Sun, Y. orcid.org/0000-0001-6129-4290 and Ball, E.A. orcid.org/0000-0002-6283-5949 (2022) Deep learning applied to automatic modulation classification at 28 GHz. In: Arai, K., (ed.) Intelligent Systems and Applications: Proceedings of the 2022 Intelligent Systems Conference (IntelliSys) Volume 1. 2022 Intelligent Systems Conference (IntelliSys), 01-02 Sep 2022, Amsterdam, The Netherlands. Lecture Notes in Networks and Systems, 1 (LNNS 542). Springer International Publishing , pp. 403-414. ISBN 9783031160714
Abstract
Automatic Modulation Classification (AMC) is a fast-expanding technology, which is used in software defined radio platforms, particularly relevant to fifth generation and sixth generation technology. Modulation classification as a specific topic in AMC applies Deep Learning (DL) in this work, which contributes a creative way to analyse the signal transmitted in low Signal to Noise Ratio (SNR). We describe a dynamic system for the Millimeter wave (mmW) bands in our work. The signals collected from the receiving system is without phase lock or frequency lock. DL is applied to our system to classify the modulation types within a wide range of SNR. In this system, we provided a method named Graphic Representation of Features (GRF) in order to present the statistical features in a spider graph for DL. The RF modulation is generated by a lab signal generator, sent through antennas and then captured by an RF signal analyser. In the results from the system with the GRF techniques we find an overall classification accuracy of 56% for 0 dB SNR and 67% at 10 dB SNR. Meanwhile the accuracy of a random guess with no classifiers applied is only 25%. The results of the system at 28 GHz are also compared to our previous work at 2 GHz.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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 Intelligent Systems and Applications: Proceedings of the 2022 Intelligent Systems Conference (IntelliSys) Volume 1. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Automatic modulation classification; Deep learning; Millimeter wave |
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: | 29 Nov 2022 14:46 |
Last Modified: | 31 Aug 2023 00:13 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Lecture Notes in Networks and Systems |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-16072-1_30 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193901 |