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| Presentation | 2015-10-27 09:30 [Technology Exhibit] Hardware Demonstration of Wi-Fi Fingerprint based Millimeter Wave Beamforming Ehab Mahmoud Mohamed (Osaka Univ.), Gia Khanh Tran, Kei Sakaguchi (Tokyo Tech), Seiichi Sampei (Osaka Univ.)  SR2015-62 SRW2015-43 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Millimeter wave (mmWave), especially the 60 GHz band, has been receiving much attention as a key enabler for the 5G cellular networks. Beamforming is tremendously used with mmWave transmissions to enhance the link quality and overcome the channel propagation loss. The current beamforming mechanism, proposed by the IEEE 802.11ad standard, is mainly based on exhaustive searching the best transmit (TX) and receive (RX) antenna beams. This beamforming mechanism requires a very high setup time, which makes it difficult to coordinate a multiple number of mmWave access points (APs) in mobile channel conditions as a 5G requirement. In this exhibition, we demonstrate a mmWave beamforming mechanism, which enables a mmWave AP to fast estimate the best beam to communicate with a user equipment (UE) using statistical learning. In this scheme, the fingerprints of the UE 5 GHz (Wi Fi) signal and 60 GHz (mmWave) best beam identification (ID) are collected in an offline phase on a grid of arbitrary learning points (LPs) in target environments. Therefore, by just comparing the current UE Wi-Fi signal with the pre-stored UE WiFi fingerprints, the mmWave AP can immediately estimate the best beam to communicate with the UE at its current position. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Millimeter Wave / Beamforming / Wi-Fi Fingerprints / Best Sector ID / Statistical Learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 115, no. 273, SR2015-62, pp. 59-60, Oct. 2015. | |
| Paper # | SR2015-62 | |
| Date of Issue | 2015-10-19 (SR, SRW) | |
| ISSN | Print edition: ISSN 0913-5685 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 | SR2015-62 SRW2015-43 |
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| Conference Information | |
| Committee | SR SRW |
| Conference Date | 2015-10-26 - 2015-10-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | KKE |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Technical Exhibition, Product Exhibition, etc. |
| Paper Information | |
| Registration To | SR |
| Conference Code | 2015-10-SR-SRW |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Hardware Demonstration of Wi-Fi Fingerprint based Millimeter Wave Beamforming |
| Sub Title (in English) | |
| Keyword(1) | Millimeter Wave |
| Keyword(2) | Beamforming |
| Keyword(3) | Wi-Fi Fingerprints |
| Keyword(4) | Best Sector ID |
| Keyword(5) | Statistical Learning |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Ehab Mahmoud Mohamed |
| 1st Author's Affiliation | Osaka University (Osaka Univ.) |
| 2nd Author's Name | Gia Khanh Tran |
| 2nd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 3rd Author's Name | Kei Sakaguchi |
| 3rd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 4th Author's Name | Seiichi Sampei |
| 4th Author's Affiliation | Osaka University (Osaka Univ.) |
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| Speaker | Author-1 |
| Date Time | 2015-10-27 09:30:00 |
| Presentation Time | 150 minutes |
| Registration for | SR |
| Paper # | SR2015-62, SRW2015-43 |
| Volume (vol) | vol.115 |
| Number (no) | no.273(SR), no.274(SRW) |
| Page | pp.59-60 |
| #Pages | 2 |
| Date of Issue | 2015-10-19 (SR, SRW) |
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