# Channel Correlation Diversity in MU-MIMO Systems -- Analysis and Measurements

## TL;DR

This paper analyzes the impact of diverse, physically motivated channel correlation models on MU-MIMO system performance, showing that realistic correlation variations significantly influence spectral efficiency and system metrics.

## Contribution

It introduces measurement-based correlation models capturing variations across terminals and derives closed-form expressions for key performance metrics under these models.

## Key findings

- Diverse correlation patterns significantly affect system performance.
- Measurement-based models provide more accurate performance predictions.
- Closed-form approximations offer insights into correlation impacts.

## Abstract

In multiuser multiple-input multiple-output (MU-MIMO) systems, channel correlation is detrimental to system performance. We demonstrate that widely used, yet overly simplified, correlation models that generate identical correlation profiles for each terminal tend to severely underestimate the system performance. In sharp contrast, more physically motivated models that capture variations in the power angular spectra across multiple terminals, generate diverse correlation patterns. This has a significant impact on the system performance. Assuming correlated Rayleigh fading and downlink zero-forcing precoding, tight closed-form approximations for the average signal-to-noise-ratio, and ergodic sum spectral efficiency are derived. Our expressions provide clear insights into the impact of diverse correlation patterns on the above performance metrics. Unlike previous works, the correlation models are parameterized with measured data from a recent 2.53 GHz urban macrocellular campaign in Cologne, Germany. Overall, results from this paper can be treated as a timely re-calibration of performance expectations from practical MU-MIMO systems.