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Deep Learning-Assisted Index Estimator for Generalized LED Index Modulation OFDM in Visible Light Communication

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Abstract
In this letter, we present the first attempt of active light-emitting diode (LED) indexes estimating for the generalized LED index modulation optical orthogonal frequency-division multiplexing (GLIM-OFDM) in visible light communication (VLC) system by using deep learning (DL). Instead of directly estimating the transmitted binary bit sequence with DL, the active LEDs at the transmitter are estimated to maintain acceptable complexity and improve the performance gain compared with those of previously proposed receivers. Particularly, a novel DL-based estimator termed index estimator-based deep neural network (IE-DNN) is proposed, which can employ three different DNN structures with fully connected layers (FCL) or convolution layers (CL) to recover the indexes of active LEDs in a GLIM-OFDM system. By using the received signal dataset generated in simulations, the IE-DNN is first trained offline to minimize the index error rate (IER); subsequently, the trained model is deployed for the active LED index estimation and signal demodulation of the GLIM-OFDM system. The simulation results show that the IE-DNN significantly improves the IER and bit error rate (BER) compared with those of conventional detectors with acceptable run time.
Author(s)
레 쩐 만김성환
Issued Date
2021
Type
Article
Keyword
bit error ratedeep learningindex modulationorthogonal frequency division multiplexingvisible light communications
DOI
10.3390/photonics8050168
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9067
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_f2eb194e77fb40f2833ae4f0f897162a&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Deep%20Learning-Assisted%20Index%20Estimator%20for%20Generalized%20LED%20Index%20Modulation%20OFDM%20in%20Visible%20Light%20Communication&offset=0&pcAvailability=true
Publisher
PHOTONICS
Location
스위스
Language
영어
ISSN
2304-6732
Citation Volume
8
Citation Number
5
Citation Start Page
168
Citation End Page
168
Appears in Collections:
Engineering > IT Convergence
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