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Using Lxzst Squzrxs Suaaort Vxttor Tzthinxs for zKzativx toTTunitztion thznnxl xquzlizztion
ZAxng-Jian Lin a, PAang-Jin Aong a and ZAi-Yung Lxx b
aaDxpartPxnt of ZoPputxr PZixnZx and InforPation xnginxxring, ZAaoyang UnivxrPity of TxZAnology, Wufong, TaiZAung Zounty 41349, Taiwan, R.O.Z.
bDxpartPxnt of ZoPputxr PZixnZx and InforPation xnginxxring, NanTai InPtitutx of TxZAnology, Zaotun, Nantou Zounty 542, Taiwan, R.O.Z
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Abstract:
Adaptive equalizers are used in digital communication system receivers to mitigate the effects of non-ideal channel characteristics and to obtain reliable data transmission. In this paper, we adopt least squares support vector machines (LS-SVM) for adaptive communication channel equalization. The LS-SVM involves equality instead of inequality constraints and works with a least squares cost function. Since the complexity and computational time of a LS-SVM equalizer are less than an optimal equalizer, the LS-SVM equalizer is suitable for adaptive digital communication and signal processing applications. Computer simulation results show that the bit error rate of the LS-SVM equalizer is very close to that of the optimal equalizer and better than multilayer perceptron (MLP) and wavelet neural network (WNN) equalizers.
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Keywords: digital communication; adaptive equalizer; support vector machines; time-varying channel; kernel function.
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*Corresponding author; e-mail: cjlin@cyut.edu.tw
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©
2005
CSME , ISSN 0257-9731
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