Machine learning for performance improvement of periodic NFT-based communication system

O. Kotlyar, Morteza Kamalian Kopae, J. Prilepsky, M. Pankratova, S. Turitsyn
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引用次数: 5

Abstract

We compare performance of several machine learning methods, including support vector machine, k-nearest neighbours, k-means clustering, and Gaussian mixture model, used for increasing transmission reach in the optical communication system based on the periodic nonlinear Fourier transform signal processing
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基于周期性nft通信系统性能改进的机器学习
我们比较了几种机器学习方法的性能,包括支持向量机、k近邻、k均值聚类和高斯混合模型,这些方法用于基于周期性非线性傅立叶变换信号处理的光通信系统中增加传输距离
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