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| Presentation | 2020-11-27 11:50 [Invited Lecture] Two-step Path Loss Prediction Method by Artificial Neural Network for Wireless Service Area Planning Kentaro Saito, Yongri JIN, CheChia Kang, Jun-ichi Takada (Tokyo Tech.)  SRW2020-38 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, wireless network systems are utilized in various industry fields and the wireless service area planning became one of the important tasks to realize efficient and high-quality wireless communication service. The machine learning technology attracts the interests of researchers to improve the efficiency of the area planning task because the radio propagation loss in unknown locations can be predicted by the training data without explicit algorithms. Our previous work showed that the path loss (PL) characteristics become complicated in the high PL region, and it can degrade the entire prediction accuracy. In this paper, we propose the two-step PL prediction method by the artificial neural network (ANN) to solve the issue. Firstly, the area is classified into several zones according to the PL range. And then the PL is predicted by ANNs that were trained for respective zones. Our proposal was evaluated by the ray-tracing simulation data, and the result showed that it improved the root mean square error (RMSE) of PL prediction from 7.9 dB to 4.1 dB. The method is expected to be utilized for the wireless service area planning in various environments. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Cell planning / Indoor propagation / Machine learning / Neural network / Path loss prediction / propagation simulation / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 260, SRW2020-38, pp. 61-66, Nov. 2020. | |
| Paper # | SRW2020-38 | |
| Date of Issue | 2020-11-19 (SRW) | |
| 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 | SRW2020-38 |
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| Conference Information | |
| Committee | SRW SeMI CNR |
| Conference Date | 2020-11-26 - 2020-11-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | IoT Workshop |
| Paper Information | |
| Registration To | SRW |
| Conference Code | 2020-11-SRW-SeMI-CNR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Two-step Path Loss Prediction Method by Artificial Neural Network for Wireless Service Area Planning |
| Sub Title (in English) | |
| Keyword(1) | Cell planning |
| Keyword(2) | Indoor propagation |
| Keyword(3) | Machine learning |
| Keyword(4) | Neural network |
| Keyword(5) | Path loss prediction |
| Keyword(6) | propagation simulation |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Kentaro Saito |
| 1st Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech.) |
| 2nd Author's Name | Yongri JIN |
| 2nd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech.) |
| 3rd Author's Name | CheChia Kang |
| 3rd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech.) |
| 4th Author's Name | Jun-ichi Takada |
| 4th Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech.) |
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| Speaker | Author-1 |
| Date Time | 2020-11-27 11:50:00 |
| Presentation Time | 20 minutes |
| Registration for | SRW |
| Paper # | SRW2020-38 |
| Volume (vol) | vol.120 |
| Number (no) | no.260 |
| Page | pp.61-66 |
| #Pages | 6 |
| Date of Issue | 2020-11-19 (SRW) |
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