A Class of Diffusion Algorithms with Logarithmic Cost over Adaptive Sparse Volterra Network

TL;DR

This paper introduces a new class of diffusion algorithms leveraging logarithmic cost and l0-norm constraints to efficiently estimate sparse Volterra networks, demonstrating superior performance in various scenarios.

Contribution

It proposes a novel diffusion algorithm class for sparse Volterra networks based on logarithmic cost and l0-norm, improving estimation efficiency.

Findings

Superior performance in Gaussian scenarios

Effective in impulsive noise environments

Outperforms existing algorithms

Abstract

In this Letter, we present a novel class of diffusion algorithms that can be used to estimate the coefficients of sparse Volterra network (SVN). The development of the algorithms is based on the logarithmic cost and l0-norm constraint. Simulations for Gaussian and impulsive scenarios are conducted to demonstrate the superior performance of the proposed algorithms as compared with the existing algorithms.