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| Presentation | 2022-03-04 09:55 Low-overhead Beam and Power Allocation Using Deep Learning for mmWave Networks Yuwen Cao, Tomoaki Ohtsuki (Keio Univ.)  RCS2021-284 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this report, we develop a novel deep learning (DL)-based hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks for facilitating a fast beamforming at the base station (BS). Notably, the challenge involved in mmWave networks lies in that: (i) user mobility, as well as frequent beam reselections, render degraded mmWave communication performance in terms of reliability and throughput; (ii) users who are geographically co-located together may render serve beam conflicts thus deteriorating mmWave communication performance; (iii) existing DL-based methods predict the beamforming matrix that in fact can not be well-suited to the underlying channel distribution as the beamforming dimension at BS is large. Motivated by this, we investigate low-overhead beam and power allocation by using the DL technology. To this end, we first develop a novel beam-quality prediction model to predict the high-resolution beam energy images by exploiting the DL and super-resolution technologies. Afterward, we develop a DL-based beam and power allocation approach which enables high allocation accuracy with only a portion of $s$ time-sequential low-resolution beam images. Simulation results show that our proposed approach guarantees sub-optimal throughput performance with low-overhead in relative to the counterpart approaches. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep learning / beam and power allocation / multiuser mmWave networks / super-resolution / low-overhead / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 391, RCS2021-284, pp. 159-163, March 2022. | |
| Paper # | RCS2021-284 | |
| Date of Issue | 2022-02-23 (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 | RCS2021-284 |
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| Conference Information | |
| Committee | RCS SR SRW |
| Conference Date | 2022-03-02 - 2022-03-04 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2022-03-RCS-SR-SRW |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Low-overhead Beam and Power Allocation Using Deep Learning for mmWave Networks |
| Sub Title (in English) | |
| Keyword(1) | Deep learning |
| Keyword(2) | beam and power allocation |
| Keyword(3) | multiuser mmWave networks |
| Keyword(4) | super-resolution |
| Keyword(5) | low-overhead |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Yuwen Cao |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Tomoaki Ohtsuki |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-03-04 09:55:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCS2021-284 |
| Volume (vol) | vol.121 |
| Number (no) | no.391 |
| Page | pp.159-163 |
| #Pages | 5 |
| Date of Issue | 2022-02-23 (RCS) |
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