An artificial intelligence model based on multi-step feature engineering and deep attention network for optical network performance monitoring

Abstract

Optical network performance monitoring technology, which can effectively identify physical impairment in the network, is of great significance to ensure network stability and prevent accidents. In order to establish a high-precision performance monitoring framework for optical networks, a three-step feature engineering and deep attention mechanism approach is proposed in the paper. The main modeling process consists of the following three steps: In Step I, the AAH (Asynchronous Amplitude Histogram) method is used to initially extract features from the original data. In Step II, the KPCA (Kernel Principal Component Analysis) method and the Q-learning method are utilized to reduce the feature dimension and transmit the optimized feature to the downstream predictor. In step III, the downstream predictor based on the LSTM (long short-term memory network) and attention mechanism can effectively construct the mapping between features and labels and realize data prediction. After several groups of comparative experiments, the following conclusions can be obtained: (a) Ablation experiments and component comparisons demonstrate that the proposed framework is able to effectively combine feature engineering and predictors, which can get excellent results. (b) The optical network performance detection framework proposed in the paper achieves better results than three SOTA (state-of-the-art) models and fifteen alternative frameworks.