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Paper Abstract and Keywords
Presentation 2019-10-03 13:05
[Invited Lecture] Deep Learning Based Communication Environment Estimation Using Received Signals
Kazuki Maruta, Shun Kojima, Chang-Jun Ahn (Chiba Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) The performance of wireless communication greatly depends on the propagation environment.
Adaptive modulation and coding (AMC) is essential to adapt to each environment.
Parameters representing the communication environment include SNR, Doppler shift, line of sight/multipath components, and interference.
Such information should be estimated by individual signal processing using known reference signals.
If all information can be estimated instantaneously without use of known signals, channel capacity can be efficiently and maximally utilized by AMC and interference cancellation.
Deep learning is applied to resolve these issues.
By extracting various features from the received signal, information about communication environments can be estimated simultaneously.
This paper presents our recent works, deep learning aided communication environment estimation, and proposes a approach that can improve the estimation accuracy by giving additional processing to the received signal.
Keyword (in Japanese) (See Japanese page) 
(in English) Adaptive modulation / Noise estimation / Interference estimation / Doppler frequency / Deep learning / / /  
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 MIKA  
Conference Date 2019-10-02 - 2019-10-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Wireless Communication System, etc. 
Paper Information
Registration To MIKA 
Conference Code 2019-10-MIKA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Learning Based Communication Environment Estimation Using Received Signals 
Sub Title (in English)  
Keyword(1) Adaptive modulation  
Keyword(2) Noise estimation  
Keyword(3) Interference estimation  
Keyword(4) Doppler frequency  
Keyword(5) Deep learning  
1st Author's Name Kazuki Maruta  
1st Author's Affiliation Chiba University (Chiba Univ.)
2nd Author's Name Shun Kojima  
2nd Author's Affiliation Chiba University (Chiba Univ.)
3rd Author's Name Chang-Jun Ahn  
3rd Author's Affiliation Chiba University (Chiba Univ.)
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Date Time 2019-10-03 13:05:00 
Presentation Time 30 
Registration for MIKA 
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