Title :
Complexity reduction of the NLMS algorithm via selective coefficient update
Author :
Aboulnasr, T. ; Mayyas, K.
Author_Institution :
Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada
fDate :
5/1/1999 12:00:00 AM
Abstract :
This article proposes an algorithm for partial update of the coefficients of the normalized least mean square (NLMS) finite impulse response (FIR) adaptive filter. It is shown that while the proposed algorithm reduces the complexity of the adaptive filter, it maintains the closest performance to the full update NLMS filter for a given number of updates. Analysis of the MSE convergence and steady-state performance for independent and identically distributed (i.i.d.) signals is provided for the extreme case of one update/iteration
Keywords :
FIR filters; adaptive filters; adaptive signal processing; computational complexity; filtering theory; least mean squares methods; MSE convergence; NLMS FIR adaptive filter; NLMS algorithm; adaptive echo cancellation; complexity reduction; finite impulse response; i.i.d. signals; independent identically distributed signals; normalized least mean square; one update/iteration; partial coefficient update; selective coefficient update; steady-state performance; Acoustic applications; Adaptive filters; Convergence; Digital signal processing chips; Error correction; Finite impulse response filter; Performance analysis; Signal analysis; Signal processing algorithms; Steady-state;
Journal_Title :
Signal Processing, IEEE Transactions on
