Multiparameter performance monitoring of pulse amplitude modulation channels using convolutional neural networks

A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.

Ultra-flat dispersion in an integrated waveguide with five and six zero-dispersion wavelengths for mid-infrared photonics

We propose a new type of dispersion flattening technology, which can generate an ultra-flat group velocity dispersion profile with five and six zero-dispersion wavelengths(ZDWs). The dispersion value varies from-0.15 to 0.35 ps/(nm·km) from 4 to 8 μm, which to the best of our knowledge is the flattest one reported so far, and the dispersion flatness is improved by more than an order of magnitude. We explain the principle of producing six ZDWs. Mode distribution in this waveguide is made stable over a wide bandwidth. General guidelines to systematically control the dispersion value, sign, and slope are provided, and one can achieve the desired dispersion by properly adjusting the structural parameters. Fabrication tolerance of this waveguide is also examined.