# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 2 Related Work
Finally, it is input to CNN again to reduce the dimensionality to obtain CSI. The results show that the proposed DL-based algorithm can obtain higher estimation accuracy than the traditional channel estimation algorithm in different environments. The authors in [19] proposed a Linear Machine Learning (LML) based channel estimation and online training scheme. The data are generated in groups by tracking the channel in real-time and a linear regression model is used as the learning module. The results show that the scheme can adapt to channel changes under shorter training cycles and a small amount of training data. Another way to improve the accuracy of channel estimation is through denoising. The authors in [20] proposed an image noise reduction model Denoising convolutional neural network (DnCNN). The model can learn the noise in the image while cascading on residual learning can accelerate the training process. Simulation results demonstrate that DnCNN has better denoising effect compared with traditional denoising methods and can be extended to handle general image denoising tasks. The authors in [21] consider the millimeter-wave channel matrix as a two-dimensional image and apply a Learned Denoising-based Approximate Message Passing (LDAMP) network, fusing DnCNN to an iterative signal reconstruction algorithm for channel estimation. Simulation results showing that the LDAMP outperforms conventional algorithms and compressed sensing algorithms. The authors in [22] designed a channel estimator based on Knowledge-Driven Machine Learning (KDML) using the DnCNN in large-scale Multiple-Input MultipleOutput(MIMO) systems. The estimator has a simple neural network structure and a low training cost. Simulation results demonstrate that the estimator outperforms the LS algorithm and machine learn-based estimators. The authors in [23] proposed a two-stage channel grid estimation algorithm ChannelNet. Firstly, the time-frequency response of the fast-fading channel is considered as a two-dimensional image, and the Low Resolution (LR) image is obtained by Gaussian interpolation of the pilot value. Then the Super-Resolution Neural Network (SRCNN) algorithm is used to obtain the estimated channel as a High Resolution(HR) image. Finally, the DnCNN is used to remove the noise effect. An improved Convolutional Blind Denoising Network (CBDNet) is proposed as a channel estimator in a mmWave MIMO system [24]. The estimator can estimate the SNR level while reducing the noise in the channel matrix. However, the channel model and the mismatch of moving speed are not considered. According to our current survey, the existing DL-based channel estimation studies mainly stay at the level of improving the generalizability of the model to SNR. And generalizability considering channel model and movement speed has not been available or less studied. In this section, we comprehensively introduce the application of AI/ML in the direction of channel estimation in OFDM/MIMO communication. Some of these techniques that are used in the high-speed channel and our contribution are summarised and compared in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Research Direction** & **Contribution** & **Limitation** \\ \hline \multirow{4}{*}{[16]} & \multirow{4}{*}{Improving the channel estimation accuracy of OFDM systems in high-speed mobile scenarios} & Proposed ChanEstNet, which can avoid the fast time-varying and non-smooth characteristics in high-speed moving scenes that lead to the limited estimation performance & Only the two extreme speeds under the same channel are considered \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \multirow{4}{*}{[17]} & \multirow{4}{*}{ \begin{tabular}{c} Consider the effect of ICI to improve the accuracy of channel estimation under fast time-varying channels \\ time-varying channels \\ \end{tabular}