Cheng-Jian Lina*, Shang-Jin Honga, and Chi-Yung Leeb

Department of Computer Science and Information Engineering, Chaoyang University of Technology, Wufong, Taichung County 41349, Taiwan, R.O.C.
b Department of Computer Science and Information Engineering, Nankai Institute of Technology, Caotun, Nantou County 542, Taiwan, R.O.C.

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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.

Keywords: digital communication; adaptive equalizer; support vector machines; time-varying channel; kernel function.

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Accepted: 2005-03-07
Available Online: 2005-04-03

Cite this article:

Lin, C.-J., Hong, S.-J., Lee, C.-Y., 2005. Using least squares support vector machines for adaptive communication channel equalization. International Journal of Applied Science and Engineering, 3, 51–59.