Title :
Convergence evaluation of a random step-size NLMS adaptive algorithm in system identification
Author :
Jimaa, S.A. ; Shimamura, T.
Author_Institution :
Commun. Eng. Dept., Khalifa Univ. of Sci., Sharjah, United Arab Emirates
Abstract :
A new and simple method to adjust the step-size (μ) of the standard Normalized Least Mean Square (NLMS) adaptive algorithm is proposed here. The value of μ is totally controlled by the use of a Pseudorandom Noise (PRN) uniform distribution that is defined by values from 0 to 1. Randomizing the step-size parameter eliminates much of the trade-off between residual error and convergence speed compared with the fixed step-size. The mean-square error (MSE) of using the new algorithm in the adaptation process of system identification over a defined communication channel is investigated here. The proposed uniformly distributed step-size variation in the adaptation process of the NLMS algorithm makes it possible to have similar convergence rate but lower steady state error compared with fixed μ.
Keywords :
adaptive filters; convergence; identification; least mean squares methods; random noise; telecommunication channels; PRN; adaptive filtering; communication channel; convergence evaluation; mean-square error method; normalized least mean square adaptive algorithm; pseudorandom noise; random step-size NLMS adaptive algorithm; residual error; system identification; Adaptation model; Adaptive filters; Convergence; Signal processing algorithms; Steady-state; System identification; Adaptive algorithms; NLMS; System Identification;
Conference_Titel :
Signal Processing (ICSP), 2010 IEEE 10th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-5897-4
DOI :
10.1109/ICOSP.2010.5657004
