Information Entropy and Fuzzy Logic Based Equalizer for PolMux QAM Coherent Optical Communication Systems
In polarization-multiplexed (PolMux) coherent optical communication systems, adaptive blind equalizer is efficient in demultiplexing and mitigating intersymbol interference (ISI). A novel blind algorithm based on Information Entropy and fuzzy logic is proposed, in which the Renyi's $\alpha$ entropy is adopted to measure the uncertainty of error between the desired and estimated probability density function (PDF). The nonparametric PDF estimator of Parzen window method is employed to estimate the PDF of symbols. Meantime, a fuzzy-logic tuning unit is designed to adjust the kernel size of Parzen window, which leads to fast convergence rate and small steady mean-square error. By simulation in PolMux-16 quadrature amplitude modulation (QAM) coherent systems, the correctness and effectiveness of the proposed algorithm are verified.
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Information Entropy and Fuzzy Logic Based Equalizer for PolMux QAM Coherent Optical Communication Systems
Semantic Scholar · Computer Science · 2017
Abstract
In polarization-multiplexed (PolMux) coherent optical communication systems, adaptive blind equalizer is efficient in demultiplexing and mitigating intersymbol interference (ISI). A novel blind algorithm based on Information Entropy and fuzzy logic is proposed, in which the Renyi's $\alpha$ entropy is adopted to measure the uncertainty of error between the desired and estimated probability density function (PDF). The nonparametric PDF estimator of Parzen window method is employed to estimate the PDF of symbols. Meantime, a fuzzy-logic tuning unit is designed to adjust the kernel size of Parzen window, which leads to fast convergence rate and small steady mean-square error. By simulation in PolMux-16 quadrature amplitude modulation (QAM) coherent systems, the correctness and effectiveness of the proposed algorithm are verified.
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