PAM-4/8光互连中基于完全二叉树结构的SVM分类机器学习

2017 IEEE Optical Interconnects Conference (OI) Pub Date : 2017-06-01 DOI:10.1109/OIC.2017.7965524

Guoyao Chen, Lin Sun, Ke Xu, Jiangbing Du, Zuyuan He

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引用次数: 6

摘要

提出了一种基于完全二叉树结构的支持向量机的PAM-N系统有效非线性决策框架的机器学习方法。仿真结果表明，在100 gbps PAM-4和PAM-8系统中，该分类器的接收端功率灵敏度分别提高了2 db和6 db。

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Machine learning of SVM classification utilizing complete binary tree structure for PAM-4/8 optical interconnection

A machine learning method of effective nonlinear decision frame for PAM-N system based on support vector machine (SVM) using complete binary tree (CBT) structure is demonstrated in this work. The simulations results indicate improved performance by the proposed classifier which enhances the power sensitivity by 2-dB and 6-dB at the receiver side in 100-Gbps PAM-4 and PAM-8 systems respectively.

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2017 IEEE Optical Interconnects Conference (OI)

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