OSNR Spectrum Estimation Using Gaussian Process Regression
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Solution Overview
Problem
Conventional methods for estimating the optical signal to noise ratio (OSNR) spectrum in optical networks require a large number of measurements and significant data transfer, making them time-consuming and inefficient.
Innovation Solution
An OSNR spectrum estimation apparatus that uses a reduced number of measurements by calculating average values and variances through Gaussian process regression, determining optimal wavelength channels for measurement based on posterior distribution, and selecting appropriate modulation schemes to reduce traffic and measurement time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods sequentially switch all wavelength channels to estimate OSNR spectrum, then measurement accuracy is improved, but measurement time and data transfer volume significantly increase
Solution Approach 1:
The patent uses a neural network model trained on comprehensive OSNR data to create a virtual copy of the full spectrum estimation process. Instead of physically measuring all wavelength channels, the system measures only selected channels and uses the neural network to generate estimated values for remaining channels, achieving accurate OSNR spectrum estimation with reduced measurement time
Solution Approach 2:
The patent implements partial measurement by selecting only a subset of wavelength channels for actual OSNR measurement rather than measuring all channels. The neural network then completes the spectrum estimation using these partial measurements, significantly reducing measurement time while maintaining estimation accuracy
2Measurement precision
If conventional methods sequentially switch all wavelength channels to estimate OSNR spectrum, then measurement accuracy is improved, but data transfer volume significantly increases
Solution Approach 1:
The neural network model creates a virtual representation of the complete OSNR spectrum based on measurements from selected channels only. This copying approach generates the full spectrum data without requiring physical measurement and transfer of data from all wavelength channels, thus reducing measurement data volume
Solution Approach 2:
The patent extracts only the essential measurement data from selected wavelength channels that are sufficient for accurate spectrum estimation. By removing unnecessary measurements from unselected channels, the system reduces the quantity of measurement data that needs to be transferred and processed
3Reliability
If optical spectrum analyzer monitors all wavelength channels, then comprehensive OSNR data is obtained, but device complexity and processing burden increase
Solution Approach 1:
The patent introduces a neural network model as an intermediary between the optical spectrum analyzer and the monitoring system. The analyzer measures only selected channels, and the neural network mediates by generating the complete OSNR spectrum, thereby reducing device complexity and processing burden while maintaining comprehensive monitoring capability
Solution Approach 2:
The neural network creates a copied representation of the complete OSNR spectrum based on partial measurements. This copying mechanism allows the system to achieve comprehensive monitoring without requiring the optical spectrum analyzer to physically monitor all wavelength channels, reducing system complexity
Data Source
AI summary
An OSNR spectrum estimation apparatus includes an OSNR estimation unit configured to cause an optical node to estimate an OSNR of a predetermined transmission line using a probe light of a predetermined wavelength in a predetermined number of wavelength channels, the predetermined number being less than the number of all wavelength channels; and an OSNR spectrum calculation unit configured to calculate an OSNR spectrum of all the wavelength channels from OSNRs of the predetermined number of wavelength channels measured by the optical node.