Simplified SVM Equalization Algorithm Based on Single Hyperplane Training Enabled 50Gb/s PAM-4/8 With 10-G Optics in NG-PON System

Abstract

In this paper, we firstly present a novel equalizer only with a single support vector machine (SVM) training hyperplane to compensate the distortion of multi-class PAM-M signals in lowcost limited-bandwidth next generation passive optical network (NG-PON) system. For this scheme, only single SVM-based hyperplane is trained, and then it is extracted and used to realize the multi-classification task of PAM-M by adjusting the corresponding offset, which would greatly reduce the complexity of equalization algorithm and enhance its application in future high-speed PON system. Moreover, the 50-Gb/s PAM-4/8 per wavelength transmission experiment with 10-G optics in 20km standard single mode fiber (SSMF) PON system are set up and utilized to manifest the feasibility and effectiveness of our scheme. The results show that, compared with the common SVM scheme with multi-classification hyperplane training, our method can reduce M-1 times computational complexity caused by hyperplane training for multiple-level pulse amplitude modulation (PAM-M) signals. Except this, the propose scheme can also maintain almost the same bit error ratio (BER) performance as the common SVM classifier with multi-hyperplane case.

SVM-CNN Hybrid Classification for Waste Image Using Morphology and HSV Color Model Image Processing

“…Support Vector Machine (SVM) is a classification model that uses a hyperplane as an intermediary for each category as a differentiator for each category [15]. Usually, SVM is used for two categories, but it is possible to use it for more than two categories [16,17].…”