Optical Comb Generator-based Microwave Photonic Filter Performance Improvement Using Multilayer Perceptron (MLP) Neural Network

In this paper, we proposed multilayer perceptron (MLP) neural network for improving the performance of a microwave photonic filter (MPF). Moreover, we proposed an optical comb generator to attain a central reconfigurable MPF. The frequency response of MPF varies due to some factors such as fiber length, optical carrier, and wavelength spacing. Thus, we can accurately adjust the center frequency of the MPF and its bandwidth by varying these factors, which enhances the network flexibility and very important for future optical networks. However, very difficult to determine these factors if we assign the MPF's transmission window randomly. Thus, we apply MLP technique to learn and understand the inverse mapping between the frequency response and the MPF parameters such as length of the fiber and wavelength spacing for precise prediction of the given arbitrary frequency response. Therefore, the experimental result proves that the proposed MLP model accurately predicts the parameters of MPF such as length of the fiber and wavelength spacing.