Optical fiber nonlinearity equalizer with support vector regression based on perturbation theory

Abstract

With the development of high-speed and large-capacity optical fiber communication technology, nonlinear damage has been one great obstacle to the development of optical fiber communication. Transmission distance and signal energy are two main factors that affect the nonlinear damage. The nonlinear compensation algorithm based on perturbation theory can compensate nonlinear damage. However, the coefficients of perturbation matrix cannot be calculated easily. In this paper, we make use of machine learning as a tool, propose an equalizer with support vector regression based on perturbation theory. For a 120 Gb/s, 375 km transmission distance, dual-polarization 64 quadrature amplitude modulation communication system, bit error ratio (BER) is lower than hard-decision forward error correction threshold 3.8×10−3 in the launched optical power (LOP) from -4 dBm to 3 dBm and when LOP is 1 dBm, BER is lower than forward error correction threshold 1.0×10−3 . Moreover, it is shown that the optimum LOP is increased by 2 dB.