Enhanced Sparse Bayesian Learning-Based Channel Estimation for Massive MIMO-OFDM Systems

Pilot contamination limits the potential benefits of massive multiple input multiple output (MIMO) systems. To mitigate pilot contamination, in this paper, an efficient channel estimation approach is proposed for massive MIMO systems, using sparse Bayesian learning (SBL) namely coupled hierarchical Gaussian framework where the sparsity of each coefficient is controlled by its own hyperparameter and the hyperparameters of its immediate neighbours. The simulation results show that the proposed method can reconstruct original channel coefficients more effectively compared to the conventional channel estimators in terms of channel estimation accuracy in the presence of pilot contamination.