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dc.contributorLázaro Villa, José Antonio
dc.contributorSarmiento Hernández, Samael
dc.contributor.authorKais Carranza, Johanna Libertad
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.description.abstractVCSEL's capacity of direct modulation and its low cost makes this device a feasible cost-effective transmitter for ultra-dense wavelength division multiplexing (uDWDM) metro-access networks using coherent detection. However, performing direct-phase modulation in semiconductors can be complex due to its nonlinear characteristics. This research presents Neural Network (NN) training techniques for Time-Series analysis in order to describe the correlation between the input current given to the device and its output optical phase, using a 1550nm RayCan SM-VCSEL. Main goal is training a NN capable of predicting an ideal optical power signal for a specific phase result achievable by inverse training, that is: optical phase is the neural network input while the optical power is the desired target. The experiment is done in three stages: (i) VCSEL's characterization, (ii) NN training to predict input current knowing optical power, and (iii) NN training to predict optical power from a known optical phase.
dc.publisherUniversitat Politècnica de Catalunya
dc.rightsS'autoritza la difusió de l'obra mitjançant la llicència Creative Commons o similar 'Reconeixement-NoComercial- SenseObraDerivada'
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject.lcshNeural networks (Computer science)
dc.subject.otherNeural Network
dc.subject.otherTime-Series Analysis
dc.titleChirp-based direct phase modulation of VCSELs managed by Neural Networks
dc.typeMaster thesis
dc.subject.lemacXarxes neuronals (Informàtica)
dc.rights.accessOpen Access
dc.audience.mediatorEscola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona

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