Uplink channel estimation method of large-scale MIMO system

Abstract

The invention provides an uplink channel estimation method of a large-scale MIMO system, and the method comprises the following steps: (1) modelling for a probability model of a channel through usinga Gaussian mixture model; (2) performing channel estimation through using optimal Bayesian parameter estimation; (3) giving an iterative initial value through using a hierarchical clustering algorithm; (4) solving an edge probability density function in the step (2) through using an approximate message passing algorithm; (5) iteratively solving parameters of the Gaussian mixture model through using an expectation maximization algorithm. In the method, sparse characteristics of channel gain in a beam domain are used fully, the Bayesian parameter estimation method is used, learning statistical information in advance is not needed, and compared with the conventional channel estimation based on LS, better MSE performance can be obtained.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to an uplink channel estimation method of a massive MIMO system. Background technique [0002] In a massive MIMO system, the wireless channel is affected by shadow fading and frequency selective fading, and has great randomness, which brings great challenges to receiver design. The coherent detection in the receiver requires channel state information, and channel estimation technology is used to solve this problem. Whether the channel estimation is accurate will directly affect whether the receiving end can correctly demodulate the transmitted signal, which is a measure of the performance of a wireless communication system. important indicators. Therefore, the research of channel estimation algorithm is an important work. [0003] Traditional channel estimation algorithms are based on least squares (LS) estimation and minimum mean square error (MMSE) estimation. Th...

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Problems solved by technology

However, the current research on channel estimation using machine learning is limited, only literature [4] (Chao-Kai Wen, Shi Jin, Kai-Kit Wong, et al.Channel Estimation for MassiveMIMO Using Gaussian-Mixture Bayesian Learning[J].IEEE Trans.Wireless Commun,2015, 14(3):1356–1368.) proposed A Bayesian channel estimation algorithm based on Gaussian mixture model (GMM) can achieve better mean square error (MSE) performance, but when taking the initial value of GMM parameter iteration, the algorithm adopts an average selection Although this method is simple, its convergence speed is very slow

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