# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 5 Experimental Evaluation
### Simulation Results
#### 5.2.3 Generalization of NDR-Net on Channel Model  
In this part, In order to explore the generalization performance of the proposed algorithm NDR-Net on the channel model, \(CNN_{N}\) is used to estimate the RMS delay spread value of the channel. Similar to the idea proposed in Section 5.2.1, \(CNN_{D}\) is trained on three channel models, namely TDL-A, TDL-B and TDL-C, to obtain three network models. Then, according to the root mean square delay spread value, the corresponding trained denoising network is selected for denoising, so as to achieve the purpose of adaptive channel model selection. Among them, the RMS delay spreads of the three channels are 10 ns, 100 ns and 1000 ns, respectively. Because the number is too small, in order to facilitate the model to compare the labels, they are scaled and multiplied by \(10^{7}\), so that the labels of \(CNN_{N}\) become 0.1 s, 1 s and 10 s.  
Figure 9 shows that when the fixed training SNR is 15 dB, the model is trained under TDL-A-30 Km/h, TDL-B-120 Km/h and TDL-C-350 Km/h, and tested under TDL-A-30 Km/h. The channel model generalization performance of different channel estimation algorithms obtained is compared. It should be noted in particular here that the proposed algorithm is not sensitive to the Doppler shift due to velocity, since it has been shown in the previous subsection. This means that the effect of velocity on the algorithm is small and almost negligible. Thus the presence of velocity has no effect on the results of the experiment. As can be seen from the simulation results, in the case of low SNR, due to the large noise component, the error between the RMS delay extension of the TDL-B channel estimated by \(CNN_{N}\) network and the real value is large, resulting in the channel model is easy to be judged as the mismatched channel model TDL-C, resulting in misjudgment in the selection of the model. Under high SNR, because SNR and channel model do not match at the same time, the model trained under TDL-B and TDL-C channel can be well adapted to the test channel.  
In Figure 10, the test channel model is changed to TDL-B-120 Km/h. At this time, the error of TDL-A channel is extremely large and can no longer meet the requirements. Similarly, the test channel model is changed to TDL-C-350 Km/h in Figure 11. At this time, TDL-A and TDL-B cannot satisfy the requirements due to too large errors. The MMSE algorithm always has the best performance at low SNR since it takes into account the noise factor. At high SNR, NDR-Net performs better than LS estimates, but MMSE also outperforms NDR-Net when the channel is the more complex TDL-C.  
Figure 8: Generalization performance of NDR-Net when the training channels are TDL-C-350 Km/h and TDL-C-500 Km/h and the test channel is TDL-C-500 Km/h.  
Figure 10: Generalization performance of different channel estimators when the training SNR = 15 dB, and the testing channel is TDL-B-120 Km/h.  
Figure 9: Generalization performance of different channel estimators when the training SNR = 15 dB, and the testing channel is TDL-A-30 Km/h.