Paper
IPM / Cognitive Sciences / 7858 
School of Cognitive Sciences

Title: 
Adaptive Channel Equalization using Fasteuclidean Direction Search Algorithm

Author(s): 
1 . 
M. Shams Esfand Abadi
 2 . 
A. Mahlooji Far
 3 . 
E. Kabir
 4 . 
R. Ebrahimpour


Status: 
In Proceedings

Proceeding: 
2nd IEEEGCC Conference

Year: 
2004

Supported by: 
IPM


Abstract: 
Least mean square (LMS) adaptive filters have been used in a wide range of signal processing applications because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the ?ultimate? adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Recently adaptive filtering are presented that are based on the Euclidean Direction Search (EDS) method of optimization. The fast version of this class is called the FastEDS or FEDS algorithm. The FEDS based algorithms have a fast convergence rate and computational complexity. This algorithm is investigated for adaptive channel equalization. The FEDS algorithm is shown to perform very well in attenuating noise and intersymbol interference
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