Multi-task deep neural network (MT-DNN) enabled optical performance monitoring from directly detected PDM-QAM signals

We experimentally demonstrate a multi-task deep neutral network (MT-DNN) enabled optical performance monitoring (OPM) for PDM-QPSK/8QAM/16QAM signals, by using the asynchronous amplitude histogram (AAH) after the direct detection. Consequently, we can simultaneously realize the modulation format identification (MFI), baud rate identification (BRI), and optical signal-to-noise ratio (OSNR) monitoring simultaneously. In the simulation, when both 20Gbaud and 30Gbaud PDM-QPSK, PDM-8QAM and PDM-16QAM signals are taken into account, the accuracies of both MFI and BRI are 100%. Meanwhile, the root-mean-square error (RMSE) of OSNR monitoring is 0.58dB over a range of 10-22dB, 14-24dB, and 17-26dB for PDM-QPSK, PDM-8QAM and PDM-16QAM, respectively. Furthermore, the numerical results show that RMSE of OSNR monitoring is degraded from 0.58dB to 0.97dB, when chromatic dispersion (CD) is accumulated from 0 to 1600ps/nm. Meanwhile, the MFI accuracy is degraded from 100% to 97.25%, and the BRI accuracy remains 100%. When 2.8Gbaud and 9.8Gbaud signals are used for the experimental verification under the condition of back-to-back transmission, the accuracies of MFI and BRI are 100% and 96.8%, respectively, and the RMSE of OSNR monitoring is 0.76dB. Since only one photodetector (PD) with the asynchronous sampling and one MT-DNN are required, the proposed OPM scheme has the advantage of high cost performance.