Efficient Information Geometry Approach for Massive MIMO-OFDM Channel Estimation

TL;DR

This paper introduces an efficient information geometry-based method for massive MIMO-OFDM channel estimation, simplifying the process and achieving near-optimal performance with low computational complexity.

Contribution

The paper proposes a simplified and efficient IGA (EIGA) for massive MIMO-OFDM channel estimation, enabling fast implementation with FFT and proven convergence.

Findings

EIGA achieves near-optimal channel estimation performance.

EIGA converges within a wide range of damping factors.

Simulation results confirm low complexity and high accuracy.

Abstract

We investigate the channel estimation for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We revisit the information geometry approach (IGA) for massive MIMO-OFDM channel estimation. By using the constant magnitude property of the entries of the measurement matrix, we find that the second-order natural parameters of the distributions on all the auxiliary manifolds are equivalent to each other, and the first-order natural parameters are asymptotically equivalent to each other at the fixed point. Motivated by these results, we simplify the process of IGA and propose an efficient IGA (EIGA) for massive MIMO-OFDM channel estimation, which allows efficient implementation with fast Fourier transformation (FFT). We then establish a sufficient condition of its convergence and accordingly find a range of the damping factor for the convergence. We show that this range of damping factor is sufficiently wide by using the specific properties of the measurement matrices. Further, we prove that at the fixed point, the a posteriori mean obtained by EIGA is asymptotically optimal. Simulations confirm that EIGA can achieve the optimal performance with low complexity in a limited number of iterations.