NSLMS: a Proportional Weight Algorithm for Sparse Adaptive Filters
R.K. Martin and C.R. Johnson, Jr.
Abstract
In this paper we discuss a proportional weight algorithm that is similar
to LMS. The distinction is that the new algorithm (called normalized
sparse LMS, or NSLMS) has a time-varying vector stepsize, whose
coefficients are proportional to the magnitudes of the current values of
the tap estimates.
We show that when the system to be identified is sparse, NSLMS converges
faster than LMS (to the same asymptotic MMSE for both algorithms). We
also discuss the effect of the initialization on the performance of NSLMS.
