Method and system for gmm non-uniform quantization for filter multi-carrier modulation optical communication
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, and achieve good quantization effects, good signal estimation effects, and improved performance effect
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Embodiment 1
[0066] The first embodiment provides a GMM non-uniform quantization system for filtering multi-carrier modulation optical communication, such as figure 1 It includes: a preprocessing module 11, a calculation module 12, and 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 the 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 from the real part signal sample vector, and obtain respectively. The most responsive Gaussian ...
Embodiment 2
[0116] This embodiment provides a method for GMM non-uniform quantization for filter multi-carrier modulated optical communication, comprising the steps of:
[0117] S11. carry out data preprocessing to the data sequence sent by the filter bank multi-carrier modulation transmitter, obtain a sample vector, and use the sample vector as the input vector of the GMM quantization module; the sample vector includes the real part signal sample vector and the imaginary part. Partial signal sample vector;
[0118] S12. Calculate the GMM parameter of the input real part signal sample vector according to the maximum expectation algorithm, and then estimate the GMM parameter of the imaginary part signal sample vector according to the GMM parameter calculated from the real part signal sample vector, and obtain the Gaussian mixture with the largest responsivity respectively. Model;
[0119] S13. The sample is input into the Gaussian mixture model with the largest responsivity, and the clust...
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