# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 2 Related Work  
In 5G communications, traditional signal processing algorithms are inefficient and inflexible in handling large data. It is also difficult to fit the nonlinear operations in the environment. Artificial Intelligence/Machine Learning (AI/ML) technologies are experts in extracting signal features automatically, processing data in milliseconds, and modeling complex wireless environments. Therefore, the convergence of AI/ML technologies with 5G can solve lots of the challenges faced by current channel estimation. In addition, at the latest 3 rd Generation Partnership Project (3 GPP) meeting, the first projects for the first version of the 5G-Advanced Release 18 (R18) standard were identified, which also includes how AI/ML can be applied to DL-based physical layer transmission technologies. Many scholars have applied AI/ML techniques to the physical layer of communication, including DL-based autoencoder technology [1; 2; 3], Modulation recognition technique [4; 5],Peak to Average Power Ratio (PAPR) reduction techniques [6; 7], Channel State Information (CSI) feedback technology [8; 9], Joint source channel coding [10; 11]. Therefore, the application of AI/ML techniques to channel estimation has great potential for both improving the accuracy of channel estimation and improving performance in scenarios with diverse channel conditions. The authors in [12] show the preliminary results of applying deep learning to channel estimation and signal detection, which uses the Fully Connected Deep Neural Network (FC-DNN) to implicitly estimate the CSI and then directly recover the transmitted symbols. The method is experimentally shown to exhibit more superior performance than traditional signal detection methods, but convergence is slow due to too many parameters. To reduce the estimation complexity, the authors in [13] combine the FC-DNN in [12] and the Channel Parameter-Based (CPB) algorithm in [14] to propose the Channel Parameter Refinement Network (CPR-Net). This network can directly estimate the channel parameters and then reconstruct the channel response by channel parameters. The results demonstrate that CPR-Net can significantly improve the estimation accuracy of complex amplitude and Doppler shift, but there is the problem of error propagation. The work in [15] proposed a DL architecture called ComNet. ComNet with a combination of digital and analog drives, using two subnetworks for channel estimation and signal detection, respectively. The results show that the model can achieve higher detection accuracy with significantly reduced parameters compared to the FC-DNN network. The above models consider only time-invariant channels, which are not applicable to the time-varying channels. Improving the accuracy of channel estimation under time-varying conditions is done in two main ways. One is to find a suitable DL model to estimate the channel response directly as accurately as possible, and the second is to use a suitable denoising algorithm to reduce the noise of the already estimated channel response. For the first method, many scholars have made related studies. The authors in [16] proposed ChanEstNet to address the problem of limited performance of downlink channel estimation due to fast time-varying and non-stationary characteristics in high-speed mobile scenarios. This method first uses CNN to extract the channel characteristic vector and then uses Recurrent Neural Network (RNN) to estimate the channel, so that it learns the features of fast time-varying and non-stationary channels. The work in [17] proposed a novel Internal Carrier Interference Network(ICINet) for sensing Inter-Carrier Interference (ICI). Experiments show that the proposed ICINet can significantly reduce inter-subcarrier Interference and improve the accuracy of existing channel estimation schemes. In [18], the interpolation process in the frequency domain is first simulated using a two-layer CNN. Then the Bi-directional Long Short-Term Memory (BiLSTM) structure is used for channel prediction.