Channel Estimation for Massive MIMO Systems with Lens Antenna Array via Sparse Bayesian Learning

Massive multiple-input multiple-output (MIMO) systems with lens antenna arrays have found their way in modern communications. With the help of the beam selection characteristic in lens antenna arrays, the dimension of massive MIMO systems can be reduced. This paper investigates the channel estimation problem in massive MIMO systems with lens antenna array, where the inherent channel cluster structure is considered. In our proposed scheme, a local Beta process (LBP) is utilized to characterize a sparse structure. The uplink channel estimation problem is modelled as a sparse channel reconstruction problem, and it is solved by sparse Bayesian learning (SBL). Finally, simulation results verify that the proposed algorithm can not only achieve ideal performance of channel estimation, but also have robust adaptation to variable signal-to-noise ratio (SNR) environments.