Dual-Stage Multiple Parameters Estimation for Low-Margin Elastic Optical Networks

Abstract

A dual-stage algorithm structure is proposed to improve estimation accuracy and reliability for low-margin elastic optical network. At the first-stage, a multitask learning-based artificial neural network (MTL-ANN) is proposed to estimate multiple parameters simultaneously. At the second-stage, a threshold-based decision module is deployed to divide the estimation results into reliable results and doubtful results. As to the doubtful results, we investigate the deviation range and underestimate the results to allocate adequate system margin. The algorithm structure is experimentally demonstrated for optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in a polarization division multiplexing (PDM) coherent optical system. Signals’ amplitude histograms (AHs) of circular constellation diagrams are selected as the input features. The results show that the MFI accuracy of nine M-QAM formats under consideration is 100%. With 93.6% OSNR estimation accuracy at first-stage, OSNR estimation with accuracy higher than 99% is achieved for the reliable results. In addition, the confidence level of doubtful results within 3 dB deviation is 0.96.