Low-rank MMSE filters, Kronecker-product representation, and regularization: a new perspective

TL;DR

This paper introduces a novel approach to efficiently determine the regularization parameter in low-rank MMSE filters using Kronecker-product representation, highlighting its importance in rank selection and demonstrating significant performance improvements.

Contribution

It presents a new method for regularization parameter selection in low-rank MMSE filters leveraging Kronecker-product representation, linking regularization to rank choice.

Findings

Significant performance gains over existing methods

Regularization parameter closely linked to rank selection

Validated through comprehensive simulations

Abstract

In this work, we propose a method to efficiently find the regularization parameter for low-rank MMSE filters based on a Kronecker-product representation. We show that the regularization parameter is surprisingly linked to the problem of rank selection and, thus, properly choosing it, is crucial for low-rank settings. The proposed method is validated through simulations, showing significant gains over commonly used methods.