# Channel Modeling and Channel Estimation for Holographic Massive MIMO with Planar Arrays

arXiv: https://arxiv.org/abs/2108.04633 · HTML: https://mlsanity.com/paper/2108.04633/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2021.

## Built on by (most cited first, 2 of 2)

- [Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers](https://mlsanity.com/paper/2301.03371/) (2022)
- [HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers](https://mlsanity.com/paper/2401.05420/) (2023)

## Abstract (arXiv)

In a realistic wireless environment, the multi-antenna channel usually exhibits spatially correlation 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.