Random Walk for modelling Multi Core Fiber cross-talk and step distribution characterisation

TL;DR

This paper introduces a novel random walk model for multi-core fiber cross-talk characterization that accurately captures time and frequency domain features, demonstrating robustness and superior information content for source classification.

Contribution

The paper presents a new random walk based model for IC-XT that generalizes existing models, with a novel characterization method and machine learning for improved source classification.

Findings

Model accurately fits experimental IC-XT data with over 99.3% accuracy.

Characterization method is robust to time-window, temperature, and signal properties.

Machine learning outperforms traditional statistical methods in source classification.

Abstract

A novel random walk based model for inter-core cross-talk (IC-XT) characterization of multi-core fibres capable of accurately representing both time-domain distribution and frequency-domain representation of experimental IC-XT has been proposed. It was demonstrated that this model is a generalization of the most widely used model in literature to which it will converge when the number of samples and measurement time-window tend to infinity. In addition, this model is consistent with statistical analysis such as short term average crosstalk (STAXT), keeping the same convergence properties and it showed to be almost independent to time-window. To validate this model, a new type of characterization of the IC-XT in the dB domain (based on a pseudo random walk) has been proposed and the statistical properties of its step distribution have been evaluated. The performed analysis showed that this characterization is capable of fitting every type of signal source with an accuracy above 99.3%. It also proved to be very robust to time-window length, temperature and other signal properties such as symbol rate and pseudo-random bit stream (PRBS) length. The obtained results suggest that the model was able to communicate most of the relevant information using a short observation time, making it suitable for IC-XT characterization and core-pair source signal classification. Using machine-learning (ML) techniques for source-signal classification, we empirically demonstrated that this technique carries more information regarding IC-XT than traditional statistical methods.