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Paper Abstract and Keywords
Presentation 2019-10-24 16:55
[Invited Talk] Deep Learning for Physical-Layer 5G Wireless Techniques
Guan Gui (NJUPT) RCS2019-191
Abstract (in Japanese) (See Japanese page) 
(in English) The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the current communication systems, which were designed on the basis of conventional communication theories, significantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learning-based communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We vision that the appealing deep learning-based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep learning / 5G communications / non-orthogonal multiple access / massive MIMO / millimeter wave / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 244, RCS2019-191, pp. 71-76, Oct. 2019.
Paper # RCS2019-191 
Date of Issue 2019-10-17 (RCS) 
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 RCS2019-191

Conference Information
Committee RCS  
Conference Date 2019-10-24 - 2019-10-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Yokosuka Research Park 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Wireless Communication Schemes, Wireless Communication Systems, Wireless Standards, Future Wireless Systems, etc. 
Paper Information
Registration To RCS 
Conference Code 2019-10-RCS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Learning for Physical-Layer 5G Wireless Techniques 
Sub Title (in English)  
Keyword(1) Deep learning  
Keyword(2) 5G communications  
Keyword(3) non-orthogonal multiple access  
Keyword(4) massive MIMO  
Keyword(5) millimeter wave  
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1st Author's Name Guan Gui  
1st Author's Affiliation Nanjing University of Posts and Telecommunications (NJUPT)
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Speaker Author-1 
Date Time 2019-10-24 16:55:00 
Presentation Time 50 minutes 
Registration for RCS 
Paper # RCS2019-191 
Volume (vol) vol.119 
Number (no) no.244 
Page pp.71-76 
#Pages 6 
Date of Issue 2019-10-17 (RCS) 

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