Cai, J., Gan, F., Cao, X. et al. (1 more author) (2022) Signal modulation classification based on the transformer network. IEEE Transactions on Cognitive Communications and Networking, 8 (3). pp. 1348-1357. ISSN 2372-2045
Abstract
In this work, the Transformer Network (TRN) is applied to the automatic modulation classification (AMC) problem for the first time. Different from the other deep networks, the TRN can incorporate the global information of each sample sequence and exploit the information that is semantically relevant for classification. In order to illustrate the performance of the proposed model, it is compared with four other deep models and two traditional methods. Simulation results show that the proposed one has a higher classification accuracy especially at low signal to noise ratios (SNRs), and the number of training parameters of the proposed model is less than those of the other deep models, which makes it more suitable for practical applications.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | automatic modulation classification; transformer network; deep learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 May 2022 09:22 |
Last Modified: | 20 May 2023 00:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/tccn.2022.3176640 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187401 |