A Deep Learning Approach to Universal Binary Visible Light Communication Transceiver
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hoon | - |
dc.contributor.author | Quek, Tony Q. S. | - |
dc.contributor.author | Lee, Sang Hyun | - |
dc.date.accessioned | 2021-08-31T11:38:39Z | - |
dc.date.available | 2021-08-31T11:38:39Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-02 | - |
dc.identifier.issn | 1536-1276 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/57890 | - |
dc.description.abstract | This paper studies a deep learning (DL) framework for the design of binary modulated visible light communication (VLC) transceiver with universal dimming support. The dimming control for the optical binary signal boils down to a combinatorial codebook design so that the average Hamming weight of binary codewords matches with arbitrary dimming target. An unsupervised DL technique is employed for obtaining a neural network to replace the encoder-decoder pair that recovers the message from the optically transmitted signal. In such a task, a novel stochastic binarization method is developed to generate the set of binary codewords from continuous-valued neural network outputs. For universal support of arbitrary dimming target, the DL-based VLC transceiver is trained with multiple dimming constraints, which turns out to be a constrained training optimization that is very challenging to handle with existing DL methods. We develop a new training algorithm that addresses the dimming constraints through a dual formulation of the optimization. Based on the developed algorithm, the resulting VLC transceiver can be optimized via the end-to-end training procedure. Numerical results verify that the proposed codebook outperforms theoretically best constant weight codebooks under various VLC setups. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | DESIGN | - |
dc.subject | NONLINEARITY | - |
dc.subject | MITIGATION | - |
dc.subject | SCHEME | - |
dc.title | A Deep Learning Approach to Universal Binary Visible Light Communication Transceiver | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Sang Hyun | - |
dc.identifier.doi | 10.1109/TWC.2019.2950026 | - |
dc.identifier.scopusid | 2-s2.0-85079793600 | - |
dc.identifier.wosid | 000522027400018 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, v.19, no.2, pp.956 - 969 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS | - |
dc.citation.title | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS | - |
dc.citation.volume | 19 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 956 | - |
dc.citation.endPage | 969 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | DESIGN | - |
dc.subject.keywordPlus | NONLINEARITY | - |
dc.subject.keywordPlus | MITIGATION | - |
dc.subject.keywordPlus | SCHEME | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Transceivers | - |
dc.subject.keywordAuthor | Optical transmitters | - |
dc.subject.keywordAuthor | Optical pulses | - |
dc.subject.keywordAuthor | Light emitting diodes | - |
dc.subject.keywordAuthor | Receivers | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Visible light communication | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | dimming support | - |
dc.subject.keywordAuthor | primal-dual method | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.