# Covariance Matrix Estimation in Massive MIMO

## TL;DR

This paper addresses the challenge of estimating covariance matrices in massive MIMO systems, proposing methods that leverage the longer coherence interval of covariance matrices to improve estimation accuracy despite interference.

## Contribution

It introduces novel covariance matrix estimation techniques that utilize pilot sequence assignment across multiple coherence intervals to mitigate interference effects.

## Key findings

- Proposed methods improve covariance estimation accuracy.
- Enhanced channel estimation leads to better system performance.
- Techniques effectively reduce interference impact during training.

## Abstract

Interference during the uplink training phase significantly deteriorates the performance of a massive MIMO system. The impact of the interference can be reduced by exploiting second order statistics of the channel vectors, e.g., to obtain minimum mean squared error estimates of the channel. In practice, the channel covariance matrices have to be estimated. The estimation of the covariance matrices is also impeded by the interference during the training phase. However, the coherence interval of the covariance matrices is larger than that of the channel vectors. This allows us to derive methods for accurate covariance matrix estimation by appropriate assignment of pilot sequences to users in consecutive channel coherence intervals.