## 4 Simulation Results  
In this section, we verify the performance of our proposed algorithm by the normalized mean square error (NMSE). The NMSE was defined as \(\left\|\mathbf{h}-\mathbf{\hat{h}}\right\|_{2}^{2}/\left\|\mathbf{h}\right\|_ {2}^{2}\). We set the parameters as follows (see Table 1): the number of symbols \(N=64\), the number of subcarriers \(M=128\), the carrier frequency was \(15\) kHz, and the subcarrier frequency was \(4\times 10^{9}\) Hz. The maximum Doppler shift varied according to the speed, and the maximum delay value was \(l_{\tau}=10\). \(k_{max}\) was a parameter related to speed.  
First, we compared the performance of different algorithms under different signal-to-noise ratios (SNR). Here, we used the reference algorithm for the least square (LS) algorithm, the orthogonal matching pursuit (OMP) algorithm, and the conventional SBL algorithm [22]. The user speed was \(250\) km/h, and \(l_{\tau}=10\). As shown in Figure 5, in the case of a low signal-to-noise ratio, our proposed structured SBL algorithms have a significant advantage over other algorithms. Specifically, our algorithm better reduces interference.  
Then, we compared the cases where \(l_{\tau}\) is constantly changing in different delay regions, as shown in Figure 6. In this OTFS modulation system, we set the SNR to \(20\) dB, the user speed to \(250\) km/h, and \(Q=4\). The figure shows that the structured SBL algorithm we proposed maintains certain advantages. As the delay range \(l_{\tau}\) continues to expand, the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Symbols \(N\) & \(64\) \\ Subcarries \(M\) & \(128\) \\ Carrier frequency \(\Delta f\) & \(15\) kHz \\ Subcarrier frequency & \(4\times 10^{9}\) Hz \\ The maximum delay \(l_{max}\) & \(10\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 5: Normalized MSE versus SNR.  
estimation results become increasingly accurate. We can see that under the same estimation error, compared to other algorithms, the structured SBL has the smallest value in delay dimension, which means that the pilot proportion is the smallest. For example, under the condition of a mean square error of \(10^{-2}\), the values of the delay dimension \(l_{\tau}\) of the structured SBL and conventional SBL algorithm are 7 and 8, respectively. The pilot overhead (except guard symbols) is \(\eta=\frac{(2k_{\text{max}}-1)(l_{\tau}+1)}{M\times N}\). The pilot overheads of the structured SBL and the conventional SBL are about 1.07% and 1.21%, respectively.  
Next, we set the different speeds for performance comparison in Figure 7. Here, we set the speed variation range to [150; 350]; the SNR was 20 dB, \(Q=4\), and \(l_{\tau}=5\). The figure shows that the channel estimation results are affected to some extent with increasing speed. The higher the speed is, the slightly worse the accuracy of the estimation becomes. However, in terms of the trend of the curve, the curve is smooth overall with little change, and the LS algorithm is highly sensitive to changes in speed. On the other hand, the structured SBL algorithm maintains high robustness.  
Figure 6: Delay range \(l_{\tau}\) versus normalized MSE.  
Figure 7: Velocity versus normalized MSE.