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| Paper Abstract and Keywords | ||
| Presentation | 2020-11-18 10:00 A Study on Forecasting of Available Spectrum Resources for Sharing using Envelope Extraction and Machine Learning Tatsuya Nagao, Takahiro Hayashi, Yoshiaki Amano (KDDI Research)  SR2020-22 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, the demand for spectrum resources has been increasing, which has raised expectations for dynamic spectrum access (DSA). DSA is a technique for allowing secondary systems to use the same spectrum band at locations and times when the primary system is not used. DSA is a technique for improving the spectrum utilization efficiency while avoiding interference between systems, by allowing secondary systems to use the same spectrum at locations and times when the primary system is not in use. Especially in the millimeter-wave band, which is expected to be used more in the future, it is greatly affected by the surrounding environment, such as the number of people and vehicles. As a result, the coverage of radio waves varies depending on environmental changes. By considering those variations, it is possible to detect additional spectrum resources that will be newly available to the secondary system. In this paper, we propose a method for predicting the received power of a primary system that varies due to environmental changes and detecting the future available spectrum resources by machine learning, and show the results of the simulation evaluation. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Dynamic Spectrum Access / Machine Learning / Time-series analysis / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 238, SR2020-22, pp. 1-4, Nov. 2020. | |
| Paper # | SR2020-22 | |
| Date of Issue | 2020-11-11 (SR) | |
| 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 | SR2020-22 |
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| Conference Information | |
| Committee | SR |
| Conference Date | 2020-11-18 - 2020-11-20 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Software Radio, Cognitive Radio, Spectrum Sharing, etc. |
| Paper Information | |
| Registration To | SR |
| Conference Code | 2020-11-SR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Forecasting of Available Spectrum Resources for Sharing using Envelope Extraction and Machine Learning |
| Sub Title (in English) | |
| Keyword(1) | Dynamic Spectrum Access |
| Keyword(2) | Machine Learning |
| Keyword(3) | Time-series analysis |
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| 1st Author's Name | Tatsuya Nagao |
| 1st Author's Affiliation | KDDI Research, Inc. (KDDI Research) |
| 2nd Author's Name | Takahiro Hayashi |
| 2nd Author's Affiliation | KDDI Research, Inc. (KDDI Research) |
| 3rd Author's Name | Yoshiaki Amano |
| 3rd Author's Affiliation | KDDI Research, Inc. (KDDI Research) |
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| Speaker | Author-1 |
| Date Time | 2020-11-18 10:00:00 |
| Presentation Time | 25 minutes |
| Registration for | SR |
| Paper # | SR2020-22 |
| Volume (vol) | vol.120 |
| Number (no) | no.238 |
| Page | pp.1-4 |
| #Pages | 4 |
| Date of Issue | 2020-11-11 (SR) |
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