Reduced-Rank Adaptive Filtering Based on Joint Iterative Optimization of Adaptive Filters

TL;DR

This paper introduces a new reduced-rank adaptive filtering method using joint iterative optimization, which improves convergence and tracking performance while reducing computational complexity.

Contribution

It presents a novel joint iterative optimization scheme for adaptive filters, including MMSE-based design and low-complexity NLMS algorithms, outperforming existing reduced-rank methods.

Findings

Outperforms state-of-the-art reduced-rank schemes in convergence.

Achieves better tracking performance.

Operates at significantly lower complexity.

Abstract

This letter proposes a novel adaptive reduced-rank filtering scheme based on joint iterative optimization of adaptive filters. The novel scheme consists of a joint iterative optimization of a bank of full-rank adaptive filters that forms the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe minimum mean-squared error (MMSE) expressions for the design of the projection matrix and the reduced-rank filter and low-complexity normalized least-mean squares (NLMS) adaptive algorithms for its efficient implementation. Simulations for an interference suppression application show that the proposed scheme outperforms in convergence and tracking the state-ofthe- art reduced-rank schemes at significantly lower complexity.