Area Rate Evaluation based on Spatial Clustering of massive MIMO Channel Measurements

TL;DR

This paper introduces a clustering algorithm to evaluate area throughput in massive MIMO channels by distinguishing LoS and NLoS regions using spatially consistent measurements and a rank-metric criterion.

Contribution

It proposes a novel spatial clustering method for assessing area throughput in massive MIMO channels, incorporating real measurements and a new orthogonality clustering scheme.

Findings

The algorithm accurately estimates degrees of freedom in spatially consistent channels.

It effectively distinguishes LoS and NLoS regions for throughput analysis.

The method maximizes user throughput per area using real measurement data.

Abstract

Channel models for massive MIMO are typically based on matrices with complex Gaussian entries, extended by the Kronecker and Weichselberger model. One reason for observing a gap between modeled and actual channel behavior is the absence of spatial consistency in many such models, that is, spatial correlations over an area in the x, y-dimensions are not accounted for, making it difficult to study, e.g., area-throughput measures. In this paper, we propose an algorithm that can distinguish between regions of non-line-of-sight (NLoS) and line-of-sight (LoS) via a rank-metric criterion combined with a spiral search. With a k-means clustering algorithm a throughput per region (i.e., cluster) can be calculated, leading to what we refer to as "area-throughput". For evaluating the proposed orthogonality clustering scheme we use a simple filtered MIMO channel model which is spatially consistent, with known degrees of freedom. Moreover, we employ actual (spatially consistent) area channel measurements based on spatial sampling using a spider antenna and show that the proposed algorithm can be used to estimate the degrees of freedom, and, subsequently, the number of users that maximizes the throughput per square meter.