A fiber nonlinearity compensation scheme based on an unsupervised-learning neural network is proposed. In the proposed scheme, labels in the training data and weights of the neural network are iteratively updated until converging. To validate the proposed scheme, a 3200 km dual-polarization 16-QAM simulation link and an 1800 km single-polarization experimental link were carried out. Simulation and experiment results validate that the proposed method can achieve the same equalization performance as the supervised-learning-neural-network-based scheme, without any pre-defined training data.
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