Optical Fiber Network Power Control Using Nonlinearity Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining optical parameters in optical fiber networks are operationally burdensome and error-prone, relying heavily on offline simulations and failing to account for optical nonlinear interactions, which can lead to inefficient power management and network degradation.
Innovation Solution
A method for dynamically adjusting control parameters in optical fiber networks by taking measurements, deriving estimated data on optical nonlinearity and amplified spontaneous emission, and applying control algorithms to optimize parameters using steepest descent algorithms, ensuring safe and efficient power management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline simulations are used to determine control parameters, then good control parameters such as peak power can be determined, but the process becomes operationally burdensome and error prone
Solution Approach 1:
The system performs self-adjustment by automatically monitoring optical parameters and controlling amplifier gains without requiring external offline simulations. The optical line control firmware continuously adapts control parameters based on real-time measurements, making the system self-optimizing and eliminating manual intervention.
Solution Approach 2:
The system implements closed-loop feedback by measuring optical signal characteristics at the receiver, comparing them against target values, and adjusting amplifier control parameters accordingly. This continuous feedback mechanism replaces offline simulations with real-time adaptive control, maintaining accuracy while reducing operational burden.
2Productivity
If high power is used to maximize capacity, then network capacity increases, but optical nonlinear interactions cause network degradation
Solution Approach 1:
The system dynamically adjusts control parameters including per-channel launch powers and total output power based on real-time optical conditions. By continuously optimizing these parameters, the system operates at the optimal power level that maximizes capacity while staying below the threshold where nonlinear degradation becomes significant.
Solution Approach 2:
The control system transitions from static offline-determined parameters to dynamic real-time adjustment. The firmware continuously adapts amplifier gains and launch powers based on current network conditions, allowing the system to respond to changing traffic patterns and environmental factors while maintaining optimal performance.
3Stability of the object's composition
If equalization of ASE to signal power ratio is performed, then power tilt across spans is addressed, but channel power limits must be respected which constrains optimization
Solution Approach 1:
The system applies different control strategies to different channels and amplifier spans based on their specific characteristics. Rather than uniform equalization, the firmware adjusts each channel's launch power and each amplifier's gain profile independently, allowing local optimization that respects channel-specific power limits while maintaining overall network balance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient power management and network optimization, minimizing degradation and maximizing capacity while ensuring stability of in-service channels, even in high-power regimes where nonlinearities are significant.
Implementation Method 1
an optical amplifier device is operative to amplify the optical signal
Implementation Method 2
taking measurements, deriving estimated data on optical nonlinearity and amplified spontaneous emission
Implementation Method 3
through the optical fiber network
Implementation Method 4
This equalization addresses the strong power tilt that can accumulate across spans of optical fiber mainly due to Stimulated Raman Scattering (SRS)
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
Adjustment of one of more control parameters of a section (10) of an optical fiber network is accomplished by applying one or more control algorithms using estimated data. The estimated data are derived (4) from measurements (2) of optical signals in the section and from knowledge of the section. The estimated data is a function of optical nonlinearity and of amplified spontaneous emission. Alternately, multiple receivers (64) are comprised in a flexible coherent transceiver (54) of a multi-span optical fiber network, each receiver operative to handle communications on a respective channel. The multiple receivers measure optical characteristics. For each receiver, the optical characteristics include optical nonlinear interactions on the respective channel, the optical nonlinear interactions being at least partially dependent from one span (56) to another span (56). An optical power of a signal on each of the multiple channels is adjusted as a function of the optical characteristics.