Numerical simulation and experimental demonstration of accurate machine learning aided IQ time-skew and power-imbalance identification for coherent transmitters

A novel method assisted by feed-forward artificial neural network model for joint identification of in-phase/quadrature (IQ) time-skews and power-imbalances for coherent optical transmitters is proposed in this paper. This method not only reduces the complexity of hardware design but also significantly improves the accuracy of time-skew/power-imbalance estimation, therefore largely enhances the efficiency of coherent transmitter offline calibration. The proposed method works in the heterodyne detection manner to detect both the time- and power- imperfections. A modified 2 × 2 real-valued channel equalizer for QPSK and 16-QAM signals is applied to extract the channel coefficients, which can fully reconstruct the signals. A key tool using artificial neural networks is used to establish the explicit numerical relationships between the values of IQ time-skew/power-imbalance and the channel coefficients. Both simulation and experimental tests are carried out to verify the capability of the proposed method. Simulation results show that the mean square errors (MSE) of IQ time-skew and power-imbalance estimation can reach below 0.03% of the symbol period and 5.72 × 10 -5 at the optical signal-to-noise ratio value of 20 dB. Experiment tests based on 100-Gb/s and 200-Gb/s coherent optical modules show that the mean absolute errors (MAEs) of estimated IQ time-skew and power-imbalance are 0.145 ps and 0.01 for 32-GBaud QPSK signal and 0.162ps and 0.006 for 32-GBaud 16-QAM signal. A demonstrative calibration process is applied to a coherent optical module with pre-set IQ time-skew and power-imbalance by improving the Q 2 factor of 32-Gbaud 16-QAM signals from 12.9 dB to 20.3 dB in a coherent optical transmission link at back-to-back case.