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Machine-learning-based pilot symbol assisted channel prediction
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Ye, Youjie and Chen, Yunfei (2022) Machine-learning-based pilot symbol assisted channel prediction. IET Communications, 16 (8). pp. 866-877. doi:10.1049/cmu2.12390 ISSN 1751-8644.
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Official URL: https://doi.org/10.1049/cmu2.12390
Abstract
In this paper, machine learning (ML) algorithms is used for channel prediction in wireless communications. The performances of five ML algorithms are compared in terms of the prediction accuracy and the symbol error rate (SER) of different modulation schemes based on the prediction. The result shows that, for channel prediction, support vector machine (SVM) has the best performance in terms of accuracy and stability. For signal detection, SVM and linear regression (LR) have their own advantages in different ranges of signal to noise ratio (SNR). At high constellation size, ML methods give similar performances to existing scheme. From the numerical examples, the SERs based on SVM and LR can both reach lower than 10−3 in binary phase shift keying and 16-ary quadrature amplitude modulation signaling, and can reach 1.13×10−2 and 4.28×10−3 in 16-ary phase shift keying signaling respectively. In terms of prediction time, SVM is more efficient.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Journal or Publication Title: | IET Communications | ||||||||
| Publisher: | The Institution of Engineering and Technology | ||||||||
| ISSN: | 1751-8644 | ||||||||
| Official Date: | May 2022 | ||||||||
| Dates: |
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| Volume: | 16 | ||||||||
| Number: | 8 | ||||||||
| Number of Pages: | 9 | ||||||||
| Page Range: | pp. 866-877 | ||||||||
| DOI: | 10.1049/cmu2.12390 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Copyright Holders: | © 2022 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology | ||||||||
| Date of first compliant deposit: | 28 March 2022 | ||||||||
| Date of first compliant Open Access: | 13 April 2022 | ||||||||
| RIOXX Funder/Project Grant: |
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