Abstract
In this paper, we study the uplink channel estimation based on machine learning algorithm in massive MIMO systems. Based on the sparsity of channel gains in the beam domain, we use Gaussian mixture model (GMM) to model the channel. The expectation maximization (EM) algorithm is adopted to obtain the parameters of GMM. Bayesian algorithm is used to estimate the channel gains. The approximate message passing (AMP) algorithm is used to solve the multiple integrals in Bayesian estimation algorithm to reduce the computational load. When determining the initial values of AMP and EM algorithms, the hierarchical clustering algorithm is adopted to improve the mean square error (MSE) and convergence performance of the algorithm. Simulation results show that the performance of the proposed algorithm is better than that of the traditional least square (LS) algorithm and the existing Bayes-GMM algorithm.
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