# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 6 Conclusions  
In this paper, a cascaded network NDR-Net is proposed to address the problem of insufficient estimation performance of traditional channel estimation algorithms and existing DL-based channel estimation algorithms in OFDM systems. This model can simultaneously solve the problems of signal-to-noise ratio, channel model and mobile speed mismatch in 5G multipath time-varying channels.  
The proposed NDR-Net is compared with LS, MMSE estimation algorithm and a single denoising model under different SNR, three 5G channels and mobile speeds by considering the signal as full pilot, using QPSK modulation scheme and MSE as evaluation function. Simulation results show that the proposed model can be applied to standard 5G channels, and it also improves by nearly 5-7 dB compared with the MMSE algorithm in the case of SNR mismatch, and can overcome the problem of too small noise adaptation interval encountered by the single denoising model. When the moving velocities in the same scene are not matched, the proposed model can always be applied to different Doppler shifts and has certain robustness. When the Channel model is not matched, the proposed algorithm can always get the optimal result by jointly estimating the SNR and Channel model, which can solve the problem of both SNR and Channel model mismatch. In addition, we also verify the generalization performance of the proposed algorithm in the presence of local oscillation offset and signal clipping at both transceivers. The results demonstrate that the proposed algorithm performs well under the condition that the local oscillation offset is less than 0.01 dB and is robust to the channel estimation after signal clipping.  
For simplicity, the overall system is supposed to be time-synchronized. The proposed algorithm focuses on typical scenarios of 5G terrestrial communication, including pedestrian, vehicle and urban channels, which to a large extent can meet the basic communication needs of most 5G scenarios. Notice that, the practical 5G communication environment covers more complex scenarios, the cost and overhead will increase dramatically if more channel models are trained separately. Therefore, the proposed algorithm is suitable for scenarios that require less real-time computing and have more abundant computing resources. In future work, deeper research and analysis will be conducted from the perspectives of how to reduce the complexity of the model, how to reduce the noise introduced by pilot and channel estimation under high-order modulation.  
Conceptualization, Y.L., M.L. and X.B.; Methodology, Y.L. and X.B.; Validation, Y.L.; Investigation, Y.L.; Resources, M.L.; Writing--Original Draft Preparation, Y.L.; Writing--Review and Editing, Y.L., M.L. and X.B.; Supervision, Y.L., M.L. and X.B.; Project Administration, M.L. and X.B.; Funding Acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China under Grant No.2018 YFB1802201.  
Not applicable.  
The authors would like to thank the Shanghai Advanced Research Institute, Chinese Academy of Sciences for supporting in this investigative project.  
The authors declare no conflict of interest.