Channel Modeling and Channel Estimation for Holographic Massive MIMO With Planar Arrays

TLDR

In realistic wireless environments, spatially correlated fading is pronounced in holographic massive MIMO systems with densely deployed antennas, making full knowledge of the spatial correlation matrix difficult to obtain. The paper aims to develop a channel model for holographic massive MIMO that accounts for non‑isotropic scattering and directive antennas, and to propose a geometry‑aware channel estimation scheme that identifies a reduced‑rank subspace covering any spatial correlation matrix. The authors compare the conventional LS estimator with a novel geometry‑aware estimator that exploits array geometry to identify a reduced‑rank subspace covering any spatial correlation matrix. The geometry‑aware estimator outperforms the LS estimator without requiring user‑specific channel statistics.

Abstract

In a realistic wireless environment, the multi-antenna channel usually exhibits spatially correlated fading. This is more emphasized when a large number of antennas is densely deployed, known as holographic massive MIMO (multiple-input multiple-output). In the first part of this letter, we develop a channel model for holographic massive MIMO by considering both non-isotropic scattering and directive antennas. With a large number of antennas, it is difficult to obtain full knowledge of the spatial correlation matrix. In this case, channel estimation is conventionally done using the least-squares (LS) estimator that requires no prior information of the channel statistics or array geometry. In the second part of this letter, we propose a novel channel estimation scheme that exploits the array geometry to identify a subspace of reduced rank that covers the eigenspace of any spatial correlation matrix. The proposed estimator outperforms the LS estimator, without using any user-specific channel statistics.