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Paper Abstract and Keywords
Presentation 2023-06-15 16:25
Neural Network-Based Digital Pre-Distortion Using Same Coefficients for Compensating Frequency Band Dependent Nonlinearities of a Power Amplifier
Ao Yamashita, Hiroto Sakaki, Hideyuki Nakamizo (Mitsubishi Electric Corporation) RCS2023-61
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a neural network (NN) based digital pre-distortion (DPD) using same coefficients for compensating frequency band dependent nonlinearities of a power amplifier (PA). Wideband PAs which can operate in many frequency bands are being developed for low-cost 4G/5G base stations. Generally, PAs are used with DPD in the base stations. When PA is common to frequency bands, DPD is desirable to be common. However, the nonlinearities of a PA depend on the frequency bands. To compensate the different nonlinearities with DPD, it is necessary to recalculate coefficients with high computational cost or to keep many coefficients. The proposed NN has a frequency band information input to represent frequency band dependent nonlinearities. The input can be used as a variable to compensate for the nonlinearities. We experimented with the proposed NN for compensating 3 frequency bands dependent nonlinearities. Experimental results show that the same adjacent channel power ratio (ACPR) and error vector magnitude (EVM) were achieved as the conventional NN. In addition, the number of coefficients was 28% less than the conventional ones.
Keyword (in Japanese) (See Japanese page) 
(in English) Digital pre-distortion / Neural network / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 76, RCS2023-61, pp. 194-199, June 2023.
Paper # RCS2023-61 
Date of Issue 2023-06-07 (RCS) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee RCS  
Conference Date 2023-06-14 - 2023-06-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido University, and online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) First Presentation in IEICE Technical Committee, Resource Control, Scheduling, Wireless Communications, etc. 
Paper Information
Registration To RCS 
Conference Code 2023-06-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Neural Network-Based Digital Pre-Distortion Using Same Coefficients for Compensating Frequency Band Dependent Nonlinearities of a Power Amplifier 
Sub Title (in English)  
Keyword(1) Digital pre-distortion  
Keyword(2) Neural network  
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1st Author's Name Ao Yamashita  
1st Author's Affiliation Information Technology R&D Center, Mitsubishi Electric Corporation (Mitsubishi Electric Corporation)
2nd Author's Name Hiroto Sakaki  
2nd Author's Affiliation Information Technology R&D Center, Mitsubishi Electric Corporation (Mitsubishi Electric Corporation)
3rd Author's Name Hideyuki Nakamizo  
3rd Author's Affiliation Information Technology R&D Center, Mitsubishi Electric Corporation (Mitsubishi Electric Corporation)
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Speaker Author-1 
Date Time 2023-06-15 16:25:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2023-61 
Volume (vol) vol.123 
Number (no) no.76 
Page pp.194-199 
#Pages
Date of Issue 2023-06-07 (RCS) 


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