Due to the limitations of pilot pollution problems

efficient and highly accurate channel state information at the base station is critical to the potential benefits of implementing massive Multiple Input Multiple Output（MIMO）Orthogonal Frequency Division Multiplexing（OFDM）systems.It has recently been shown that Compressed Sensing（CS）techniques can solve pilot contamination problems.However

in the CS channel estimation algorithm

it is difficult to obtain the prior knowledge of channel sparseness directly.In order to solve this problem

an effective channel estimation method based on Bayesian Compressied Sensing（BCS）is proposed.The method uses sparse signal statistics as a priori knowledge to the multiuser multi-scale MIMO system uplinks.The simulation results show that the proposed method can reconstruct the original channel coefficients more effectively than the traditional channel estimation method.