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| Paper Abstract and Keywords | ||
| Presentation | 2019-11-21 09:55 Deep Neural Network Based Distortion Compensation for Doherty Amplifiers Yoshimasa Egashira, Reina Hongyo, Keiichi Yamaguchi (Toshiba)  CQ2019-89 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The problem with Doherty amplifiers, which are high-efficiency power amplifiers, is the occurrence of complex memory distortion. In this paper, we focus on digital pre-distortion (DPD) using deep neural networks (DNN) as a technology to compensate for memory distortion of Doherty amplifiers with high accuracy and investigate the effects of the number of learning parameters and neuron activation functions on the compensation performance of DNN-DPD. As a result of the evaluation using actual GaN (Gallium Nitride) Doherty amplifier, it is shown that DNN-DPD can achieve suppression performance that exceeds Volterra series based DPD by increasing the number of learning parameters. Futhermore, in order to improve the distortion compensation performance of DNN-DPD, it is important to select an appropriate activation function according to the number of learning parameters and it is shown that the ReLU function is more suitable for the activation function of DNN-DPD with more than 2000 learning parameters compared to the conventional sigmoid function. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Digital predistortion / Deep neural network / Doherty amplifiers / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 298, CQ2019-89, pp. 7-12, Nov. 2019. | |
| Paper # | CQ2019-89 | |
| Date of Issue | 2019-11-14 (CQ) | |
| 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 | CQ2019-89 |
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| Conference Information | |
| Committee | NS ICM CQ NV |
| Conference Date | 2019-11-21 - 2019-11-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Rokkodai 2nd Campus, Kobe Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Network quality, Network measurement/management, Network virtualization, Network service, Blockchain, Security, Network intelligence, etc. |
| Paper Information | |
| Registration To | CQ |
| Conference Code | 2019-11-NS-ICM-CQ-NV |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Deep Neural Network Based Distortion Compensation for Doherty Amplifiers |
| Sub Title (in English) | |
| Keyword(1) | Digital predistortion |
| Keyword(2) | Deep neural network |
| Keyword(3) | Doherty amplifiers |
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| 1st Author's Name | Yoshimasa Egashira |
| 1st Author's Affiliation | Toshiba Corp. (Toshiba) |
| 2nd Author's Name | Reina Hongyo |
| 2nd Author's Affiliation | Toshiba Corp. (Toshiba) |
| 3rd Author's Name | Keiichi Yamaguchi |
| 3rd Author's Affiliation | Toshiba Corp. (Toshiba) |
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| Speaker | Author-1 |
| Date Time | 2019-11-21 09:55:00 |
| Presentation Time | 25 minutes |
| Registration for | CQ |
| Paper # | CQ2019-89 |
| Volume (vol) | vol.119 |
| Number (no) | no.298 |
| Page | pp.7-12 |
| #Pages | 6 |
| Date of Issue | 2019-11-14 (CQ) |
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