研究目的
To monitor the performance of PAM4 optical communication systems by using principal component analysis and support vector regression with eigenvectors derived from asynchronous amplitude histograms.
研究成果
The proposed method using PCA and SVR with AAHs is effective and accurate for monitoring the performance of PAM4 optical communication systems, with prediction errors for CD and OSNR within acceptable ranges. The method also has potential applications in monitoring PAM-N optical communication systems.
研究不足
The study focuses on PAM4 optical communication systems under specific conditions (nonlinear optical fiber). The generalization of the method to other systems or conditions may require further validation.
1：Experimental Design and Method Selection:
The study involves constructing a PAM4 optical communication system to generate experimental data under nonlinear optical fiber conditions. Asynchronous amplitude histograms (AAHs) are used to extract features from the data, followed by principal component analysis (PCA) for dimensionality reduction and support vector regression (SVR) for prediction.
2：Sample Selection and Data Sources:
Experimental data about chromatic dispersion (CD) and optical signal to noise ratio (OSNR) are collected under various conditions by adjusting system parameters.
3：List of Experimental Equipment and Materials:
The system includes dispersion compensating optical fiber (DCF), single-mode optical fiber (SMF), erbium-doped optical fiber amplifiers (EDFA), and an optical attenuator.
4：Experimental Procedures and Operational Workflow:
The PAM4 signal is transmitted through the system, sampled asynchronously to construct AAHs, and then processed using PCA and SVR for CD and OSNR prediction.
5：Data Analysis Methods:
The study employs PCA for dimensionality reduction of eigenvectors derived from AAHs and SVR for the prediction of CD and OSNR, analyzing the prediction errors.
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