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| Presentation | 2021-11-10 15:55 Channel Parameter Estimation by using Environmental Features Inocent Calist, Zhiqiang Li, Minseok Kim (Niigata Univ.)  AP2021-106 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recent developments in the next generation of mobile communication and the application of the Internet of things has raised the need to develop more accurate channel models. This work presents the development of a supervised based machine learning (ML) prediction model for large scale channel parameters (LSCPs) estimation by analyzing the reflected multipath ray's information. The reflected rays varies with the morphology structure of the propagation environment, hence a dynamic LSCPs predictive model can be realized. The input parameters to the prediction model are transmitter (TX) and receiver (RX) positional coordinates, and the reflected rays' information such as the delay, angle of arrival, angle of departure, elevation angle of arrival, elevation angle of departure, and power gain. The proposed model was implemented using Random Forest (RF) which can predict both linear and nonlinear data. Ray tracing (RT) simulation was performed to calculate the input measurement dataset of the LSCPs, and the input information of the reflected rays. Cross validation was then utilized to validate the model. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Machine learning / parameter estimation / channel / rays information / prediction model / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 233, AP2021-106, pp. 34-38, Nov. 2021. | |
| Paper # | AP2021-106 | |
| Date of Issue | 2021-11-03 (AP) | |
| 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 | AP2021-106 |
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| Conference Information | |
| Committee | AP RCS |
| Conference Date | 2021-11-10 - 2021-11-12 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | NBC-Bekkan (Nagasaki) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Adaptive Antenna, Equalization, Interference Canceler, MIMO, Wireless Communications, etc. |
| Paper Information | |
| Registration To | AP |
| Conference Code | 2021-11-AP-RCS |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Channel Parameter Estimation by using Environmental Features |
| Sub Title (in English) | |
| Keyword(1) | Machine learning |
| Keyword(2) | parameter estimation |
| Keyword(3) | channel |
| Keyword(4) | rays information |
| Keyword(5) | prediction model |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Inocent Calist |
| 1st Author's Affiliation | Niigata University (Niigata Univ.) |
| 2nd Author's Name | Zhiqiang Li |
| 2nd Author's Affiliation | Niigata University (Niigata Univ.) |
| 3rd Author's Name | Minseok Kim |
| 3rd Author's Affiliation | Niigata University (Niigata Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-11-10 15:55:00 |
| Presentation Time | 25 minutes |
| Registration for | AP |
| Paper # | AP2021-106 |
| Volume (vol) | vol.121 |
| Number (no) | no.233 |
| Page | pp.34-38 |
| #Pages | 5 |
| Date of Issue | 2021-11-03 (AP) |
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