Transfer learning simplified multi-task deep neural network for PDM-64QAM optical performance monitoring

We experimentally demonstrate a transfer learning (TL) simplified multi-task deep neural network (MT-DNN) for joint optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) from directly detected PDM-64QAM signals. First, we investigate the quality of amplitude histogram (AH) generation on the performance of OSNR monitoring and experimentally clarify the importance of higher electronic sampling rate in order to realize precise OSNR monitoring for high-order QAM format. Next, by implementing TL from simulation to experiment, when both 10Gbaud PDM-16QAM and PDM-64QAM signals are considered, the accuracy of MFI reaches 100% and the root-mean-square error (RMSE) of OSNR monitoring is 1.09dB over a range of 14-24dB and 23-34dB for PDM-16QAM and PDM-64QAM, respectively. Meanwhile, the used training samples and epochs can be substantially reduced by 24.5% and 44.4%, respectively. Since single photodetector (PD) and one TL simplified MT-DNN are used, the proposed optical performance monitoring (OPM) scheme with high cost performance can be applied for advanced modulation formats.