Method and device for estimating parameters of mixture Gaussian distribution

Abstract

Embodiments of the invention provide a method and a device for estimating parameters of mixture Gaussian distribution. According to the method, when mixture Gaussian is used for modeling, all initial component parameters and initial mixed coefficients of a mixture Gaussian probability density function are estimated according to a clustering algorithm, then all the initial component parameters and initial mixed coefficients are taken as initial values of an expectation maximization (EM) algorithm or a greedy EM algorithm, and iterative operation is performed through the EM algorithm or the greedy EM algorithm so as to obtain all final component parameters and final mixed coefficients of the mixture Gaussian probability density function. Through adoption of the method, the clustering algorithm provides a coarse estimation of the parameters of the mixture Gaussian probability density function; on the basis of the coarse estimation of the parameters, the EM algorithm improves the precision of parameter estimation, so that combination of the clustering algorithm and EM algorithm guarantees a likelihood function to converge to global maximum point, the convergence time of the likelihood function is shortened and the implementation complexity is lowered.

Description

technical field [0001] Embodiments of the present invention relate to data communication technology, and in particular to a method and device for estimating parameters of a mixed Gaussian distribution. Background technique [0002] The Multiple Input Multiple Output (MIMO) system can double the capacity and spectrum utilization of the communication system without increasing the bandwidth. MIMO combines Orthogonal Frequency Division Multiplexing (Orthogonal Frequency Division Multiplexing, OFDM for short) technology constitutes the MIMO OFDM system and is also two key technologies in the Long Term Evolution (LTE for short) system. An important technology in MIMO technology is the detection technology of space division multiplexing. The commonly used detection algorithms include Turbo detection algorithm based on minimum mean square error (MMSE for short) and decoding feedback, and based on QR decomposition and M Algorithm (QR decomposition based M-algorithm, QRD-M for short)...

AI Technical Summary

Problems solved by technology

However, in the prior art, the accuracy of the estimated parameters of the EM algorithm is related to the initial value, and the initial value is considered to be set. If the initial value is not selected properly, the algorithm will not converge or converge to a local maximum, and different initial values The parameters lead to different convergence speeds, and the implementation complexity is not uniform

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