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AssigneeNEC Corporation
InventorsShaoliang Zhang, Fatih Yaman, Eduardo Rodriguez, Yoshihisa Inada, Kohei Nakamura, Takanori Inoue
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Aspects of the present disclosure describe systems, methods and structures for optical fiber nonlinearity compensation using neural networks that advantageously employ machine learning (ML) algorithms for nonlinearity compensation (NLC) that advantageously provide a system-agnostic model independent of link parameters, and yet still achieve a similar or better performance at a lower complexity as compared with prior-art methods. Systems, methods, and structures according to aspects of the present disclosure include a data-driven model using the neural network (NN) to predict received signal nonlinearity without prior knowledge of the link parameters. Operationally, the NN is provided with intra-channel cross-phase modulation (IXPM) and intra-channel four-wave mixing (IFWM) triplets that advantageously provide a more direct pathway to underlying nonlinear interactions.
An improved nonlinearity compensation (NLC) method for optical transmission networks employing a neural network (NN-NLC) said improved NN-NLC method CHARACTERIZED IN THAT:
An improved nonlinearity compensation method for optical networks employing a neural network having training and execution stages said improved method comprising:
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