Optical Signal De-Noising Using Unsupervised Distortion Learning
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Solution Overview
Problem
Conventional distortion compensation systems in optical communication face inaccuracies due to non-deterministic distortion components, require large data sets for accurate learning, and are limited to specific operating conditions, leading to inefficient distortion compensation.
Innovation Solution
A signal processing system that identifies distortion in an unsupervised manner, using unsupervised learning algorithms to characterize signal statistical properties and modify input signals based on learned parameters, enabling accurate distortion compensation without requiring extensive data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional distortion compensation systems use supervised learning methods to learn distortion compensation functions, then the system can compensate for deterministic distortion components, but the system cannot accurately learn non-deterministic distortion components which follow a distribution
Solution Approach 1:
The patent segments distortion into deterministic components (which can be learned) and non-deterministic components (which follow a distribution). By separating these components, the system can apply different processing approaches: supervised learning for deterministic parts and statistical characterization for non-deterministic parts, thereby improving overall compensation accuracy without requiring the system to fail on non-deterministic elements.
Solution Approach 2:
The patent introduces an intermediary statistical model that characterizes the distribution of non-deterministic distortion components. This intermediary model acts as a bridge between the deterministic learned compensation and the stochastic non-deterministic elements, allowing the system to handle both types of distortion effectively by mediating between fixed patterns and random variations.
2Measurement precision
If the system collects large amounts of signal data to accurately learn the distortion compensation function, then the learning accuracy improves, but the data requirement becomes excessively large
Solution Approach 1:
The patent extracts and removes non-deterministic distortion components from the signal data before the learning process. By taking out these non-deterministic elements that cannot be learned anyway, the remaining deterministic components can be learned accurately with significantly less data, thereby reducing the excessive data requirement while maintaining learning accuracy.
Solution Approach 2:
Instead of attempting to learn all distortion components including non-deterministic ones (excessive action), the patent applies partial learning only to the deterministic components that can actually be learned. This partial approach avoids the need for excessively large data sets required to try to learn the entire distortion profile including unlearnable elements.
3Measurement precision
If the distortion compensation function is learned based on specific operating conditions, then the compensation is accurate for those conditions, but the compensation function becomes limited and cannot adapt to changing operating conditions
Solution Approach 1:
The patent characterizes the statistical properties (parameters) of non-deterministic distortion components under different operating conditions. By modeling how these statistical parameters change with operating conditions, the system can adapt the compensation function to new conditions by updating the statistical characterizations, thereby maintaining accuracy across varying conditions without being limited to the original training conditions.
4Reliability
If the system uses pre-compensation means to add pre-compensated components to the input signal, then distortion compensation is achieved, but the system complexity increases
Solution Approach 1:
The patent merges the distortion compensation function learning process with the existing supervised learning framework. By combining the deterministic learning component and the statistical characterization of non-deterministic components into a unified compensation approach, the system achieves effective distortion compensation without requiring separate complex subsystems, thereby reducing overall system complexity while maintaining reliability.
Data Source
AI summary
This disclosure relates to a signal processing system for an optical communication system including an optical transmitter and an optical receiver. The signal processing system is configured to: identify a distortion of input signals from an optical receiver in an unsupervised manner to output distortion parameters indicating the distortion identified; and utilize the outputted distortion parameters to modify signal inputted to an optical transmitter.