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Paper Abstract and Keywords
Presentation 2026-04-17 14:20
Investigation of Convolutional Filters for Reducing Computational Complexity in CNN-DPD Training
Yudai Shiota (Kagoshima Univ.), Hiroto Sakaki (Mitsubishi Electric Corp.), Kenjiro Nishikawa (Kagoshima Univ.) WPT2026-5 MW2026-5
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a new method of neural network based digital predistortion (NN-DPD) to compensate for the distortions of the Power Amplifier (PA). NN-DPD is associated with high power consumption due to its network complexity and computational overhead. The real-valued time-delay convolutional neural network (RVTDCNN) method successfully reduces the number of parameters compared to conventional NN-DPD by employing convolutional kernels. However, the simultaneous convolutional computation of I/Q components possessing phase information and envelope-dependent terms possessing only amplitude information in an overlapping manner via kernels hinders the reduction of computational complexity. This paper proposes a method to separate these input datasets and process them using distinct convolutional kernels. Experimental results demonstrate that the proposed method achieves performance comparable to RVTDCNN while reducing the number of parameters by 29.38% and floating-point operations (FLOPs) by 27.42%.
Keyword (in Japanese) (See Japanese page) 
(in English) NN-DPD / neural network / power amplifier / digital predistortion / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 6, MW2026-5, pp. 25-30, April 2026.
Paper # MW2026-5 
Date of Issue 2026-04-10 (WPT, MW) 
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 WPT2026-5 MW2026-5

Conference Information
Committee WPT MW  
Conference Date 2026-04-17 - 2026-04-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Aiport Sado 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Wireless Power Transfer, Microwave 
Paper Information
Registration To MW 
Conference Code 2026-04-WPT-MW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigation of Convolutional Filters for Reducing Computational Complexity in CNN-DPD Training 
Sub Title (in English)  
Keyword(1) NN-DPD  
Keyword(2) neural network  
Keyword(3) power amplifier  
Keyword(4) digital predistortion  
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1st Author's Name Yudai Shiota  
1st Author's Affiliation Kagoshima University (Kagoshima Univ.)
2nd Author's Name Hiroto Sakaki  
2nd Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
3rd Author's Name Kenjiro Nishikawa  
3rd Author's Affiliation Kagoshima University (Kagoshima Univ.)
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Speaker Author-1 
Date Time 2026-04-17 14:20:00 
Presentation Time 25 minutes 
Registration for MW 
Paper # WPT2026-5, MW2026-5 
Volume (vol) vol.126 
Number (no) no.5(WPT), no.6(MW) 
Page pp.25-30 
#Pages 6 
Date of Issue 2026-04-10 (WPT, MW) 

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