Proportionate Affine Projection Algorithms for Block-sparse System Identification

TL;DR

This paper introduces a new family of block-sparse proportionate affine projection algorithms (BS-PAPA) that enhance system identification performance, offering faster convergence and better tracking for block-sparse systems.

Contribution

The paper proposes the BS-PAPA algorithm family, unifying several existing algorithms and providing efficient implementations for improved block-sparse system identification.

Findings

BS-PAPA outperforms existing algorithms in convergence speed.

BS-MPAPA reduces computational complexity.

Proposed algorithms demonstrate superior tracking ability.

Abstract

A new family of block-sparse proportionate affine projection algorithms (BS-PAPA) is proposed to improve the performance for block-sparse systems. This is motivated by the recent block-sparse proportionate normalized least mean square (BS-PNLMS) algorithm. It is demonstrated that the affine projection algorithm (APA), proportionate APA (PAPA), BS-PNLMS and PNLMS are all special cases of the proposed BS-PAPA algorithm. Meanwhile, an efficient implementation of the proposed BS-PAPA and block-sparse memory PAPA (BS-MPAPA) are also presented to reduce computational complexity. Simulation results demonstrate that the proposed BS-PAPA and BS-MPAPA algorithms outperform the APA, PAPA and MPAPA algorithms for block-sparse system identification in terms of both faster convergence speed and better tracking ability.