## 5 Experimental Evaluation
### Simulation Results  
According to the simulation results of Section 5.2.4, it can be seen that using a two-layer two-way LSTM structure for FTN signal detection will achieve relatively optimal results. The following simulation will use the above-mentioned relatively optimal LSTM structure for simulation unless otherwise specified. In addition, we convert the accuracy in the data-driven model into BER, a performance indicator in the communication field, for performance analysis.  
Taking into account the feasibility of engineering implementation, we performed simulations in a variety of different multipath channel scenarios, which also proved the effectiveness of the proposed scheme to a certain extent.  
2.1 Performance Comparison between DMD-JD FTN Detection Algorithm and Pure Data-Driven Detection Algorithm  
Firstly, as shown in Figure 4, shows the performance comparison of different channel equalization algorithms when the compression factor \(\tau=0.7\) in the TU channel. The curves in different colors in the figure represent different channel equalization algorithms, followed by no equalization, ZF equalization, and MMSE equalization. Differentiing shapes represent different data-driven algorithms. The redine represents the pure data-driven performance curve, which has aarge performance gap compared with the results of the DMD-JD FTN algorithm represented by the blue and black curves. The reason is that pure data driving is difficult to solve the non-linear interference introduced by FTN and the aliasing of non-linear interference through the multipath channel, which results in the mismatch of training data and test data. In Section 5.2.3, the performance of the DMD-JD FTN algorithm will also be compared with the performance of traditional model-driven algorithms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Train set size & 10,000 \\ Test set size & 10,000 \\ Numayer & 6 \\ Dropout rate & 1.0 \\ Num unit & 128 \\ Learning Rate & \(5\times 10^{-4}\) \\ Batch Size & 64 \\ Num Epochs & 10 \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental settings in Deep Learning.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Channel Model** & **Channel Parameters** \\ \hline \multirow{2}{*}{TU (Typical Urban)} & \(\zeta=[\) 0.0 \(\upmu\)s, 0.2 \(\upmu\)s, 0.5 \(\upmu\)s, 1.6 \(\upmu\)s, 2.3 \(\upmu\)s, 5.0 \(\upmu\)s ]; \\ \cline{2-3}  & P = [ –3.0 dB, 0.0 dB, –2.0 dB, –6.0 dB, –8.0 dB, –10.0 dB ]; \\ \cline{2-3}  & \(\zeta=[\) 0.0 \(\upmu\)s, 0.2 \(\upmu\)s, 0.8 \(\upmu\)s, 1.2 \(\upmu\)s, 2.3 \(\upmu\)s, 3.7 \(\upmu\)s ]; \\ \cline{2-3}  & P = [ 0.0 dB, \(-0.9\) dB, \(-4.9\) dB, \(-8.0\) dB, \(-7.8\) dB, \(-23.9\) dB ]; \\ \cline{2-3}  & \(\zeta=[\) 0.0 \(\upmu\)s, 14.0 \(\upmu\)s, 20.0 \(\upmu\)s ]; \\ \cline{2-3}  & P = [ 0.0 dB, \(-10.0\) dB, \(-14.0\) dB ]; \\ \hline \hline \end{tabular}
\end{table}
Table 3: The delay and power of different paths in the channel model.  
The black and blue curves in the figure represent the performance of using MMSE equalization and ZF equalization, respectively. It can be seen that the performance of using MMSE equalization is far superior to that of ZF equalization, even reaching 3-5 dB. The possible reason is that the ZF equalization does not consider the influence of noise factors, and the results of the MMSE equalization are quite different when the noise isarge, and the performance gap is reduced when the signal-to-noise ratio is relatively high. In addition, since ZF is an unbiased estimation, the noise signal will be amplified when the signal is equalized, thereby affecting the performance of the data driving method.