GEVD-based Low-Rank Channel Covariance Matrix Estimation and MMSE Channel Estimation for Uplink Cellular Massive MIMO Systems

TL;DR

This paper introduces a GEVD-based low-rank covariance matrix estimator for uplink massive MIMO systems, enabling improved MMSE channel estimation with minimal system requirements and no inter-cell communication.

Contribution

It proposes a novel low-rank covariance matrix estimator using GEVD that reduces system complexity and enhances MMSE channel estimation in massive MIMO systems.

Findings

The proposed estimator effectively estimates covariance matrices from uplink data.

Numerical simulations show improved channel estimation accuracy.

The method requires minimal system assumptions and no inter-cell communication.

Abstract

Uplink channel estimation is a crucial component for the performance of cellular massive MIMO systems. However, when the number of user equipments (UEs) grows, the sharing of the available resources causes interference between UEs in neighboring cells. Minimum mean squared error (MMSE) channel estimators have been proposed to mitigate this interference, but these require estimates of the channel covariance matrices. Therefore, a new channel covariance matrix estimator for low-rank channel covariance matrices is presented in this paper, using a generalized eigenvalue decomposition (GEVD) of two covariance matrices that can be estimated from the available uplink data. The requirements for the systems are minimal and, except for synchronization, there is no need for communication between the different cells and no prior knowledge on the background noise is required. Approximate MMSE estimators are also derived based on the newly proposed channel covariance matrix estimator. The effectiveness of the proposed methods is demonstrated in numerical simulations.