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

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