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Paper Abstract and Keywords
Presentation 2019-11-26 10:30
[Poster Presentation] Initial Evaluation of Modulation Recognition Based on Deep Learning using Estimated SNR as Side Information
Yasutaka Yamashita, Shigeru Uchida, Hiroshi Aruga (Mitsubishi Electric Corp.)
Abstract (in Japanese) (See Japanese page) 
(in English) Modulation classification is an important research subject in cognitive radios and radio monitoring systems to recognize wireless environment. This paper reports the results of an initial evaluation of a novel modulation classification method based on deep learning using estimated SNR as side information.
Keyword (in Japanese) (See Japanese page) 
(in English) Modulation Classification / Convolutional Neural Network / / / / / /  
Reference Info. IEICE Tech. Rep.
Paper #  
Date of Issue  
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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Conference Information
Committee RISING  
Conference Date 2019-11-26 - 2019-11-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Fukutake Learning Theater, Hongo Campus, Univ. Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Researches on Super-Intelligent Networking, etc. 
Paper Information
Registration To RISING 
Conference Code 2019-11-RISING 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Initial Evaluation of Modulation Recognition Based on Deep Learning using Estimated SNR as Side Information 
Sub Title (in English)  
Keyword(1) Modulation Classification  
Keyword(2) Convolutional Neural Network  
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1st Author's Name Yasutaka Yamashita  
1st Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
2nd Author's Name Shigeru Uchida  
2nd Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
3rd Author's Name Hiroshi Aruga  
3rd Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
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Speaker
Date Time 2019-11-26 10:30:00 
Presentation Time 50 
Registration for RISING 
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