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• W4224297992 abstract "The demand for high-speed data transmission has increased rapidly over the past few years, leading to advanced optical communication techniques. However, most of machine learning based equalizers which are trained with offline dataset mainly focus on achieving low BER, neglecting the generalization ability when the properties of optical link change. In this paper, we propose Gaussian Mixture model (GMM) based low-complexity adaptive machine-learning equalizers. The proposed online training strategy can fine-tune the parameters in GMM with a small amount of training sequence by introducing Maximum a posteriori probability (MAP) algorithm and sliding window. The size of online training sequence can be reduced by utilizing the parameters trained from offline data as the priori information to reduce the size of online training sequence. The proposed adaptive GMM based equalizers can not only show an excellent capability of mitigating nonlinear distortions but also show the ability to update parameters automatically according to the channel conditions. In addition, experimental results show that by introducing MAP algorithm and sliding window, the convergence speed of the proposed equalizers have been accelerated by 2 times, compared with the case that the traditional Expectation Maximization (EM) algorithm is used for online parameter update. On the other hand, there is no significant increase in computational complexity during the online stage." @default.
• W4224297992 created "2022-04-26" @default.
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• W4224297992 date "2022-08-01" @default.
• W4224297992 modified "2023-10-11" @default.
• W4224297992 title "GMM based low-complexity adaptive machine-learning equalizers for optical fiber communication" @default.
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• W4224297992 doi "https://doi.org/10.1016/j.optcom.2022.128312" @default.
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