Performance benefits of resource-constrained adaptive filtering

In this paper we study the convergence performance of partial-update adaptive filters in a systems identification context, which has several applications in signal processing and communications. We show that a certain class of data-dependent partial-update adaptive filter algorithms can achieve improved convergence performance with respect to their full-update counterparts for highly correlated input signals. The findings of the paper are illustrated with simulation examples.