Channel Estimation in Massive MIMO Systems Using a Modified Bayes-GMM Method
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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.
KeywordsChannel estimation GMM Bayesian estimation Hierarchical clustering
This work was supported by the National Natural Science Foundation of China (No. 61271235) and the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education.
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