Equalization for a Wireless ATM Channel with a Recurrent Neural Network Pruned by Genetic Algorithm

Author

Park, Dong-Chul

Author_Institution

Dept. of Inf. Eng., Myong Ji Univ., Yongin

fYear

2008

fDate

6-8 Aug. 2008

Firstpage

670

Lastpage

674

Abstract

A new method for pruning the complex bilinear recurrent neural network(CBLRNN) is proposed in this paper. The pruned CBLRNN is applied to the equalization of signals for a wireless ATM network.The transmitted signal is assumed to be modulated by phase shift keying approach. The pruned CBLRNN based equalizer is compared with currently used decision feedback equalizer (DFE), Volterra filter based equalizer, and multilayer perceptron neural network equalizer. Experiments show that the pruned CBLRNN gives better results in terms of MSE and SER criteria over conventional equalizers.

Keywords

asynchronous transfer mode; equalisers; genetic algorithms; multilayer perceptrons; recurrent neural nets; wireless channels; Volterra filter based equalizer; asynchronous transfer mode; complex bilinear recurrent neural network; decision feedback equalizer; equalization; genetic algorithm; multilayer perceptron neural network equalizer; phase shift keying; wireless ATM channel; wireless ATM network; Asynchronous transfer mode; Computational complexity; Decision feedback equalizers; Genetic algorithms; Multilayer perceptrons; Neural networks; Nonlinear filters; Recurrent neural networks; Signal processing; Viterbi algorithm; 8PSK; BLRNN; Equalization; Genetic algorithm; Pruning;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008. SNPD '08. Ninth ACIS International Conference on

Conference_Location

Phuket

Print_ISBN

978-0-7695-3263-9

Type

conf

DOI

10.1109/SNPD.2008.111

Filename

4617450