  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.
