Channel equalization based on two weights neural network

Authors:
Wenming Cao;Wanfang Chai;Shoujue Wang
Affiliations:
Collage of Information engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China;Collage of Information engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China;Collage of Information engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Year:
2005

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Abstract

In this paper, we discuss the application of a two weights neural network (TWNN) to the channel equalization problem. In particular, the purpose of the paper is to improve the previously developed TWNN equalizer with training using K-means and LMS methods; reducing the TWNN network size by considering a lesser number of TWNN kernels, and developing new techniques for determining channel order which is required to specify the structure of an TWNN equalizer. A linear regression model was used for estimating the channel order. The basic idea of reducing the network size is to select the centers, based on the channel lag. This work includes the comparison of the limits of mean square error (MSE) convergence of both a linear equalizer and an TWNN equalizer. mean square error.