Accelerating Transmitter Dispersion Eye Closure Quaternary Assessment by Deep Transfer Learning Technique

Abstract

Transmitter dispersion eye closure quaternary (TDECQ) is one of the most important parameters that can systematically evaluate the signal quality of four-level pulse amplitude modulation format (PAM-4) transmitters. We experimentally demonstrate the acceleration of TDECQ assessment by deep transfer learning (DTL) technique. With the help of amplitude histograms (AHs) generated by the received PAM-4 signal, we are able to characterize the TDECQ value directly by the deep neural network (DNN). To further reduce both the computation time and the experimental data used for the training, simulation data can be used to provide the suitable source domain for the DTL implementation. The experimental results of 25 Gbaud PAM-4 signals indicate that, the mean absolute error of TDECQ assessment is below 0.18 dB over a TDECQ range from 1.9 to 3.6 dB. Meanwhile, 33.3% amount of the used experimental data and 40% epochs can be simultaneously reduced during the training process, due to the use of DTL.