Optical Calibration Apparatus Using Machine Learning for Distortion Compensation
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Solution Overview
Problem
Existing optical communication systems face challenges in compensating for distortions introduced by transceivers, which can lead to misinterpretation of messages at the receiver.
Innovation Solution
A calibration apparatus that uses machine learning-based models to determine configuration parameters for pre-distortion, intermediate pre-distortion, and post-distortion compensators in an optical communication system, enabling effective compensation for distortions introduced by the transceiver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional compensation methods are used, then the system structure is simple, but the distortion compensation accuracy is insufficient
Solution Approach 1:
The patent divides the distortion compensation function into three separate compensators: pre-distortion compensator, intermediate pre-distortion compensator, and post-distortion compensator. Each compensator handles specific distortion components, allowing independent optimization of compensation accuracy for different distortion types while maintaining overall system manageability through modular architecture.
Solution Approach 2:
The patent implements preliminary calibration by training machine learning models offline using simulation data before actual system operation. The models are trained to predict optimal compensation parameters in advance, allowing the system to achieve high compensation accuracy without complex real-time computation during actual communication operations.
2Measurement precision
If multiple compensators are added to improve compensation accuracy, then the distortion compensation accuracy is improved, but the calibration complexity increases
Solution Approach 1:
The patent performs all calibration activities in advance by training machine learning models using extensive simulation data generated from the channel model. The models learn optimal compensation parameters during offline training, eliminating the need for complex real-time calibration procedures during actual system operation and reducing operational complexity.
Solution Approach 2:
The patent creates virtual copies of the physical system through detailed channel models and simulation environments. These digital twins allow extensive calibration and model training using synthetic data that represents various communication scenarios, enabling comprehensive calibration without requiring complex physical measurement and adjustment procedures.
3Measurement precision
If machine learning models are trained extensively to improve compensation accuracy, then the compensation accuracy is improved, but the training time and computational resources increase
Solution Approach 1:
The patent performs all intensive model training activities during an offline calibration phase before actual system operation begins. By completing the time-consuming training process in advance using simulation data, the system achieves high compensation accuracy without requiring lengthy training during actual communication operations, thus minimizing operational time loss.
Solution Approach 2:
The patent uses simulated copies of real-world communication channels instead of actual physical measurements for training. These virtual channel models allow rapid generation of training data and model training without the time constraints and resource requirements of physical calibration measurements, significantly reducing training time while maintaining accuracy.
Data Source
AI summary
A calibration apparatus trains a first machine learning-based model with a first training dataset to determine configuration parameters of an intermediate pre-distortion compensator in an optical communication system that includes a transmitter, a receiver, and an optical communication channel. The transmitter includes a pre-distortion compensator, the intermediate pre-distortion compensator, and an MZM compensator. The calibration apparatus trains a second machine learning-based model with a second training dataset to determine configuration parameters of the post-distortion compensator in the receiver. The calibration apparatus trains a third machine learning-based model with a third training dataset to determine configuration parameters of the pre-distortion compensator. When generating the second training data, the intermediate pre-distortion compensator is configured with the configuration parameters generated using the first machine learning-based model. When generating the third training data, the post-distortion compensator is configured with the configuration parameters generated using the second machine learning-based model.