Interference Cancellation Information Geometry Approach for Massive MIMO Channel Estimation

TL;DR

This paper introduces low-complexity interference cancellation algorithms for massive MIMO channel estimation based on information geometry, achieving near-MMSE performance with reduced computational costs.

Contribution

The paper develops novel IC-IGA and IC-SIGA algorithms that approximate MMSE estimation using information geometry, with reduced complexity suitable for practical massive MIMO systems.

Findings

Algorithms achieve similar performance to existing IGA

Reduced complexity with beam-based statistical channel model and FFT

Proven mean equivalence to MMSE estimation

Abstract

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.