English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We investigate the performance of a machine learning classificationtechnique, called the Parzen window, to mitigate the fiber nonlinearity in thecontext of dispersion managed and dispersion unmanaged systems. The techniqueis applied for detection at the receiver side, and deals with the non-Gaussiannonlinear effects by designing improved decision boundaries. We also propose atwo-stage mitigation technique using digital back propagation and Parzen windowfor dispersion unmanaged systems. In this case, digital back propagationcompensates for the deterministic nonlinearity and the Parzen window deals withthe stochastic nonlinear signal-noise interactions, which are not taken intoaccount by digital back propagation. A performance improvement up to 0:4 dB interms of Q factor is observed.

A Machine Learning-Based Detection Technique for Optical Fiber Nonlinearity Mitigation