由于存在导频污染问题，基站侧高效、高精度地获取信道状态信息对实现大规模多输入多输出(MIMO)正交频分复用系统的潜在优点至关重要。通过使用压缩感知技术，可以有效解决导频污染问题。然而，在压缩感知信道估计算法中，很难直接获取信道稀疏度的先验知识。为了解决这个问题，文章提出一种基于贝叶斯压缩感知的信道估计方法，该方法将稀疏信号的统计信息作为先验知识，并运用于多用户大规模MIMO系统的上行链路。仿真结果表明，与传统的信道估计方法相比，所提方法能有效重构原始信道系数。

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.