Joint, accurate and robust optical signal-to- noise ratio and modulation format monitoring scheme using a single Stokes-parameter-based artificial neural network

A joint and robust optical signal-to-noise ratio (OSNR) and modulation format monitoring scheme using an artificial neural network (ANN) is proposed and demonstrated via both numerical simulations and experiments. Before ANN, the power iteration method in Stoke space is employed to estimate the phase difference between two orthogonal polarizations caused by fiber birefringence. Then, a three layers ANN is employed to approximate the relationship between the cumulative distribution function of a single Stokes parameter (S ) and the targeted OSNR and format information. The simulation results show that the probability of OSNR estimation error within 1dB in the proposed scheme is 100%, 99.78%, 100%, 99.78% and 98.89% for 28GS/s QPSK, 8PSK, 8QAM, 16QAM and 64QAM, respectively. Meanwhile, the proposed scheme also shows high modulation format identification accuracy in the presence of nonlinear Kerr effect and residual chromatic dispersion. With 1 dB OSNR estimation error, the proposed scheme can tolerate the residual chromatic dispersion and phase-related polarization rotation rate up to 100ps/nm and 50kHz, respectively. The experimental results also further confirm that the proposed scheme shows high modulation identification accuracy for 28GS/s QPSK, 8PSK and 16QAM under the scenarios of both back-to-back and fiber transmission. Meanwhile, with the launched power of 0dBm, the mean OSNR estimation error in our scheme is smaller than 1 dB within ±160ps/nm residual chromatic dispersion after fiber transmission.