Paper Abstract and Keywords |
Presentation |
2022-06-17 10:35
A Study on Data-Driven Fine-Tuning for ICA-Aided Blind Signal Separation Taisuke Nogami, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Hisato Iwai (Doshisha Univ.) RCS2022-61 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Internet of Things (IoT) communications, which will plays an important role in the next-generation wireless communications infrastructure, will support massive connection requests, while packet sizes tend to be small.
MIMO (Multi-Input Multi-Output) transmission, which enables spatial multiplexing of signals at the same time and frequency, is a promising method to enable the large-scale connections. Generally, to separate spatially multiplexed signals, channel state information (CSI) is estimated by using pilot signals, and spatial filtering is applied based on the estimated CSI. However, it is undesirable from the viewpoint of transmission efficiency to add long pilot sequences to each small data packets. In such a situation, independent component analysis (ICA), a type of blind signal separation (BSS) technology, can be applied; however, ICA requires evaluations of non-Gaussianity and a relatively long data sequence for this estimation, so high detection accuracy cannot be expected even if ICA is applied directly to small packet size.
In this paper, we apply Deep Unfolding (DU), a type of deep learning, and improve the signal separation performance of ICA with relatively short data series length. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
ICA / deep learning / deep unfolding / data-driven tuning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 73, RCS2022-61, pp. 218-223, June 2022. |
Paper # |
RCS2022-61 |
Date of Issue |
2022-06-08 (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) |
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RCS2022-61 |
Conference Information |
Committee |
RCS |
Conference Date |
2022-06-15 - 2022-06-17 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
University of the Ryukyus, Senbaru Campus and online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
First Presentation in IEICE Technical Committee, Resource Control, Scheduling, Wireless Communications, etc. |
Paper Information |
Registration To |
RCS |
Conference Code |
2022-06-RCS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study on Data-Driven Fine-Tuning for ICA-Aided Blind Signal Separation |
Sub Title (in English) |
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ICA |
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deep learning |
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deep unfolding |
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data-driven tuning |
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1st Author's Name |
Taisuke Nogami |
1st Author's Affiliation |
Doshisha University (Doshisha Univ.) |
2nd Author's Name |
Shinsuke Ibi |
2nd Author's Affiliation |
Doshisha University (Doshisha Univ.) |
3rd Author's Name |
Takumi Takahashi |
3rd Author's Affiliation |
Osaka University (Osaka Univ.) |
4th Author's Name |
Hisato Iwai |
4th Author's Affiliation |
Doshisha University (Doshisha Univ.) |
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Speaker |
Author-1 |
Date Time |
2022-06-17 10:35:00 |
Presentation Time |
10 minutes |
Registration for |
RCS |
Paper # |
RCS2022-61 |
Volume (vol) |
vol.122 |
Number (no) |
no.73 |
Page |
pp.218-223 |
#Pages |
6 |
Date of Issue |
2022-06-08 (RCS) |
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