MAPPING EDFA NOISE FIGURE AND GAIN FLATNESS OVER THE POWER MASK USING NEURAL NETWORKS
Keywords:Artificial Neural Networks, Multilayer Perceptron, Optical Amplifiers, Device Characterization
Optical Amplifiers play an important hole in reconfigurable optical communications networks. The device characterization within the dynamic operational range is crucial for the proper deployment and usage of such devices. In general, one needs to measure a certain number of operation points to complete the characterization. In spite of this, there is a lot of missing data for some operational deployment cases. We show that one can use simple neural networks to execute a regression task and obtain a continuous characterization curve of Gain Flatness and Noise Figure performance, along the entire Amplifier Power Mask. This regression can be made using a lower number of points than usual. We obtained estimated errors lower than 0.1Â dB for Gain Flatness and Noise Figure over the entire operational range.
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