A Hybrid Genetic Algorithm with Simulated Annealing for Nonlinear Blind Equalization Using RBF Networks

• Published in: Adaptive and Natural Computing Algorithms pp. 257 - 265
• Main Authors: Han, Soowhan, Lee, Imgeun, Han, Changwook
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Decision Feedback Equalizer; Hybrid Genetic Algorithm; Normalize Root Mean Square Error; Transmitted Symbol; Volterra Kernel
• ISBN: 9783540715894; 3540715894
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-71618-1_29

In this study, a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, is derived for nonlinear channel blind equalization using RBF networks. The proposed hybrid genetic algorithm is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. From these estimated output states, the desired channel states of the nonlinear channel are derived and placed at the center of a RBF equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm (GA) and a simplex GA. It is shown that the relatively high accuracy and fast convergence speed have been achieved.