Why Does a Kronecker Model Result in Misleading Capacity Estimates?

TL;DR

This paper investigates why the Kronecker model often leads to inaccurate capacity estimates in MIMO systems, revealing that its limitations stem from fundamental structural mismatches at different SNR regimes.

Contribution

It provides a theoretical explanation for the Kronecker model's inaccuracies by linking it to channel decomposition and sparsity patterns using random matrix theory.

Findings

Kronecker model misestimates capacity due to structural assumptions.

Sparsity of channel DoF affects low-SNR capacity estimates.

Disparities in DoF distribution impact high-SNR capacity predictions.

Abstract

Many recent works that study the performance of multi-input multi-output (MIMO) systems in practice assume a Kronecker model where the variances of the channel entries, upon decomposition on to the transmit and the receive eigen-bases, admit a separable form. Measurement campaigns, however, show that the Kronecker model results in poor estimates for capacity. Motivated by these observations, a channel model that does not impose a separable structure has been recently proposed and shown to fit the capacity of measured channels better. In this work, we show that this recently proposed modeling framework can be viewed as a natural consequence of channel decomposition on to its canonical coordinates, the transmit and/or the receive eigen-bases. Using tools from random matrix theory, we then establish the theoretical basis behind the Kronecker mismatch at the low- and the high-SNR extremes: 1) Sparsity of the dominant statistical degrees of freedom (DoF) in the true channel at the low-SNR extreme, and 2) Non-regularity of the sparsity structure (disparities in the distribution of the DoF across the rows and the columns) at the high-SNR extreme.