Published May 18, 2020
| Version v1
Conference paper
Open
Experimental Demonstration of a Machine Learning-Based in-band OSNR Estimator from Optical Spectra
Authors/Creators
- 1. Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Nokia Bell Labs
- 2. Nokia Bell Labs
- 3. Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
- 4. Universitat Politècnica de Catalunya (UPC)
Description
Channel spectral monitors are becoming a cost effective solution to improve the management, resiliency and efficiency of next generation optical transport networks. We experimentally demonstrate a technique based on machine learning (ML) for the in-band estimation of amplified spontaneous emission (ASE) noise and filter 3-dB bandwidth, using optical spectra acquired after the reconfigurable optical add/drop multiplexers (ROADMs) filters. We assess the performance of the proposed method, considering laser drift and filters bandwidth tightening scenarios, showing quite good estimation accuracy under such conditions.
Notes
@ 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.