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Effective Feature-Based Automatic Modulation Classification Method Using DNN Algorithm
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Publication Year
2019-03-18
Journal
1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, pp.557-559
Keyword
automatic modulation classificationcorrelationcumulantdeep neural networkeffective features
Mesh Keyword
Automatic modulation classificationAutomatic modulation classification (AMC)Correlation coefficientCumulantseffective featuresFeature-basedModulation signalsModulation types
All Science Classification Codes (ASJC)
Electrical and Electronic EngineeringComputer Science ApplicationsArtificial Intelligence
Abstract
In this paper, we propose an effective feature-based automatic modulation classification (AMC) method using a deep neural network (DNN). In order to classify the modulation type, we consider effective features according to the modulation signals. The proposed method removes the meaningless features that have little influence on the classification and only uses the effective features that have high influence by analyzing the correlation coefficients. From the simulation results, we observe that the proposed method can make the AMC system low complexity.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36434
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063870284&origin=inward
DOI
https://doi.org/10.1109/icaiic.2019.8669036
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8665865
Type
Conference
Funding
This research was supported by the MSIT, Korea, under the ITRC support program (2018-0-01424) supervised by the IITP. .
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Kim, Jae-Hyun Image
Kim, Jae-Hyun김재현
Department of Electrical and Computer Engineering
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