  Sparsity of channel in the next generation of wireless communication for
massive multiple-input-multiple-output (MIMO) systems can be exploited to
reduce the overhead in the training. The multitask (MT)-sparse Bayesian
learning (SBL) is applied for learning time-varying sparse channels in the
uplink for multi-user massive MIMO orthogonal frequency division multiplexing
systems. In particular, the dynamic information of the sparse channel is used
to initialize the hyperparameters in the MT-SBL procedure for the next time
step. Then, the expectation maximization based updates are applied to estimate
the underlying parameters for different subcarriers. Through the simulation
studies, it is observed that using the dynamic information from the previous
time step considerably reduces the complexity and the required time for the
convergence of MT-SBL algorithm with negligible sacrificing of the estimation
accuracy. Finally, the power leakage is reduced due to considering angular
refinement in the proposed algorithm.
