## 5 Experimental Evaluation
### Simulation Results
#### 5.2.3 Adaptability of DMD-JD Algorithm to Multipath Channels  
**A**. DMD-JD Detection Performance Trained in the Same Channel  
As shown in Figure 8, compare the generalization performance of the training model in the same channel with a fixed signal-to-noise ratio. The curves of different colors represent different multipath channels, the solidine represents the result of the normal pairing of the signal-to-noise ratio of the training data and the test data, and the dottedine represents the network trained with a fixed signal-to-noise ratio of 7 dB to test the BER performance of the corresponding channel. On the whole, the performance of using a fixed signal-to-noise ratio is somewhat compared with the normal pairing of the signal-to-noise ratio of the training data and the test data. However, when the data-driven method is truly applied to engineering, only a fixed signal-to-noise ratio or a network model with a small number of signal-to-noise ratios need to be trained in the offline state to detect signal at all signal-to-noise ratios, which will greatly increase the reusability and practicability of the model. In addition, the simulation results show that the performanceoss in different channel conditions is acceptable, that is, the DMD-JD detection algorithm trained on the same channel has better adaptive performance. Later, what this paper aims to explain is that 7 dB is an empirical parameter, which is obtained through repeated experiments. Figure 5: FTN detection performance results of different algorithm in the TU channel, MMSE equalization, and the compression factor \(\tau=0.7\). Figure 6: FTN detection performance results of different algorithm in the VA channel, MMSE equalization, and the compression factor \(\tau=0.7\). Figure 7: FTN detection performance results of different algorithm in the PB channel, MMSE equalization, and the compression factor \(\tau=0.7\). DMD-JD Detection Performance under Different Channel Training and Signal-to-Noise Ratio Matching Conditions  
As shown in Figure 9, the comparison is when the FTN signaling compression factor \(\tau=0.7\), the training data are generated from different channel states, and the test set uses the TU channel moving speed of 30 km for testing. In the figure, different colors represent different moving rates, and differentianities represent different multipath channels. From an overall point of view, the BER performanceoss of networks trained on different channels for TU-30 KM detection is about 0.3\(\sim\)0.75 dB, showing good adaptability. It can be seen that the TU channel has the worst effect when the mobile speed is 120 km. This may be due to the existence of time selective fading caused by Doppler frequency shift, and theheads to the poor matching of data set, resulting in the decline of BER performance. The performance of the PB channel when the moving speed is 3 km is relatively poor in Figures 9 and 10. The possible reason is that the moving speed of the PB channel is relativelyow, and the delay spread of the channel is relatively small, which willead to the relatively insufficient feature extraction of Doppler shift and frequency selectivity for its training network. The two red curves represent the BER performance of a TU channel with a moving speed of 60 km for training, and the performance of training with a VA channel of 60 km has a small difference in the case of a higher signal-to-noise ratio. Therefore, the difference in channel state is not the only reason thateads to the generalization ability of the model. Time-selective fading and frequency-selective fading can also be used as features that affect the BER. What needs a special explanation here is that we compared the adaptive simulation of FTN detection in multipath channels without using the channel equalization module. We found that the detection effect is very poor when the equalization module is not used, and the adaptability is almost nonexistent.