# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 5 Experimental Evaluation
### Simulation Results
#### 5.2.4 Generalization Ability of NDR-Net  
A. Joint estimation of SNR, Channel model and velocity  
Since the fixed training SNR is 15 dB when exploring the generalization of NDR-Net to the channel model and moving speed, it is easy to misjudge the channel model under low SNR. Meanwhile, the performance of the training model under the harsh environment of high SNR is better than that of the channel model matching the test channel. Therefore, in order for NDR-Net to improve channel selection capability under low SNR and model matching degree under high SNR, it is necessary to conduct joint estimation of SNR and channel model. Therefore, the model trains the TDL-A, TDL-B and TDL-C channels respectively with SNR of 5 dB and 25 dB. At this time, the corresponding moving speed of different channels is taken into account, and the environmental factors are extended. Figures 12-14 show the simulation results of the proposed algorithm when SNR is 15 dB and the channel model is TDL-A-30 Km/h, TDL-B-120 Km/h and TDL-C-350 Km/h respectively. It can be seen that since both SNR and channel model are taken into account in NDR-Net, the channel model corresponding to the test environment can always be selected by NDR-Net when testing under 0-35 dB. Since the TDL-A channel has the smallest number of paths, the MMSE can take advantage of the noise information in the channel and therefore outperforms the NDR-Net in terms of low SNR. when the channels are TDL-B and TDL-C, the MMSE estimated performance is limited at this point. It can be seen that when the MSE is \(10^{-2}\), the loss of SNR performance is 10 dB for the test at TDL-A-30 Km/h, the SNR performance improves by 2 dB at TDL-B-120 Km/h, and the SNR performance improves by about 3 dB at TDL-C-350 Km/h. Figure 11: Generalization performance of different channel estimators when the training SNR = 15 dB and the testing channel is TDL-C-350 Km/h. Figure 12: Generalization performance of different channel estimators when the test channel is TDL-A, SNR = 15 dB, Velocity = 30 Km/h. Figure 13: Generalization performance of different channel estimators when the test channel is TDL-B, SNR = 15 dB, Velocity = 120 Km/h. B. Generalization of NDR-Net under other factors  
Because of the complexity and variability of the actual high-speed mobile environment, it is not sufficient to consider only SNR, channel model and mobile speed. In OFDM systems, in addition to the Doppler shift caused by the moving speed, there is often a Carrier Frequency Offset (CFO) between the transmitter carrier frequency and the receiver local oscillator. This offset may cause a rotation in the phase of the signal and ICI, resulting in an error in the received signal. On the other hand, the OFDM signal needs to sum all subcarriers after IFFT operation, so the transmit signal in the time domain will have high peaks, resulting in an OFDM system with a high PAPR. High PAPR can lead to in-band distortion, which can reduce system performance. To reduce the PAPR, the signal can be clipped after it has been modulated. Therefore, to further verify the effectiveness of the proposed algorithm, we first consider the situation that there is a deviation of the principal vibration frequency in the actual environment. The proposed algorithm is trained at TDL-B-120 Km/h with CFO = 0. The tests were conducted at the normalized CFO of 0, 0.01, 0.05 and 0.1, respectively. The simulation results are shown in Figure 15. It can be seen that the proposed algorithm is very sensitive to the deviation of CFO. When the normalized CFO is in the range of \([0,0.01]\), the effect on the signal is small and only a small angle of phase rotation is produced. When the CFO is 0.01, the estimated performance of the model is not affected at low SNR, and the performance loss is gradually enlarged at high SNR. It can be seen that the SNR loss is 1 dB at an MSE of \(10^{-3}\). Model performance is limited at high SNR for CFO is 0.05 or above. This is because the model already appears flat after 20 dB.