EM/SAGE algorithm based iterative channel estimation in multi-cell massive MIMO systems

Abstract

This paper represents efficient expectation-maximization (EM) and space-alternating generalized expectation-maximization (SAGE) algorithm based iterative channel estimation methods for multi-cell massive multiple input multiple output (MIMO) systems. The proposed iterative channel estimation methods converge to the same mean square error (MSE) performance of the least squares (LS) estimator with the increasing number of iterations. LS channel estimation method requires conjugate transpose of a large-size pilot matrix. Conjugate transpose of the large-size matrix increases computational complexity. The size of the pilot matrix depends on the number of users. As the number of users increases, computational complexity increases too. In the proposed iterative channel estimation methods, the size of the pilot matrix is reduced to the vector size and the computational complexity is significantly reduced. The SAGE algorithm can estimate the channel by performing fewer iterations than EM, so it is a preferable method in terms of convergence speed.