# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 5 Experimental Evaluation
### Simulation Results
#### 5.2.1 Generalization of NDR-Net on SNR  
In this part, in order to explore the generalization performance of the proposed algorithm NDR-Net on SNR, firstly, the channel model is fixed as TDL-B, and the moving speed is selected as 120 Km/h. In [20], the author has proved that the \(CNN_{D}\) trained under a single fixed SNR is not suitable for a large range of SNR environment, so three SNRs are selected to train the \(CNN_{D}\) to obtain three denoising models. In this way, after the SNR value is estimated by \(CNN_{N}\), a matching denoising model can be selected for channel denoising according to the actual situation, which can ensure that the training complexity is reduced while the estimation performance is improved.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Train set size & 12,000 \\ Validation set size & 4000 \\ Test set size & 4000 \\ Num layer & 15 \\ Learning Rate & 0,001 \\ Batch Size & 8 \\ Num Epochs & 2000 \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental Settings in NDR-Net model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 3.5 GHz \\ System bandwidth & 20 MHz \\ Sample rate & 30.72 MHz \\ Subcarrier interval & 15 KHz \\ Number of valid subcarriers & 1200 \\ Number of Resource Block & 100 \\ FFT points & 2048 \\ The length of time slot & 0.5 ms \\ OFDM symbols per time slot & 7 \\ Number of OFDM symbols & 14 \\ The length of the CP & Normal CP \\ SNR range & 0 dB to 35 dB \\ Modulation scheme & QPSK \\ Channel model & TDL-A, TDL-B, TDL-C \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental Settings for OFDM signal generation.  
Figure 5 compares the generalization performance of SNR by different channel estimation algorithms. As can be seen from the simulation results, the estimation effect of the model obtained by training only \(CNN_{D}\) at 5 dB decreases gradually after 15 dB. Due to the existence of virtual subcarriers, the model already appears flat layer at 20 dB, and the estimation effect is lower than that of LS estimation after 20 dB. The serious mismatch of SNR leads to the fact that \(CNN_{D}\) has no denoising effect. The \(CNN_{D}\) model trained at 15 dB and 30 dB is the same. It can be seen that \(CNN_{D}\) can only fit a single SNR and its nearby SNR points, and lacks adaptability to other SNRs, which also proves the conclusion of literature [20] once again. The cascaded network NDR-Net (red line in the figure) can solve this problem well. Because it can adaptively select the best denoising model according to the SNR value in the current actual environment, NDR-Net can achieve good estimation performance on any SNR, and delay the flat layer problem caused by virtual subcarriers. It can be seen that in the 0-35 dB range, NDR-Net can gain 7-10 dB higher than the LS estimation, and 5-7 dB higher than the MMSE estimation.  
In addition, we also do generalization performance comparison experiments of these algorithms on SNR under fixed other channel models and moving speeds, and the generalization results are similar.