Recurrent Neural Network Soft Demapping for Mitigation of Fiber Nonlinearities and ISI

التفاصيل البيبلوغرافية
العنوان: Recurrent Neural Network Soft Demapping for Mitigation of Fiber Nonlinearities and ISI
المؤلفون: Georg Böcherer, Stephan Pachnicke, Fabio Pittala, Maximilian Schaedler, Stefano Calabro, Christian Bluemm
المصدر: OFC
بيانات النشر: Optica Publishing Group, 2021.
سنة النشر: 2021
مصطلحات موضوعية: Optical fiber, Artificial neural network, Noise (signal processing), Computer science, Optical computing, Transmission system, Viterbi algorithm, law.invention, Nonlinear system, symbols.namesake, Recurrent neural network, law, Electronic engineering, symbols
الوصف: Optical transmission systems suffer from linear and nonlinear impairments induced by components and fibers. As countermeasures, neural network soft-demappers are proposed and benchmarked against combinations of digital back-propagation, Volterra equalizers, noise whitening and soft-output Viterbi-algorithms.
DOI: 10.1364/ofc.2021.m5f.4
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::d7716240f32fe2fd399458bd2b727cad
https://doi.org/10.1364/ofc.2021.m5f.4
رقم الانضمام: edsair.doi...........d7716240f32fe2fd399458bd2b727cad
قاعدة البيانات: OpenAIRE