Adaptive Channel Equalizer using Combination of FIR and Functional Link Artificial Neural Network for Complex Signals
نویسندگان
چکیده
− This paper proposes an adaptive nonlinear channel equalizer by using combination of finite impulse response (FIR) filter and functional link artificial neural (FLANN) network (CFFLANN) capable of equalizing complex multilevel signals. The equalizer is designed to remove linear and nonlinear distortion produced by nonlinear channel. FLANN section removes the nonlinear distortions and FIR section removes linear distortions. Equalizer uses modified least mean square (MLMS) algorithm to adapt its tap weights. This system has less complex structure and high convergence speed. Performance of the equalizer is evaluated by two parameters-mean square error (MSE) and bit error rate (BER). Keywords− Adaptive equalizer, finite impulse response (FIR) filter, functional link artificial neural network (FLANN), nonlinear channel.
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