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.

中文翻译：

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低裕度弹性光网络的双级多参数估计

提出了一种双级算法结构来提高低裕度弹性光网络的估计精度和可靠性。在第一阶段，提出了一种基于多任务学习的人工神经网络（MTL-ANN）来同时估计多个参数。在第二阶段，部署基于阈值的决策模块将估计结果分为可靠结果和可疑结果。对于可疑的结果，我们调查偏差范围并低估结果以分配足够的系统余量。该算法结构在偏振分复用 (PDM) 相干光学系统中通过实验证明用于光信噪比 (OSNR) 监测和调制格式识别 (MFI)。选择圆形星座图的信号幅度直方图（AH）作为输入特征。结果表明，所考虑的九种 M-QAM 格式的 MFI 准确度为 100%。第一阶段OSNR估计精度为93.6%，OSNR估计精度高于99%，结果可靠。此外，3 dB偏差内的可疑结果的置信度为0.96。