Mitigating Fiber Nonlinearity with Machine Learning
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Nowadays, optical communication transmission is based mainly on optical fiber networks. Increasing demands for higher-capacity systems are hampered by signal distortions due to nonlinear effects of the commercial optic fibers. Different techniques have been proposed to reverse and mitigate this noise effect on the transmitted signal such as the digital backpropagation (DBP), the Volterra nonlinear compensation, the advanced modulation transmission, and perturbation pre-compensation techniques. While these techniques achieve good results they are too complicated for practical industrial implementation and add more complexity overhead on the system. This thesis is focused on investigating the merits of optical fiber mitigation using Artificial Intelligence (AI) techniques instead of analytical methods. Different AI techniques combined with perturbation-based nonlinear compensation method are used to predict the added nonlinear noise to a 16-Quadrature Amplitude Modulation (QAM) propagating signal. A MATLAB simulation program has been used to model the propagation of the signal and generate the transmitted data. The AI simulations have been employed using Python on dual-polarization single channel systems using single-stage AI techniques such as Neural Network (NN) at receiver or transmitter side and Siamese neural network (SNN), or two-stage AI techniques. In the two-stage method, different supervised classifiers have been used at the receiver side such as multi-layer perceptrons (MLP), decision tree, AdaBoosting, GBoosting, random forest, and extra trees while NN is placed at the transmitter. Additionally, different complexity reduction techniques have been applied to the proposed systems to achieve more practical performance in industrial environment applications. For the first time, a nonlinear-compensation robustness study is applied to the proposed AI techniques by detecting the performance of each technique while changing the single-mode fiber’s nonlinear coefficient value. Moreover, empirical equations are developed to represent the system’s Q-factor enhancement achieved using each of the proposed techniques as a function of the fiber nonlinear coefficient and the data features.
Cite this version of the work
Marina Melek (2021). Mitigating Fiber Nonlinearity with Machine Learning. UWSpace. http://hdl.handle.net/10012/17780