Using Least Squares Support Vector Machines for Adaptive Communication Channel Equalization

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.