A Novel Nonlinear Equalizer for Probabilistic Shaping 64-QAM Based on Constellation Segmentation and Support Vector Machine

Abstract

The probability distribution of probabilistic shaping 64 quadrature amplitude modulation (PS-64QAM) is uneven. The traditional M-ary support vector machine (SVM) algorithm is incompatible with the data set with uneven distribution. In order to solve the problem, we propose a novel nonlinear equalizer (NLE) for PS-64QAM based on constellation segmentation (CS) and SVM, called CS M-ary SVM NLE. The performance of CS M-ary SVM NLE has been demonstrated in a 120 Gb/s PS-64QAM coherent optical communication system. The experimental results show that after employing the proposed scheme, the launched optical power dynamic range (LOPDR) of PS-64QAM can be increased by 1.6 dBm compared with the situation without NLE. In addition, aided by the proposed scheme, the LOPDR of PS-64QAM is increased by 0.6 dBm than M-ary SVM NLE. Compared with employing M-ary SVM NLE and without employing NLE, when employing the proposed scheme, the Q factor is improved about 0.50 dB and 0.96 dB, respectively. The number of support vectors (SVs) and CPU running time for both NLE schemes are collected to measure the complexity of the two NLE schemes. The results show that the complexity of the proposed scheme is lower than that of the M-ary SVM scheme under the entire measured launched optical power range from −5 dBm to +5 dBm.