A MIMO Neural Network Integrated With Maximum Likelihood Phase Recovery for Transoceanic Coherent Transmission

Abstract

We proposed a low-complexity multi-input multi-output neural network integrated with a maximum likelihood phase recovery algorithm (MIMO-NN-BMLPR), which is adopted in long-haul coherent optical communication. Neural network based equalization can effectively overcome the nonlinear distortions induced during long-haul transmission. However, the forward neural network equalizer is difficult to track the phase of coherent optical signals. We integrate an additional maximum likelihood phase recovery layer with the MIMO-NN structure so that the phase noise can be compensated. In this work, 8-channel 100-GBaud dual-polarization (DP) PS-16QAM signals are successfully transmitted over 6400 km and satisfy the normalized generalized mutual information (NGMI) threshold at 0.86. The proposed MIMO-NN-BMLPR integrates the low-complexity phase estimation with the NNE to improve the performance. Compared with the traditional DSP structure, the proposal prolongs the distance by 48.84% with a 39.80% complexity reduction.