Year: 2020
Type: conference-paper
Abstract: Obtaining channel covariance knowledge is of great importance in various Multiple-Input Multiple-Output MIMO communication applications, including channel estimation and user grouping. Considering recently proposed massive MIMO systems, covariance estimation proves to be challenging due to the large number of antennas (M >> 1) employed in the base station. In this case, the number of pilot transmissions N becomes comparable to the number of antennas and standard estimators, such as the sample covariance, yield a poor estimate of the true covariance and are hence undesirable. In this paper, we propose a Maximum-Likelihood (ML) massive MIMO covariance estimator, based on a parametric representation of the channel angular spread function (ASF). The parametric representation emerges from super-resolving discrete ASF components plus approximating its continuous components using carefully chosen limited-support density function. We maximize the likelihood function using a Concave-Convex procedure, which is initialized via a non-negative least-squares optimization problem. Our simulation results show that the proposed method outperforms the state of the art in various estimation quality metrics.
Institution: Technische Universität Berlin
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: Strong
OpenAlex ID: W2982398665