A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons

We apply the Empirical Mode Decomposition (EMD) algorithm and the Time Convolutional Network (TCN) structure, predicated on Convolutional Neural Networks, to successfully enable feature extraction within high-precision optical time-frequency signals, and provide effective identification and alerts for abnormal link states. Experimental validation confirms that the proposed method not only delivers an efficacy on par with traditional manual techniques, but also excels in swiftly identifying anomalies that typically elude conventional approaches. This investigation furnishes novel theoretical backing and forecasting tools for high-precision optical transmission.

Automated summary

This paper applies the Empirical Mode Decomposition (EMD) algorithm and the Time Convolutional Network (TCN) structure, based on Convolutional Neural Networks, to extract features from high-precision optical time-frequency signals and effectively identify and alert abnormal link states. Experimental validation shows that the proposed method performs as effectively as traditional manual techniques, while excelling in swiftly identifying anomalies that conventional approaches often miss. This investigation provides novel theoretical support and forecasting tools for high-precision optical transmission.