Deep learning for BER prediction in optical connections impaired by inter-core crosstalk

Abstract

Four-level pulse amplitude modulation (PAM4) signals transmission in short-haul intensity modulation-direct detection datacenters connections supported by homogeneous weakly-coupled multicore fibers is seen as a promising technology to meet the future challenge of providing enough bandwidth and achieve high data capacity in datacenter links. However, in multicore fibers, inter-core crosstalk (ICXT) limits significantly the performance of such short-reach connections by causing large bit error rate (BER) fluctuations. In this work, a convolutional neural network (CNN) is proposed for eye-pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by ICXT, with the aim of optical performance monitoring. The performance of the CNN is assessed by estimation of the root mean square error (RMSE) using a synthetic dataset created with Monte Carlo simulation. Considering PAM4 interdatacenter connections with one interfering core and for different skew-symbol rate products, extinction ratios and crosstalk levels, the obtained results show that the implemented CNN is able to predict the BER without surpassing a RMSE limit of 0.1.