Time-frequency dual-dimension deep neural network (TF-DD-DNN)-based OSNR and XT monitoring in mode-division multiplexing systems

Abstract

We propose a time-frequency dual-dimension deep neural network (TF-DD-DNN)-based optical performance monitoring (OPM) scheme to jointly monitor the optical signal-to-noise ratio (OSNR) and the mode-coupling induced crosstalk strength (XT) in mode-division multiplexing (MDM) systems, by revealing the diversity of the amplified spontaneous emission (ASE) and XT distortions on optical signals in the time- and frequency-domains. We numerically quantify the predictions of OSNR and XT for a coherent MDM transmission link, achieving the OSNR prediction accuracy of more than 0.99 and the XT prediction accuracy of up to 1 with the root mean square error (RMSE) of 0.11 dB at OSNR = 30 dB @0.1 nm for the case of quadrature-phase-shift keying (QPSK) signals. We evaluate the impacts of the chromatic dispersion (CD) of fibers and the low-pass filter bandwidth on the prediction procedures. The dependency of the modulation formats is also discussed, confirming the robust operation of the proposed scheme. Finally, the TF-DD-DNN scheme is experimental verified. High consistency is obtained between the simulation and experimental results.