ANN/Random forest based performance monitoring in high-speed short-reach optical interconnections

Abstract

In this work, we have developed signal quality monitoring approaches in 100/400 Gbit/s short-reach transmission systems, with the application of four advanced modulation formats. In 100G and 400G transmission systems, it is shown that accuracies of 100 % have been achieved in the modulation format identification (MFI), with the use of random forest (RF) and multitask learning-based artificial neural network (MTL-ANN) for the four modulation formats mentioned. Meanwhile, average mean-square errors (MSEs) of the monitored optical signal-to-noise ratio (OSNRs) are less than 0.1 dB. Random forest uses up to 29 adders and 190 comparators, reducing its complexity by two orders of magnitude compared to MTL-ANN.