Sun, Y. orcid.org/0000-0001-6129-4290 and Ball, E. (2022) Automatic modulation classification based on machine learning. In: Laribi, M.A., Carbone, G. and Jiang, Z., (eds.) Advances in Automation, Mechanical and Design Engineering: SAMDE 2021. 2021 International Symposium on Automation, Mechanical and Design Engineering: SAMDE 2021, 03-05 Dec 2021, Beijing, China. Mechanisms and Machine Science . Springer Cham , pp. 53-67. ISBN 9783031099083
Abstract
Automatic Modulation Classification (AMC) is a rapidly evolving technology, which can be employed in software defined radio structures, such as for military communication. Machine Learning can provide novel and efficient technology for modulation classification, especially for systems working in low Signal to Noise Ratio (SNR). For this work, a dynamic modulation classification system without phase lock is trialed. The signals are captured with different SNR and duration. Traditional Machine Learning based on the mathematical features is compared with Deep Learning based on the constellations. Based on these two methods, a hybrid model is provided. This model involved the novel Deep Learning at first and the feature classification as a supplement, which achieves good performance at low SNR area.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in Advances in Automation, Mechanical and Design Engineering. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Automatic modulation classification; Machine 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: | 17 Jan 2022 14:13 |
Last Modified: | 03 Sep 2023 00:13 |
Status: | Published |
Publisher: | Springer Cham |
Series Name: | Mechanisms and Machine Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-09909-0_5 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182457 |