Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization

Author

Abrar, Shafayat ; Zerguine, Azzedine ; Bettayeb, Maamar

Author_Institution

Dept. of Comput. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia

Volume

13

Issue

6

fYear

2002

fDate

11/1/2002 12:00:00 AM

Firstpage

1472

Lastpage

1481

Abstract

Stop-and-go decision-directed (S&G-DD) equalization is the most primitive blind equalization (BE) method for the cancelling of intersymbol-interference in data communication systems. Recently, this scheme has been applied to complex-valued multilayer feedforward neural network, giving robust results with a lower mean-square error at the expense of slow convergence. To overcome this problem, in this work, a fast converging recursive least squares (RLS)-based complex-valued backpropagation learning algorithm is derived for S&G-DD blind equalization. Simulation results show the effectiveness of the proposed algorithm in terms of initial convergence.

Keywords

backpropagation; blind equalisers; convergence of numerical methods; feedforward neural nets; least squares approximations; multilayer perceptrons; convergence; data communication systems; feedforward neural network; intersymbol-interference cancelling; learning; mean-square error; multilayer neural network; recursive least-squares backpropagation algorithm; simulation; stop-and-go decision-directed blind equalization; Backpropagation algorithms; Blind equalizers; Convergence; Data communication; Decision feedback equalizers; Feedforward neural networks; Least squares methods; Multi-layer neural network; Neural networks; Robustness;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2002.804282

Filename

1058081