# Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

## TL;DR

This paper proposes a low-complexity method for estimating channel covariance matrices in hybrid MIMO mmWave systems, addressing the challenge of lower-dimensional observations due to hybrid structures.

## Contribution

It formulates the covariance estimation as a structured low-rank matrix sensing problem and introduces an efficient algorithm to solve it.

## Key findings

- Effective covariance estimation demonstrated with ULA and USPA arrays.
- Proposed method outperforms traditional approaches in accuracy and complexity.
- Numerical results validate the approach's practicality in mmWave MIMO systems.

## Abstract

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as a potential candidate for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method.