Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2023-07-28 14:10
Impact of Learning Models on Deep Joint Source Channel Coding Adaptable to 5G Systems
Ryunosuke Yamamoto, Keigo Matsumoto, Yoshiaki Inoue (Osaka Univ.), Yuko Hara-Azumi (Tokyo Tech), Kazuki Maruta (TUS), Yu Nakayama (TUAT), Daisuke Hisano (Osaka Univ.) CS2023-56
Abstract (in Japanese) (See Japanese page) 
(in English) With the development of 5G technology and the proliferation of IoT devices, Deep Joint Source-Channel Coding (Deep JSCC), which can efficiently transmit video and image data, has been attracting attention.
Deep JSCC has been reported to maintain good peak signal-to-noise power ratio (PSNR) of images even when the signal-to-noise power ratio (SNR) is very low.
When this Deep JSCC is used in a cellular communication system, the compression ratio must be adaptively varied according to the fluctuations of the communication channel.
In other words, cellular base stations need to be equipped with multiple learning models.
However, to the best of the author's knowledge, the optimal combination of SNR and compression ratio during learning has not yet been reported.
Therefore, this paper investigates the number of learning models that a cellular base station needs to have by varying the SNR and compression ratio during training in a stepwise manner.
Keyword (in Japanese) (See Japanese page) 
(in English) 5G / Joint Source-Channel Coding / Deep Learning / Semantic Communication / Image Transmission / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 137, CS2023-56, pp. 151-156, July 2023.
Paper # CS2023-56 
Date of Issue 2023-07-20 (CS) 
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 CS2023-56

Conference Information
Committee CS  
Conference Date 2023-07-27 - 2023-07-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Hachijo-machi Chamber of Commerce and Industry 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Next Generation Networks, Access Networks, Broadband Access, Power Line Communications, Wireless Communication Systems, Coding Systems, etc. 
Paper Information
Registration To CS 
Conference Code 2023-07-CS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Impact of Learning Models on Deep Joint Source Channel Coding Adaptable to 5G Systems 
Sub Title (in English)  
Keyword(1) 5G  
Keyword(2) Joint Source-Channel Coding  
Keyword(3) Deep Learning  
Keyword(4) Semantic Communication  
Keyword(5) Image Transmission  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Ryunosuke Yamamoto  
1st Author's Affiliation OsakaUniversity (Osaka Univ.)
2nd Author's Name Keigo Matsumoto  
2nd Author's Affiliation OsakaUniversity (Osaka Univ.)
3rd Author's Name Yoshiaki Inoue  
3rd Author's Affiliation OsakaUniversity (Osaka Univ.)
4th Author's Name Yuko Hara-Azumi  
4th Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
5th Author's Name Kazuki Maruta  
5th Author's Affiliation Tokyo University of Science (TUS)
6th Author's Name Yu Nakayama  
6th Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
7th Author's Name Daisuke Hisano  
7th Author's Affiliation OsakaUniversity (Osaka Univ.)
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2023-07-28 14:10:00 
Presentation Time 20 minutes 
Registration for CS 
Paper # CS2023-56 
Volume (vol) vol.123 
Number (no) no.137 
Page pp.151-156 
#Pages 6 
Date of Issue 2023-07-20 (CS) 

[Return to Top Page]

[Return to IEICE Web Page]
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan