Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
Author:
Li Yinying12, Bian Xin1, Li Mingqi1ORCID
Affiliation:
1. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China 2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract
Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep Learning (DL) based OFDM channel estimators are only applicable to Signal-to-Noise Ratios (SNRs) in a small range, and the estimation performance of the existing algorithms is greatly limited when the channel model or the mobile speed at the receiver does not match. To solve this problem, this paper proposes a novel network model NDR-Net that can be used for channel estimation under unknown noise levels. NDR-Net consists of a Noise Level Estimate subnet (NLE), a Denoising Convolutional Neural Network subnet (DnCNN), and a Residual Learning cascade. Firstly, a rough channel estimation matrix value is obtained using the conventional channel estimation algorithm. Then it is modeled as an image and input to the NLE subnet for noise level estimation to obtain the noise interval. Then it is input to the DnCNN subnet together with the initial noisy channel image for noise reduction to obtain the pure noisy image. Finally, the residual learning is added to obtain the noiseless channel image. The simulation results show that NDR-Net can obtain better estimation results than traditional channel estimation, and it can be well adapted when the SNR, channel model, and movement speed do not match, which indicates its superior engineering practicability.
Funder
National Key R&D Program of China
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference26 articles.
1. An Introduction to Deep Learning for the Physical Layer;Oshea;IEEE Trans. Cogn. Commun. Netw.,2017 2. Aoudia, F.A., and Hoydis, J. (2018). Proceedings of the 2018 52nd Asilomar Conference on Signals Systems, and Computers, Pacific Grove, CA, USA, 28–31 October 2018, IEEE. 3. Kang, X.-F., Liu, Z.-H., and Yao, M. (2022). Deep Learning for Joint Pilot Design and Channel Estimation in MIMO-OFDM Systems. Sensors, 22. 4. A Novel Modulation Classification Method for FM Signals based on the Time-Frequency Distribution and CNN;Zhang;IET Radar Sonar Navig.,2017 5. Hong, S., Wang, Y., Pan, Y., Gu, H., Liu, M., Yang, J., and Gui, G. (2020). Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 25–28 May 2020, IEEE.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|