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

(2002) Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization. Neural Networks, IEEE Transactions on, 13.

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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.

Item Type: Article
Subjects: Computer
Divisions: College Of Engineering Sciences > Electrical Engineering Dept
Depositing User: Mr. Admin Admin
Date Deposited: 24 Jun 2008 16:23
Last Modified: 01 Nov 2019 17:04
URI: http://eprints.kfupm.edu.sa/id/eprint/14125