Title :
Convergence analysis of threshold-based sparse NLMS algorithm
Author :
Al-Hassan, Abdurahman ; Sulyman, Ahmed Iyanda ; Alsanie, Abdulhameed
Author_Institution :
Electr. Eng. Dept., King Saud Univ., Riyadh, Saudi Arabia
Abstract :
In this paper we present a threshold-based sparse NLMS algorithm. The proposed algorithm uses an energy threshold criterion to detect tap sparseness and update the active coefficients accordingly. The propsed algorithm is simple to implement, and our simulation results shows that it has better estimation performance in terms of convergence speed and MSE than the standard NLMS algorithm.
Keywords :
channel estimation; convergence; estimation theory; least mean squares methods; adaptive filtering; convergence speed estimation analysis; energy threshold criterion; least mean square algorithm; sparse channel estimation; tap sparseness detection; threshold-based sparse NLMS algorithm; Algorithm design and analysis; Channel estimation; Convergence; Estimation; Indexes; Signal processing algorithms; Standards; Adaptive filter; Convergence analysis; NLMS Algorithm; Sparse Channel estimation;
Conference_Titel :
GCC Conference and Exhibition (GCC), 2013 7th IEEE
Conference_Location :
Doha
Print_ISBN :
978-1-4799-0722-9
DOI :
10.1109/IEEEGCC.2013.6705780
