Title :
Reduced Complexity Self-Tuning Adaptive Algorithms in Application to Channel Estimation
Author :
Song, Seongwook ; Sung, Koeng-Mo
Author_Institution :
Samsung Electr. Co., Ltd.., Gyeonggi
Abstract :
In this letter, reduced complexity self-tuning algorithms are proposed using simplified parameter updating procedures. Convergence analysis based on the independence assumption and the ordinary differential equation (ODE) method shows that the tuning parameter of the proposed algorithm attains the same limit as the conventional self-tuning adaptive algorithm. Simulations are carried out for channel estimation to support the analysis and performance of the proposed algorithms.
Keywords :
channel estimation; convergence; differential equations; least mean squares methods; self-adjusting systems; channel estimation; convergence analysis; least-mean-squares algorithms; ordinary differential equation; recursive-least-square algorithms; reduced complexity self-tuning algorithms; Adaptive algorithm; Algorithm design and analysis; Approximation algorithms; Channel estimation; Convergence; Differential equations; Least squares approximation; Resonance light scattering; Signal processing algorithms; Tuning; Channel estimation; LMS; RLS;
Journal_Title :
Communications, IEEE Transactions on
DOI :
10.1109/TCOMM.2007.902493