计算机科学
人工神经网络
激活函数
卷积神经网络
感知器
多层感知器
人工智能
直方图
模式识别(心理学)
图像(数学)
作者
Yong Cheng,Huaijian Luo,Meng Xiang,Zhijun Yan,Yu Chen,Yuwen Qin,Songnian Fu
出处
期刊:IEEE Communications Magazine
[Institute of Electrical and Electronics Engineers]
日期:2023-12-01
卷期号:61 (12): 156-160
标识
DOI:10.1109/mcom.005.2300034
摘要
Optical performance monitoring (OPM) by leveraging artificial neural network (ANN) has become a promising data-driven solution for stability, reconfiguration, and efficient utilization of heterogeneous optical networks. The implementation complexity as well as the involved dataset of conventional ANN, however, remains a common concern, preventing the efficient OPM implementation. Nonlinear activation functions (NAFs) are commonly used in conventional ANN for feature attraction and nonlinear mapping, which largely reduce the convenience of the OPM, posing the question on the feasibility of removing NAF in ANN-enabled OPM. Here, by investigating the necessity of NAF for the ANN-based OPM, we find that, when either amplitude histogram (AH) or asynchronous delay-tap sampling portrait (ADTP) is used as the ANN input, the commonly-used multi-layer perceptron (MLP) and convolutional neural network (CNN) can be implemented without NAFs. Acceptable variations of mean absolute error (MAE) for the OSNR monitoring are observed by 0.03 dB and 0.23 dB, respectively. Moreover, the removal of NAF does not affect the identification of both the baud-rate and the modulation format information. Our results reveal that, the linear mapping is sufficient to realize the OPM with stable statistical images, thus leading to a simplification prospect for the on-chip ANN-enabled OPM.
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