Nonlinearity Compensation Techniques Using Machine Learning

Stylianos Sygletos, Alexey A. Redyuk, Oleg Sidelnikov

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

We discuss our recent work on machine learning based nonlinear equalization in long haul transmission sytems. We show that dynamic multi-perceptron networks can deal with the memory properties of the fibre channel and provide efficient mitigation of nonlinear impairments at lower computational cost when compared to conventional digital back propagation methods.
Original languageEnglish
Title of host publicationSignal Processing in Photonic Communications 2019
PublisherOptical Society of America
ISBN (Print)978-1-943580-64-4
Publication statusPublished - 29 Jul 2019

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    Sygletos, S., Redyuk, A. A., & Sidelnikov, O. (2019). Nonlinearity Compensation Techniques Using Machine Learning. In Signal Processing in Photonic Communications 2019 [SpT2E.2] Optical Society of America.