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Paper Abstract and Keywords
Presentation 2019-10-17 12:30
[Poster Presentation] Modulation estimation using deep learning in multi-beam massive MIMO
Ryotaro Taniguchi, Kenatro Nishimori, Tsuyoshi Ohta (Niigata Univ.) AP2019-83
Abstract (in Japanese) (See Japanese page) 
(in English) machine learning has attracted attention in various research fields. Machine learning is an algorithm and statistical model for performing specific tasks efficiently without relying on explicit support and relying on patterns and inferences. Machine learning has a wide range of applications such as natural language processing, speech recognition, and bioinformatics. On the other hand, we proposed and evaluated multi-beam massive MIMO which forms an analog multi-beam at the base station and applies the Constant Modulus Algorithm (CMA) as digital signal processing to the received signal. Since the phase of the received signal of multi-beam Massive MIMO after CMA application is rotating, it is difficult to estimate and demodulate the modulation method. The authors have already proposed a method for correcting the phase of the rotated signal, but information about the modulation method of the pilot and the received signal is required for the correction. In this report, we use the convolutional neural network (CNN), which is one of machine learning, to estimate the modulation scheme of the received signal of multi-beam massive MIMO and verify its basic performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / Massive MIMO / Modulation method / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 228, AP2019-83, pp. 19-24, Oct. 2019.
Paper # AP2019-83 
Date of Issue 2019-10-10 (AP) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF AP2019-83

Conference Information
Committee AP  
Conference Date 2019-10-17 - 2019-10-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Student Session, Antennas and Propagation 
Paper Information
Registration To AP 
Conference Code 2019-10-AP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Modulation estimation using deep learning in multi-beam massive MIMO 
Sub Title (in English)  
Keyword(1) Machine learning  
Keyword(2) Massive MIMO  
Keyword(3) Modulation method  
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1st Author's Name Ryotaro Taniguchi  
1st Author's Affiliation Niigata University (Niigata Univ.)
2nd Author's Name Kenatro Nishimori  
2nd Author's Affiliation Niigata University (Niigata Univ.)
3rd Author's Name Tsuyoshi Ohta  
3rd Author's Affiliation Niigata University (Niigata Univ.)
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Speaker Author-1 
Date Time 2019-10-17 12:30:00 
Presentation Time 115 minutes 
Registration for AP 
Paper # AP2019-83 
Volume (vol) vol.119 
Number (no) no.228 
Page pp.19-24 
#Pages 6 
Date of Issue 2019-10-10 (AP) 

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