Unsupervised and supervised machine learning for performance improvement of NFT optical transmission

Abstract

We apply both the unsupervised and supervised machine learning (ML) methods, in particular, the k-means clustering and support vector machine (SVM) to improve the performance of the optical communication system based on the nonlinear Fourier transform (NFT). The NFT system employs the continuous NFT spectrum part to carry data up to 1000 km using the 16-QAM OFDM modulation. We classify the performance of the system in terms of BER versus signal power dependence. We show that the NFT system performance can be improved considerably by means of the ML techniques and that the more advanced SVM method typically outperforms the k-means clustering.