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Extreme learning machines as equalizers on optical OFDM systems

dc.contributor.authorMascaró-Muñoz, Agustín
dc.contributor.authorAhumada García, Roberto
dc.contributor.authorZabala-Blanco, David
dc.contributor.authorAzurdia-Meza, César A.
dc.contributor.authorSoto, Ismael
dc.contributor.authorPalacios Játiva, Pablo
dc.date.accessioned2023-10-25T13:08:05Z
dc.date.available2023-10-25T13:08:05Z
dc.date.issued2023
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/5039
dc.description.abstractOptical Fiber Radio (RoF) systems based on OFDM meet the needs of high transmission and reception speeds, as well as offering greater reliability in the system. These systems are exposed to various disturbances, such as the thermal and shot noise of the photodetector, the amplified emission of optical links, and the relative phase intensity in the optical oscillator. To partially address these drawbacks, techniques such as multi-carrier modulation (OFDM), pilot-assisted equalization (PAE), and typical filters have been used. Recently, Extreme Learning Machines (ELM) have been employed instead of classic digital signal processing in RoF-OFDM systems to tackle physical limitations. ELMs are learning algorithms that have low latency rates and the ability to process large volumes of data. This article presents a review and comparison of the main research studies that have utilized ELM. It should be noted that ELM-C achieved the shortest equalization time in most cases compared to other algorithms.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceIEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), 2023, 1-6es_CL
dc.subjectOFDMes_CL
dc.subjectAdaptive opticses_CL
dc.subjectIntegrated opticses_CL
dc.subjectBit error ratees_CL
dc.subjectStimulated emissiones_CL
dc.subjectOptical signal processinges_CL
dc.subjectOptical noisees_CL
dc.titleExtreme learning machines as equalizers on optical OFDM systemses_CL
dc.typeArticlees_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.uriieeexplore.ieee.org/document/10226069/authors#authorses_CL
dc.ucm.doidoi.org/10.1109/ColCACI59285.2023.10226069es_CL


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Atribución-NoComercial-SinDerivadas 3.0 Chile
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