## 4 Simulation and Result Analysis
### Results Analysis  
From the simulation curves of Figures 6 and 7, we can see that in terms of convergence speed under the condition of parameter 1, The DTV-LMS algorithm can reach a convergence state approximately during its 25 th iteration. The MVSSLMS algorithm can reach a convergence state at about the 36 th iteration. The VSSLMS algorithm and SVSLMS algorithm can reach convergence states at about the 70 th iteration and the 120 th iteration, respectively. Similarly, under the conditions of parameter 2, although their convergence speeds differ slightly, the convergence speed of the DTV-LMS algorithm proposed in this paper is obviously faster than the other three compared algorithms. When the phase change of the signal (i.e., the 500 th iteration, the tracking, and the SSM performance of the DTV-LMS and the SVSLMS algorithms) is better than that of the other two algorithms, they can quickly converge again. However, the computational complexity of the DTV-LMS algorithm is lower than that of the SVSLMS algorithm. Thus, the algorithm proposed in this paper is slightly better. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \(\mu(\mathbf{t})\) & **Parameter 1** & **Parameter 2** \\ \hline \multirow{2}{*}{SVSSLMS} & \(\mu(\mathrm{t})=\beta(1/\left(1+e^{-\alpha\mathrm{t}\mathrm{t}\mathrm{t}} \right)-0.5)\) & \(\alpha=1.0\) & \(\alpha=0.83\) \\ \cline{2-4}  & \(\beta=0.1\) & \(\beta=0.09\) \\ \hline \multirow{2}{*}{VSSLMS} & \(\mu(\mathrm{t})=\beta\left(1-\mathrm{e}^{-\alpha\mathrm{t}\mathrm{t}^{2}(t)} \right)\) & \(\alpha=0.7\) & \(\alpha=0.12\) \\ \cline{2-4}  & \(\beta=0.7\) & \(\beta=0.1\) \\ \hline \multirow{2}{*}{MVSSLMS} & \(\mu(\mathrm{t})=\alpha\mu(\mathrm{t}-1)+\beta p^{2}(t)\) & \(\alpha=0.97\) & \(\alpha=0.76\) \\ \cline{2-4}  & \(\mathrm{p}(\mathrm{t})=\gamma\mathrm{p}(\mathrm{t}-1)+(1-\gamma)\mathrm{e}( \mathrm{t})\mathrm{e}(\mathrm{t}-1)\) & \(\beta=0.03\) & \(\beta=0.03\) \\ \cline{2-4}  & \(\gamma=0.98\) & \(\gamma=0.91\) \\ \hline \multirow{2}{*}{DTV-LMS} & \(\mu(t)=atan^{-1}\left(-\beta\left|e(t)e(t-\Delta t)\right|\right)\) & \(\alpha=0.075\) & \(\alpha=0.071\) \\ \cline{2-4}  & \(\beta=3\) & \(\beta=8\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The corresponding parameter value of each algorithm. Figure 6: The curves of the algorithm convergence in parameter 1. It can be seen from the curve of the ICR shown in Figures 8 and 9 that under the condition of parameter 1, the final ICR values of the SVSLMS, MVSSLMS, and VSSLMS algorithms are 74.98 dB, 74.76 dB, and 74.15 dB, respectively; their ICR are close to each other. The final ICR value of the DTV-LMS algorithm proposed in this paper is 78.21 dB, which increased by at least 5 dB compare to the other three algorithms. Under the condition of parameter 2, the ICR values of DTV-LMS, SVSLMS, MVSSLMS, and VSSLMS are 79.96 dB, 77.03 dB, 78.12 dB, and 75.65 dB, respectively, and the ICR value of the proposed algorithm is still the highest. When the phase  
+++ ==WARNING: Truncated because of repetitions==
It can be seen from the curve of the ICR shown in Figures 8 and 9 that under the condition of parameter 1, the final ICR values of the SVSLMS, MVSSLMS, and VSSLMS algorithms are 74.98 dB, 74.76 dB, and 74.15 dB, respectively; their ICR are close to each other. The final ICR value of the DTV-LMS algorithm proposed in this paper is 78.21 dB, which increased by at least 5 dB compare to the other three algorithms. Under the condition of parameter 2, the ICR values of DTV-LMS, SVSLMS, MVSSLMS, and VSSLMS are 79.96 dB, 77.03 dB, 78.12 dB, and 75.65 dB, respectively, and the ICR value of the proposed algorithm is still the highest. When the phase suddenly changes its signal (i.e., when the 15 ms signal from the graph of the curve changes), we can see that the DTV-LMS algorithm clearly achieves its final stable ICR value faster than the other three compared algorithms--that is to say, the algorithm proposed in this paper can track and adapt to channel changes in a short time, and return to the stable state before channel mutation.