In this paper, the interference cancellation information geometry approaches (IC-IGAs) for massive MIMO channel estimation are proposed. The proposed algorithms are low-complexity approximations of the minimum mean square error (MMSE) estimation. To illustrate the proposed algorithms, a unified framework of the information geometry approach for channel estimation and its geometric explanation are described first. Then, a modified form that has the same mean as the MMSE estimation is constructed. Based on this, the IC-IGA algorithm and the interference cancellation simplified information geometry approach (IC-SIGA) are derived by applying the information geometry framework. The a posteriori means on the equilibrium of the proposed algorithms are proved to be equal to the mean of MMSE estimation, and the complexity of the IC-SIGA algorithm in practical massive MIMO systems is further reduced by considering the beam-based statistical channel model (BSCM) and fast Fourier transform (FFT). Simulation results show that the proposed methods achieve similar performance as the existing information geometry approach (IGA) with lower complexity.

翻译：本文提出了用于大规模MIMO信道估计的干扰消除信息几何方法（IC-IGAs）。所提算法是最小均方误差（MMSE）估计的低复杂度近似。为阐明所提算法，首先描述了用于信道估计的信息几何方法的统一框架及其几何解释。随后，构建了一个与MMSE估计具有相同均值的修正形式。在此基础上，通过应用信息几何框架推导出IC-IGA算法及干扰消除简化信息几何方法（IC-SIGA）。理论证明了所提算法在均衡状态下的后验均值等于MMSE估计的均值，并且通过结合基于波束的统计信道模型（BSCM）与快速傅里叶变换（FFT），进一步降低了IC-SIGA算法在实际大规模MIMO系统中的复杂度。仿真结果表明，所提方法在取得与现有信息几何方法（IGA）相近性能的同时，具有更低的计算复杂度。