Optical Network Control Using Posterior BER Models for Nonlinear Links
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Solution Overview
Problem
Current optical communication systems face challenges in handling non-Gaussian and nonlinear environments due to high computational demands, limited adaptability, and reliance on linear and Gaussian assumptions, which hinder real-time processing and efficiency.
Innovation Solution
A system integrating natural intelligence (NI) with a perception-action cycle (PAC) framework, utilizing a perceptor and executive subsystem with adaptive feedback mechanisms to dynamically adjust optical communication parameters, enabling proactive management of nonlinear impairments and enhancing bit error rate (BER) performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional linear and Gaussian processing methods are used in optical communication systems, then the system structure remains simple, but the system cannot effectively handle non-Gaussian and nonlinear environments, leading to poor BER performance
Solution Approach 1:
The patent transforms the optical signal from time domain to frequency domain using Fourier transform, changing the parameter representation of the signal. This enables the system to handle nonlinear effects by operating in the frequency domain where nonlinear distortions can be more effectively compensated through spectral analysis and processing.
Solution Approach 2:
The patent replaces traditional mechanical signal processing approaches with computational methods including Fourier transforms, spectral density calculations, and numerical simulations. This substitution enables sophisticated analysis of nonlinear optical effects without requiring complex physical hardware modifications.
2Reliability
If sophisticated signal processing methods are implemented to handle nonlinear effects, then BER performance improves, but computational complexity increases significantly
Solution Approach 1:
The patent segments the optical signal into frequency components using Fourier transform, allowing independent processing of different spectral components. This segmentation enables targeted compensation of nonlinear effects in specific frequency ranges without requiring complex full-signal processing, reducing overall computational burden.
Solution Approach 2:
The patent implements partial processing by focusing computational resources on the most significant frequency components and nonlinear effects that dominate system performance. Rather than processing all signal aspects equally, the method applies sophisticated processing only where needed to achieve BER improvement with acceptable computational complexity.
3Speed
If real-time processing is implemented in nonlinear optical environments, then system responsiveness improves, but computational demands exceed available processing capacity
Solution Approach 1:
The patent employs periodic Fourier transforms at strategically selected points along the optical transmission path rather than continuous processing. This periodic action allows the system to maintain real-time responsiveness while significantly reducing computational energy demand by processing signals at discrete intervals where nonlinear effects can be effectively captured and compensated.
Solution Approach 2:
The patent performs preliminary Fourier analysis and spectral characterization of the optical signal before full compensation processing. This preliminary action identifies dominant frequency components and nonlinear effect characteristics, allowing subsequent real-time processing to focus only on correcting identified issues rather than processing the entire signal spectrum, thereby reducing computational energy requirements.
Data Source
AI summary
What is disclosed is: a method for natural intelligence (NI) processing for a software-defined optical communication system (SDOCS). The method comprises receiving perceptions comprising a plurality of transmitted symbols, and determining, based on the received plurality of transmitted symbols, whether a suitable posterior model is available. When a suitable posterior model is available, the model is retrieved. The retrieved posterior model is used to estimate a BER, and the estimated BER is communicated to an executive subsystem. When the estimated BER is below a threshold, a prospective action is selected. The selected prospective action is tested in a virtual environment to determine whether the prospective action is beneficial. When the selected prospective action is beneficial, it is communicated to a feedback subsystem. Signals comprising the selected prospective action are received. An adjustment to implement the selected prospective action is determined, and signals to perform the determined adjustment are transmitted.