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| Paper Abstract and Keywords | ||
| Presentation | 2023-06-12 11:10 [Invited Lecture] Channel Prediction for Overhead Reduction of Channel Estimation in IRS-Assisted Wireless Communication Systems Norisato Suga (ATR/SIT), Kazuto Yano (ATR), Yafei Hou (ATR/Okayama Univ.), Toshikazu Sakano (ATR)  SRW2023-4 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The use of intelligent reflecting surface (IRS), which is a surface arrangement of elements that can control the phase of radio waves and reflect them, is being investigated for wireless communication in high frequency bands. In order to control the reflection characteristic, it is necessary to estimate the channel coefficient through each IRS element for a large number of reflective elements, transmitting, and receiving antennas. This causes significant overhead for the channel estimation. In this study, we propose a channel prediction method to reduce the overhead using Gaussian process regression with spectral mixture kernel. In Gaussian process regression, the determination of the hyperparameters used to calculate the kernel matrix has a significant impact on prediction accuracy. In this study, we evaluate the performance difference between the gradient method and validation for the hyperparameters determination. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | IRS / RIS / channel prediction / Gaussian process regression / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 75, SRW2023-4, pp. 19-24, June 2023. | |
| Paper # | SRW2023-4 | |
| Date of Issue | 2023-06-05 (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 | SRW2023-4 |
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| Conference Information | |
| Committee | SRW |
| Conference Date | 2023-06-12 - 2023-06-12 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kikai-Shinko-Kaikan Bldg. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | WLAN， WPAN， Ad-hoc, mmWave, etc. |
| Paper Information | |
| Registration To | SRW |
| Conference Code | 2023-06-SRW |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Channel Prediction for Overhead Reduction of Channel Estimation in IRS-Assisted Wireless Communication Systems |
| Sub Title (in English) | |
| Keyword(1) | IRS |
| Keyword(2) | RIS |
| Keyword(3) | channel prediction |
| Keyword(4) | Gaussian process regression |
| Keyword(5) | |
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| 1st Author's Name | Norisato Suga |
| 1st Author's Affiliation | Advanced Telecommunications Research Institute International/Shibaura Institute of Technology (ATR/SIT) |
| 2nd Author's Name | Kazuto Yano |
| 2nd Author's Affiliation | Advanced Telecommunications Research Institute International (ATR) |
| 3rd Author's Name | Yafei Hou |
| 3rd Author's Affiliation | Advanced Telecommunications Research Institute International/Okayama University (ATR/Okayama Univ.) |
| 4th Author's Name | Toshikazu Sakano |
| 4th Author's Affiliation | Advanced Telecommunications Research Institute International (ATR) |
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| Speaker | Author-1 |
| Date Time | 2023-06-12 11:10:00 |
| Presentation Time | 25 minutes |
| Registration for | SRW |
| Paper # | SRW2023-4 |
| Volume (vol) | vol.123 |
| Number (no) | no.75 |
| Page | pp.19-24 |
| #Pages | 6 |
| Date of Issue | 2023-06-05 (SRW) |
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