dc.citation.conferencePlace |
US |
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dc.citation.conferencePlace |
Waikoloa, HI, USA |
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dc.citation.title |
2019 IEEE Globecom Workshops (GC Wkshps) |
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dc.contributor.author |
Lee, Hoon |
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dc.contributor.author |
Quek, Tony Q. S. |
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dc.contributor.author |
Lee, Sang Hyun |
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dc.date.accessioned |
2024-01-31T23:09:07Z |
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dc.date.available |
2024-01-31T23:09:07Z |
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dc.date.created |
2023-09-19 |
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dc.date.issued |
2019-12-09 |
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dc.description.abstract |
This paper investigates a deep learning (DL) method for on-off keying (OOK) based visible light communication (VLC) systems where a lighting emitting diode transmits binary optical pulses to a receiver. Universal dimming abilities are considered such that the VLC transceiver meets arbitrary dimming requirement of external users. This poses a combinatorial formulation optimizing binary codewords under multiple dimming constraints. To tackle this, DL techniques are employed to design an OOK encoder-decoder pair over noisy optical channels. For universal dimming support, the training of the DL-based VLC transceiver turns out to be a constrained training problem with multiple dimming constraints. This paper employs a dual formulation to develop a constrained training strategy. Numerical results show the effectiveness of the proposed transceiver design. |
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dc.identifier.bibliographicCitation |
2019 IEEE Globecom Workshops (GC Wkshps) |
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dc.identifier.doi |
10.1109/gcwkshps45667.2019.9024576 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/78692 |
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dc.publisher |
IEEE |
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dc.title |
Deep Learning-Aided Binary Visible Light Communication Systems |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2019-12-09 |
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