The reflection and refraction of chirped Gaussian pulse at a moving step refractive-index boundary are investigated. When a chirped Gaussian pulse crosses a temporal boundary, the shape of the reflected spectra is distorted by adjusting chirp parameters. However, the transmitted spectra retain the Gaussian shape. The shape of the final output spectra is the same if the absolute values of the chirp are the same. By changing the chirp values, we can control the energy of the reflected and transmitted pulses, and the splitting distance of the pulse at the temporal boundary. By adjusting the time-dependent refractive index, chirped Gaussian pulses can experience total internal reflection at the temporal boundary. When pulse splitting occurs in an anomalous dispersion region, the velocity of the transmitted pulse decreases.

We investigate the pulse evolution and energy conservation condition at the temporal boundary under third-order dispersion. When the fundamental soliton crosses the temporal boundary and forms two reflected pulses and one transmitted pulse, the power of the transmitted pulse first increases and then decreases as the incident spectrum shifts toward the blue side. If the transmitted spectrum lies in the anomalous group-velocity dispersion region, second-order soliton is formed and dispersive wave is radiated. We present a modified phase-matching condition to predict the resonance frequencies. The predicted results are in good agreement with the results obtained by numerically solving the nonlinear Schrödinger equation.

Weakly coupled multicore fiber (WC-MCF) is promising for use in short-reach fiber telecommunication systems in the near future. Intercore crosstalk (XT) plays an important role in WCMCF-based short-reach intensity-modulation/direct-detection (IM/DD) systems, because XT can significantly degrade system performance. For the first time, the combined effect of intercore XT and the frequency offset between optical carriers, which is defined as the carrier beating impairment (CBI), is measured and characterized by experiments and numerical simulations. The experimental results show that the CBI causes large fluctuations in the signal-to-noise ratio (SNR) with a normally tolerable XT level. Such SNR fluctuations can additionally reduce the system’s XT tolerance (at worst 1-dB SNR degradation) by as much as 20 dB for 4-level pulse amplitude modulation (PAM-4) transmission. The simulation results show that the CBI is mainly affected by the specific properties of the laser source and the modulation depth of the transmitted signals. These results suggest that the XT should be designed to be much smaller for WCMCF-based short-reach IM/DD systems when there is the CBI.

In this paper, a deep learning-based prediction model VMD-GLRT is proposed to address the accuracy problem of service load prediction. The VMD-GLRT model combines Variational Mode Decomposition (VMD) and GRU-LSTM. At the same time, the model incorporates residual networks and self-attentive mechanisms to improve accuracy of the model. The VMD part decomposes the original time series into several intrinsic mode functions (IMFs) and a residual part. The other part uses a GRU-LSTM structure with ResNets and Self-Attention to learn the features of the IMF and the residual part. The model-building process focuses on three main aspects: Firstly, a mathematical model is constructed based on the data characteristics of the service workload. At the same time, VMD is used to decompose the input time series into multiple components to improve the efficiency of the model in extracting features from the data. Secondly, a long and short-term memory (LSTM) network unit is incorporated into the residual network, allowing the network to correct the predictions more accurately and improve the performance of the model. Finally, a self-focus mechanism is incorporated into the model, allowing the model to better capture features over long distances. This improves the dependence of the output vector on these features. To validate the performance of the model, experiences were conducted using open-source datasets. The experimental results were compared with other deep learning and statistical models, and it was found that the model proposed in this paper achieved improvements in mean absolute percentage error (MAPE).