Optical Communication State Estimation via Sparse Coding
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Solution Overview
Problem
Conventional methods for estimating factors causing errors in optical communication require a large amount of constellation data and high computing resources, making them inefficient for real-time analysis, especially when using low-speed interfaces like MDIO.
Innovation Solution
A device and method utilizing random sampling for data reduction, sparse coding, and dictionary learning to estimate the state of optical communication with a small amount of constellation data, involving a data preprocessing unit, a learning unit, and a recognition unit to calculate sparse coefficients for error factor estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used to estimate optical communication state, then estimation performance is improved, but the amount of constellation data required increases
Solution Approach 1:
The patent extracts only the essential features from constellation data through dimensionality reduction techniques (PCA, t-SNE, UMAP) before feeding them to the deep learning model. This extraction process reduces the data quantity requirement while preserving the critical information needed for accurate state estimation, directly resolving the contradiction between estimation performance and data quantity requirements
Solution Approach 2:
The patent transforms the high-dimensional constellation data into a lower-dimensional feature space using dimensionality reduction methods. By projecting the data into a reduced dimensionality space, the system maintains estimation accuracy while significantly reducing the amount of data needed for effective learning, thus resolving the contradiction between precision and data quantity
2Measurement precision
If deep learning methods are used to estimate optical communication state, then estimation performance is improved, but the computing amount required increases
Solution Approach 1:
The patent extracts and retains only the most informative features from the constellation data through dimensionality reduction, eliminating redundant information. This feature extraction reduces the computing burden on the deep learning model while maintaining estimation accuracy, directly addressing the contradiction between performance and computing requirements
Solution Approach 2:
The patent performs dimensionality reduction and feature extraction as preliminary actions before the actual deep learning estimation. By pre-processing the data to reduce its dimensionality and computational complexity, the system prepares the data in a more efficient format that requires less computing power during the estimation phase, thus resolving the contradiction between estimation performance and computing amount
3Device complexity
If constellation data is acquired using low-speed interfaces like MDIO, then device complexity is reduced, but the time required to acquire data increases
Solution Approach 1:
The patent performs dimensionality reduction and feature extraction as preliminary actions on the acquired constellation data. By pre-processing the data to reduce its dimensionality and complexity, the system prepares the data in an optimized format that can be quickly processed by the deep learning model, thus reducing the overall time loss from data acquisition to estimation while maintaining the use of simple low-speed interfaces
Solution Approach 2:
The patent changes the parameters of the constellation data through dimensionality reduction techniques, transforming it from a high-dimensional format to a lower-dimensional feature representation. This parameter transformation reduces the computational load and processing time required for estimation, effectively compensating for the slow data acquisition speed from low-speed interfaces
Data Source
AI summary
The present disclosure has an object of proposing a method and a device for estimating the state of a transmission path or an optical transmitter capable of mechanically estimating a factor causing an error with a small amount of constellation data and a low computing amount. The present disclosure provides a device for estimating a state of optical communication, the device including: a data preprocessing unit that reduces the number of data using random sampling with respect to constellation data in which an amplitude and a phase of optical communication data are represented by a polar coordinate diagram and performs distribution calculation and a dimension reduction; a learning unit that learns a dictionary matrix in sparse coding using learning constellation data processed by the data preprocessing unit; and a recognition unit that calculates a sparse coefficient using recognition constellation data processed by the data preprocessing unit and the dictionary matrix learned by the learning unit and estimates a factor causing degradation of the optical communication using the calculated sparse coefficient.