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DC Field | Value | Language |
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dc.contributor.author | Li, L | - |
dc.contributor.author | Huang, J | - |
dc.contributor.author | Cheng, Q | - |
dc.contributor.author | Meng, H | - |
dc.contributor.author | Han, Z | - |
dc.date.accessioned | 2020-11-08T19:22:51Z | - |
dc.date.available | 2020-11-08T19:22:51Z | - |
dc.date.issued | 2020-10-30 | - |
dc.identifier.citation | Li, L. et al. (2021) 'Automatic Modulation Recognition: A Few-Shot Learning Method Based on the Capsule Network', IEEE Wireless Communications Letters. 10 (3)pp. 474 - 477. doi: 10.1109/lwc.2020.3034913. | en_US |
dc.identifier.issn | 2162-2337 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/21786 | - |
dc.description.abstract | With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, aiming to obtain higher classification accuracy, DL requires numerous training samples. In order to solve this problem, it is a challenge to study how to efficiently use DL for AMR in the case of few samples. In this letter, inspired by the capsule network (CapsNet), we propose a new network structure named AMR-CapsNet to achieve higher classification accuracy of modulation signals with fewer samples, and further analyze the adaptability of DL models in the case of few samples. The simulation results demonstrate that when 3% of the dataset is used to train and the signal-to-noise ratio (SNR) is greater than 2 dB, the overall classification accuracy of the AMR-CapsNet is greater than 80%. Compared with convolutional neural network (CNN), the classification accuracy is improved by 20%. | - |
dc.description.sponsorship | 10.13039/501100012129-Aerospace Science and Technology Innovation Fund of China Aerospace Science and Technology Corporation; 10.13039/501100019082-Shanghai Aerospace Science and Technology Innovation Foundation (Grant Number: SAST2018045); 10.13039/501100002663-Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University (Grant Number: CX2020152); 10.13039/100000001-NSF (Grant Number: EARS-1839818, CNS-1717454, CNS-1731424 and CNS-1702850). | - |
dc.format.extent | 474 - 477 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2020 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works (see: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/). | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.subject | Convolutional neural network (CNN) | en_US |
dc.subject | Automatic modulation recognition (AMR) | en_US |
dc.subject | Capsule network (CapsNet) | en_US |
dc.subject | Few-Shot learning | en_US |
dc.subject | Deep learning (DL) | en_US |
dc.title | Automatic Modulation Recognition: A Few-Shot Learning Method Based on the Capsule Network | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2020-10-26 | - |
dc.identifier.doi | https://doi.org/10.1109/lwc.2020.3034913 | - |
dc.relation.isPartOf | IEEE Wireless Communications Letters | - |
pubs.issue | 3 | - |
pubs.publication-status | Published | - |
pubs.volume | 10 | - |
dc.identifier.eissn | 2162-2345 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2020 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works (see: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/). | 6.89 MB | Adobe PDF | View/Open |
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