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dc.contributor.authorÜlkar, Mehmet Görkem
dc.contributor.authorBaykaş, Tunçer
dc.contributor.authorPusane, Ali Emre
dc.date.accessioned2020-10-30T07:52:51Z
dc.date.available2020-10-30T07:52:51Z
dc.date.issued2020en_US
dc.identifier.citationÜlkar, M. G., Baykaş, T. ve Pusane, A. E. (2020). VLCnet: Deep learning based end-to-end visible light communication system. Journal of Lightwave Technology, 38(21), 5937-5948. https://dx.doi.org/10.1109/JLT.2020.3006827en_US
dc.identifier.issn0733-8724
dc.identifier.issn1558-2213
dc.identifier.urihttps://dx.doi.org/10.1109/JLT.2020.3006827
dc.identifier.urihttps://hdl.handle.net/20.500.12511/5990
dc.description.abstractVisible light communication is a popular research area where proposed communication methods must satisfy the lighting related requirements as well. Suggested VLC modules should not only improve communication quality such as decreasing error rates but also comply with other lighting related constraints such as sustaining certain level of illumination. This increases the complexity of the optimization problem. Moreover, most of the time the suggested modules focus on a specific block of communication system which downgrades the system-wide performance on coming together. To solve this complex problem and jointly optimize the whole system, we suggest a deep learning based method, VLCnet. Despite the increasing number of neural network based channel decoders in the literature, few of them are addressing real-life application constraints. VLCnet is an error rate decreasing solution which takes into account, reducing flicker and sustaining certain illumination level. Moreover, our channel impulse response (CIR) is taken from reference CIRs for VLC and our study considers the input-dependent noise originated by the shot noise for the sake of generality. Flicker reducing activation units (FRAU) are the key part of VLCnet and the main novelty of this publication. FRAU is an example of a competitive layer and ensures run length limitation for flicker reduction. Both with input-independent and dependent noise, simulation results show performance superiority of the proposed VLCnet method. Although they have different setups, all results demonstrate the benefit of training with certain amount of noise. From the practicality perspective, proposed system is easy to be deployed since inference operation does not have iterations unlike most of the conventional detectors.en_US
dc.description.sponsorshipTurkish Academy of Sciencesen_US
dc.language.isoengen_US
dc.publisherIEEE-Institute of Electrical and Electronics Engineers Incen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectVisible Light Communicationen_US
dc.subjectDeep Learningen_US
dc.subjectLightingen_US
dc.subjectNeural Networksen_US
dc.subjectDecodingen_US
dc.subjectTrainingen_US
dc.subjectVisible Light Communicationsen_US
dc.subjectAutoencoderen_US
dc.subjectCompetitive Learningen_US
dc.subjectFlicker Reductionen_US
dc.subjectInput Dependent Noiseen_US
dc.titleVLCnet: Deep learning based end-to-end visible light communication systemen_US
dc.typearticleen_US
dc.relation.ispartofJournal of Lightwave Technologyen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0001-9535-2102en_US
dc.identifier.volume38en_US
dc.identifier.issue21en_US
dc.identifier.startpage5937en_US
dc.identifier.endpage5948en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/JLT.2020.3006827en_US
dc.identifier.wosqualityQ1en_US
dc.identifier.scopusqualityQ1en_US


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