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Low-complexity channel estimation for V2X systems using feed-forward neural networks
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Tabesh Mehr, Pooria, Koufos, Konstantinos, El Haloui, Karim and Dianati, Mehrdad (2024) Low-complexity channel estimation for V2X systems using feed-forward neural networks. IET Communications . ISSN 1751-8628. (In Press)
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Abstract
Research on machine learning for channel estimation, especially neural network solutions for wireless communications, is attracting significant current interest. This is because conventional methods cannot meet the present demands of the high speed communication. In the paper, we deploy a general residual convolutional neural network to achieve channel estimation for the orthogonal frequency-division multiplexing (OFDM) signals in a downlink scenario. Our method also deploys a simple interpolation layer to replace the transposed convolutional layer used in other networks to reduce the computation cost. The proposed method is more easily adapted to different pilot patterns and packet sizes. Compared with other deep learning methods for channel estimation, our results for 3GPP channel models suggest improved mean squared error performance for our approach.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
Journal or Publication Title: | IET Communications | ||||||
Publisher: | The Institution of Engineering and Technology | ||||||
ISSN: | 1751-8628 | ||||||
Official Date: | 2024 | ||||||
Dates: |
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Status: | Peer Reviewed | ||||||
Publication Status: | In Press | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Copyright Holders: | © 2024 The Institution of Engineering and Technology | ||||||
Date of first compliant deposit: | 26 April 2024 | ||||||
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Open Access Version: |
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