Cost-Effective Multi-Parameter Optical Performance Monitoring Using Multi-Task Deep Learning With Adaptive ADTP and AAH

Abstract

A cost-effective optical performance monitoring (OPM) scheme is proposed to realize modulation format identification (MFI), baud rate identification (BRI), chromatic dispersion identification (CDI), and optical signal-to-noise ratio (OSNR) estimation of optical signals simultaneously. This technique is based on multi-task learning (MTL) neural network model with adaptive asynchronous delay tap plot (AADTP) and asynchronous amplitude histogram (AAH) by direct detection in the intermediate nodes of optical networks. The generation of AADTP depends on the sampling rate but not the symbol rate, which makes the scheme transparent to the baud rate. The combined inputs of AADTP with AAH improve accuracies of the neural network, compared with a single input. This scheme is verified experimentally where signals with two formats, quadrature phase shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM), two baud rates, 14 GBaud and 28 GBaud, and three CD situations, 0 ps/nm, 858.5 ps/nm, and 1507.9 ps/nm, are adopted. The best accuracies of MFI, BRI, CDI are 100%, 99.81%, and 99.83%, respectively. Meanwhile, the lowest average mean absolute error (MAE) of OSNR estimation is 0.2867 dB over the range of 10–24 dB (QPSK) and 15–29 dB (16QAM). It is cost-effective and practical for the proposed OPM technique to be applied in the intermediate nodes to construct smart optical networks since it uses only one photodetector assisted with an advanced deep learning algorithm.