Correlation-Aware Minimum Mean Square Error Channel Estimation for Uplink Massive Multiple-Input Multiple-Output Systems in Sixth-Generation Networks

Abstract

Massive Multiple-Input Multiple-Output (mMIMO) is a key enabling technology for beyond fifth-generation (B5G) and sixth-generation (6G) wireless systems; however, its performance critically depends on accurate channel state information (CSI). Conventional minimum mean square error (MMSE) channel estimators are typically derived under the assumption of spatially uncorrelated fading, which rarely holds in practical deployments due to antenna coupling, limited spacing, and structured propagation environments. As a result, spatial correlation can significantly degrade estimation accuracy. This paper investigates uplink channel estimation for mMIMO systems under spatially correlated channels. First, an analytical characterization of the effect of correlation on the traditional MMSE estimator is presented. Then, two correlation-aware estimation techniques are developed. The first approach introduces a regularized MMSE formulation to improve robustness in correlated scenarios. The second approach proposes a pre-whitening-based MMSE estimator that exploits channel covariance information to transform the correlated channel into an equivalent white channel prior to estimation. Closed-form solutions for the optimal filters are derived, and their complexity is calculated. The results of the simulation verify that the pre-whitening based estimator outperforms the conventional and regularized MMSE estimators over a wide interval for the value of the signal to noise ratio (SNR) and the correlation coefficient.