. For high-data-rate \(M\Delta f\), the value of \(M\) should be large. For not too large frame duration \(T_{f}=NT\), \(N\) should be set smaller. To facilitate effective OTFS communication in the UWA channel, it is advisable to design the parameters by selecting a larger \(M\) value and a smaller \(N\) value.  
In the UWA channel, the channel sparsity usually cannot strictly meet the sparsity assumption of compressed sensing in OMP channel estimation. To enhance the precision of UWA OTFS channel estimation, this paper proposes a model-driven DL-based UWA OTFS channel estimation method. In the proposed method, the OMP algorithm collaborates with DnCNN to estimate UWA channel, while the concatenated DnCNN denoises the preliminary channel estimation results generated by the OMP algorithm, thereby obtaining more accurate OTFS channel estimation results. The evaluation results using experimental sea channel, as illustrated in Figures 10 and 11, demonstrate the superior performance of our proposed method compared with conventional OMP and threshold-based approaches. The findings reveal that the proposed method achieves higher channel estimation accuracy and significantly reduces the BER of the system.  
This paper employs a DnCNN network with a residual block, which is a lightweight solution for OTFS channel estimation. When comparing neural networks with equivalent structure, the DnCNN network employed in this paper achieves lightweighting by reducing both the number of parameters and the number of floating-point operations. The proposed method employs approximately one-thirds fewer parameters and two-thirds fewer floating-point operations compared with the classical ResNet.