Stochastic Channel Models for Massive and XL-MIMO Systems

TL;DR

This paper systematically compares stochastic channel models for massive MIMO and XL-MIMO systems, analyzing their behavior and impact on performance metrics like capacity and SINR, especially considering near-field effects.

Contribution

It provides a comprehensive comparison of different massive MIMO channel models and investigates the influence of cluster location on precoding performance in XL-MIMO scenarios.

Findings

Cluster location affects conjugate beamforming and ZF precoding performance.

Different models exhibit distinct behaviors in capacity and SINR metrics.

Near-field and visible region effects are significant in XL-MIMO modeling.

Abstract

In this paper, stochastic channel models for massive MIMO (M-MIMO) and extreme large MIMO (XL- MIMO) system applications are described, evaluated and systematically compared. This work aims to cover new aspects of massive MIMO stochastic channel models in a comprehensive and systematic way. For that, we compare different models, presenting graphically and intuitively the behavior of each model. Each massive MIMO channel model emulates the environment using different methodologies and properties. Using metrics such as capacity, SINR, singular values decomposition (SVD), and condition number, one can understand the influence of each characteristic on the modelling and how it differentiates from other models. Moreover, in new XL-MIMO scenarios, where the near-field and visible region (VR) effects arise, our finding demonstrate that for the two assumed schemes of clusters distribution, the clusters location influences the performance of the conjugate beamforming and zero-forcing (ZF) precoding due to the correlation effect, which have been analysed from the geometric massive MIMO channel models.