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| Presentation | 2021-01-22 15:15 A Study on the Possibility of Estimating Multiple Communication Environment Information by Deep Learning Method Using Received Signal Spectrogram Shun Kojima (Chiba Univ.), Kazuki Maruta (Tokyo Tech.), Chang-Jun Ahn (Chiba Univ.)  IT2020-97 SIP2020-75 RCS2020-188 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In the next generation mobile radio communication systems, it is essential to obtain the communication environment information accurately and quickly in order to implement appropriate control such as adaptive modulation and coding for realizing high-speed, high-capacity and low-delay communication. SNR, Doppler shift, and $K$-factor are some of the communication environment parameters that have a significant impact on the performance of adaptive modulation and coding. In the past, it has been difficult to introduce these parameters into adaptive modulation and coding for high-speed and large-capacity communications because the estimation of these parameters requires a huge amount of computation, a reference signal, and large-scale signal sampling. In this paper, we propose a method for estimating these multiple communication environment parameters on a per-packet basis without using reference signals by using convolutional neural networks from spectrogram images of the received signal. From the simulation results, we clarify the effectiveness of the proposed method in terms of the estimation accuracy of SNR, Doppler shift, and $K$-factor when they are estimated independently and the estimation accuracy when these three parameters are estimated simultaneously. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Spectrogram / CNN / SNR estimation / Doppler shift estimation / K factor estimation / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 322, RCS2020-188, pp. 188-193, Jan. 2021. | |
| Paper # | RCS2020-188 | |
| Date of Issue | 2021-01-14 (IT, SIP, RCS) | |
| 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 | IT2020-97 SIP2020-75 RCS2020-188 |
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| Conference Information | |
| Committee | SIP IT RCS |
| Conference Date | 2021-01-21 - 2021-01-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2021-01-SIP-IT-RCS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on the Possibility of Estimating Multiple Communication Environment Information by Deep Learning Method Using Received Signal Spectrogram |
| Sub Title (in English) | |
| Keyword(1) | Spectrogram |
| Keyword(2) | CNN |
| Keyword(3) | SNR estimation |
| Keyword(4) | Doppler shift estimation |
| Keyword(5) | K factor estimation |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shun Kojima |
| 1st Author's Affiliation | Chiba University (Chiba Univ.) |
| 2nd Author's Name | Kazuki Maruta |
| 2nd Author's Affiliation | Tokyo Institute and Technology (Tokyo Tech.) |
| 3rd Author's Name | Chang-Jun Ahn |
| 3rd Author's Affiliation | Chiba University (Chiba Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-01-22 15:15:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | IT2020-97, SIP2020-75, RCS2020-188 |
| Volume (vol) | vol.120 |
| Number (no) | no.320(IT), no.321(SIP), no.322(RCS) |
| Page | pp.188-193 |
| #Pages | 6 |
| Date of Issue | 2021-01-14 (IT, SIP, RCS) |
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