An Iterative SAGE based Semi-Blind Channel Estimation for Massive MIMO

In this paper, we have proposed an iterative spacealternating generalized expectation maximization (SAGE) based semi-blind channel estimation technique for massive multipleinput multiple-output (MIMO) system in multi-cell pilot contamination prone scenario. The benefits of massive MIMO depend largely on the accuracy of channel state information (CSI) available at the base station (Transmitter). The proposed method improves the accuracy of conventional pilot based minimum mean squared error (MMSE) estimator by iteratively updating the estimate with the help of SAGE algorithm. The received data signals are incorporated along with the received pilot signals to obtain an improved channel estimate without the additional pilot symbols, which in turn improves the spectral efficiency of the system. Most of the existing estimators reported in the literature assume full knowledge of large scale fading coefficients of the interfering cells which is practically infeasible and incurs heavy overheads. The proposed estimator overcomes this problem with the help of an estimate obtained from the received samples. The proposed algorithm converges in almost one iteration, and achieves significant improvement over the existing pilot based estimator reported in the literature.