# MMSE Channel Estimation in Large-Scale MIMO: Improved Robustness with Reduced Complexity

arXiv: https://arxiv.org/abs/2404.03279 · HTML: https://mlsanity.com/paper/2404.03279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2024.

## Built on by (most cited first, 1 of 1)

- [HeterCSI: Channel-Adaptive Heterogeneous CSI Pretraining Framework for Generalized Wireless Foundation Models](https://mlsanity.com/paper/2601.18200/) (2026)

## Abstract (arXiv)

Large-scale MIMO systems with a massive number N of individually controlled antennas pose significant challenges for minimum mean square error (MMSE) channel estimation, based on uplink pilots. The major ones arise from the computational complexity, which scales with N3, and from the need for accurate knowledge of the channel statistics. This paper aims to address both challenges by introducing reduced-complexity channel estimation methods that achieve the performance of MMSE in terms of estimation accuracy and uplink spectral efficiency while demonstrating improved robustness in practical scenarios where channel statistics must be estimated. This is achieved by exploiting the inherent structure of the spatial correlation matrix induced by the array geometry. Specifically, we use a Kronecker decomposition for uniform planar arrays and a well-suited circulant approximation for uniform linear arrays. By doing so, a significantly lower computational complexity is achieved, scaling as N√N and Nlog N for squared planar arrays and linear arrays, respectively.