Robust Self-Learning Physical Layer Abstraction Utilizing Optical Performance Monitoring and Markov Chain Monte Carlo

Fanchao Meng; Shuangyi Yan; Rui Wang; Yanni Ou; Yu Bi; Reza Nejabati; Dimitra Simeonidou

doi:10.1109/ECOC.2017.8346217

Robust Self-Learning Physical Layer Abstraction Utilizing Optical Performance Monitoring and Markov Chain Monte Carlo

Fanchao Meng, Shuangyi Yan, Rui Wang, Yanni Ou, Yu Bi, Reza Nejabati, Dimitra Simeonidou

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Abstract

We model and experimentally demonstrate a self-learning abstraction process based on statistical assessment of the real-time monitoring data, both amplifier and non-linear noise parameters are periodically updated which further enables an accurate QoT estimator.

Original language	English
Number of pages	3
DOIs	https://doi.org/10.1109/ECOC.2017.8346217
Publication status	Published - 15 Sept 2017
Event	3rd European Conference and Exhibition on Optical Communication: ECOC 2017 - Gothenburg, Sweden Duration: 17 Sept 2017 → …

Conference

Conference	3rd European Conference and Exhibition on Optical Communication
Country/Territory	Sweden
City	Gothenburg
Period	17/09/17 → …

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10.1109/ECOC.2017.8346217

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title = "Robust Self-Learning Physical Layer Abstraction Utilizing Optical Performance Monitoring and Markov Chain Monte Carlo",

abstract = "We model and experimentally demonstrate a self-learning abstraction process based on statistical assessment of the real-time monitoring data, both amplifier and non-linear noise parameters are periodically updated which further enables an accurate QoT estimator.",

author = "Fanchao Meng and Shuangyi Yan and Rui Wang and Yanni Ou and Yu Bi and Reza Nejabati and Dimitra Simeonidou",

year = "2017",

month = sep,

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T1 - Robust Self-Learning Physical Layer Abstraction Utilizing Optical Performance Monitoring and Markov Chain Monte Carlo

AU - Meng, Fanchao

AU - Yan, Shuangyi

AU - Wang, Rui

AU - Ou, Yanni

AU - Bi, Yu

AU - Nejabati, Reza

AU - Simeonidou, Dimitra

PY - 2017/9/15

Y1 - 2017/9/15

N2 - We model and experimentally demonstrate a self-learning abstraction process based on statistical assessment of the real-time monitoring data, both amplifier and non-linear noise parameters are periodically updated which further enables an accurate QoT estimator.

AB - We model and experimentally demonstrate a self-learning abstraction process based on statistical assessment of the real-time monitoring data, both amplifier and non-linear noise parameters are periodically updated which further enables an accurate QoT estimator.

U2 - 10.1109/ECOC.2017.8346217

DO - 10.1109/ECOC.2017.8346217

M3 - Conference Paper

T2 - 3rd European Conference and Exhibition on Optical Communication

Y2 - 17 September 2017

ER -

Robust Self-Learning Physical Layer Abstraction Utilizing Optical Performance Monitoring and Markov Chain Monte Carlo

Abstract

Conference

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