Large-scale MIMO low-complexity channel estimation method based on weighted Kapetyn grade number expansion

Abstract

The invention discloses a large-scale MIMO low-complexity channel estimation method based on weighted Kapetyn grade number expansion. Firstly a Kapetyn grade number expansion method is utilized for performing approximate expansion on a channel covariance inverse matrix in a Bayesian-MMSE channel estimation expression. A matrix inversion operation is converted to matrix multiplication and matrix addition operations. Then a weighting manner is performed on each coefficient of a polynomial for optimizing polynomial expansion, establishing a model for solving weighting coefficient vectors alpha and beta for minimizing an estimated mean square error, and estimating the channel matrix by means of solving results of alpha and beta. Experiment results represent a fact that an MSE which is obtained through the channel estimation method based on a weighted Kapetyn grade number expansion is convergent to an MMSE method along with order number increase of the polynomial, and furthermore calculation complexity of the channel estimation method is lower than that of the MMSE method. Compared with a traditional Taylor-MMSE and Kapetyn grade number expansion channel estimation method, the channel estimation method based on the weighted Kapetyn grade number has higher convergence speed to the MMSE method.

Description

technical field [0001] The invention belongs to the field of mobile communication, and mainly relates to a low-complexity channel estimation algorithm in a massive MIMO system. Specifically, the weighted-based Kapetyn series expansion algorithm is used to estimate the channel state information of the massive MIMO system, and the model is established to use the iterative method to solve the optimal solution of the polynomial coefficients of the series expansion, so that the MSE estimated by the algorithm converges to the MMSE faster algorithm. Background technique [0002] The massive MIMO system is one of the core technologies of the fifth-generation mobile communication system. In this system, the base station is equipped with a large number of antennas (greater than 100) to serve more mobile users in order to obtain higher spectral efficiency, data transmission rate and throughput and better communication quality. Efficient CSI acquisition is a key issue for massive MIMO...

AI Technical Summary

Problems solved by technology

[0003] The traditional MMSE estimation algorithm to estimate CSI needs to calculate the inverse of a high-dimensional matrix, which is very complex. Using the Kapetyn series expansion method instead of matrix inversion to estimate the channel can significantly reduce the computational complexity. When the polynomial order N and When the Kapteyn order K tends to infinity, the MSE (mean square error) estimated by Kapetyn series expansion will converge to the MMSE method

However, in practice, for fixed N and K, the estimation error of Kapetyn series expansion is too large

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