Complex principal component analysis-based complex-valued fully connected NN equalizer for optical fibre communications

An increasing number of scholars have proposed many schemes to mitigate the Kerr nonlinearity effect restricting the transmission capacity of optical fibres. In this paper, we proposed a complex principal component analysis-based complex-valued fully connected neural network (P-CFNN) model aided by perturbation theory and demonstrated it experimentally on a dual-polarization 64-quadrature-amplitude modulation coherent optical communication system. What we believe to be a novel complex principal component analysis (CPCA) algorithm applied to complex-valued fully connected neural network (CFNN) is designed to further reduce the computational complexity of the model. Meanwhile, an equivalent real-valued fully connected neural network (RFNN) with the same time complexity as a CFNN is proposed for fair performance comparison. Under all launched optical powers, the performance of the P-CFNN equalizer is the best among all comparison algorithms, and the maximum ∆Q-factor compared to without employing the nonlinear compensation algorithm reaches 3.94 dB. In addition, under the constraint of the same Q-factor, we confirmed that the proposed P-CFNN obtained a 40% reduction in time complexity and a 70% reduction in space complexity compared with the PCA-based RFNN, which also proved the very large application prospect of the P-CFNN equalizer in optical fibre communication systems.