Methods and systems for GMM non-uniform quantization of filter multicarrier modulated optical communications
A filter multi-carrier and modulated light technology, which is applied in the field of optical communication, can solve the problems of high computational complexity and the large number of samples required by the non-parametric estimation histogram method
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Embodiment 1
[0066] Embodiment 1 provides a system for non-uniform quantization of GMM for filter multi-carrier modulation optical communication, such as figure 1 Shown includes: a preprocessing module 11, a calculation module 12, an output module 13;
[0067] The preprocessing module 11 is used to perform data preprocessing on the data sequence sent by the filter bank multi-carrier modulation transmitter to obtain a sample vector, and use the sample vector as an input vector of the GMM quantization module; the sample vector includes real part signal sample vector and imaginary part signal sample vector;
[0068] The calculation module 12 is used to calculate the GMM parameters of the input real part signal sample vector according to the maximum expectation algorithm, and then estimate the GMM parameters of the imaginary part signal sample vector according to the GMM parameters calculated by the real part signal sample vector, and obtain respectively Gaussian mixture model with maximum re...
Embodiment 2
[0116] This embodiment provides a method for non-uniform quantization of GMM for filter multi-carrier modulation optical communication, including steps:
[0117] S11. Perform data preprocessing on the data sequence sent by the filter bank multi-carrier modulation transmitter to obtain a sample vector, and use the sample vector as the input vector of the GMM quantization module; the sample vector includes a real part signal sample vector and an imaginary vector Internal signal sample vector;
[0118] S12. Calculate the GMM parameter of the input real part signal sample vector according to the maximum expectation algorithm, then estimate the GMM parameter of the imaginary part signal sample vector according to the GMM parameter calculated by the real part signal sample vector, and obtain the Gaussian mixture with the largest responsivity respectively Model;
[0119] S13. Input the sample into the Gaussian mixture model with the largest responsiveness to perform clustering opera...
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