University of Oulu

K. Mei, J. Liu, X. Zhang, K. Cao, N. Rajatheva and J. Wei, "A Low Complexity Learning-Based Channel Estimation for OFDM Systems With Online Training," in IEEE Transactions on Communications, vol. 69, no. 10, pp. 6722-6733, Oct. 2021, doi: 10.1109/TCOMM.2021.3095198

A low complexity learning-based channel estimation for OFDM systems with online training

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Author: Mei, Kai1; Liu, Jun1; Zhang, Xiaoying1;
Organizations: 1College of Electronic Science and Technology, National University of Defense Technology, Changsha, China
2Center for Wireless Communications, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-12-21


In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.

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Series: IEEE transactions on communications
ISSN: 0090-6778
ISSN-E: 1558-0857
ISSN-L: 0090-6778
Volume: 69
Issue: 10
Pages: 6722 - 6733
DOI: 10.1109/TCOMM.2021.3095198
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is supported in part by the China Scholarship Council (CSC), National Natural Science Foundation of China (NSFC) under Grant 61931020. (Corresponding author: Kai Mei.)
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