Neuro-Fuzzy Network for Adaptive Channel Equalization Rahib H.Abiyev 1 , Tayseer Al-shanableh 2 1 Near East University, Department of Computer Engineering, P.O. Box 670, Le- fkosa, TRNC, Mersin-10, Turkey rahib@neu.edu.tr 2 Near East University, Department of Electrical and Electronic Engineering, P.O. Box 670, Lefkosa, TRNC, Mersin-10, Turkey shanableh@neu.edu.tr Abstract This paper presents the equalization of channel distortion by using neuro-fuzzy network. The structure and learning algorithm of neuro-fuzzy network have been described. Using learning algorithm of neuro-fuzzy network an adaptive equalizer have been developed. The developed equalizer recovers transmitted signal efficiently. The use of neuro-fuzzy equalizer in digital signal transmission allows to decrease training time of parameters and the com- plexity of network. The result obtained from the simulation is compared with the simulation result of neural equalizer, and it is shown that neuro-fuzzy equalizer has better performance than other one. 1. Introduction In today’s communication environment, the channels are affected by both linear and nonlinear distortion. To equalize channel distortions, such as intersymbol interferences, chan- nel noises the various equalizers have been applied [1]. Classical equalizers do not perform well in rapidly fading channels. When channel has time-varying characteristics and channel model is not precisely known adaptive equalization is applied. Adaptive equalization can be divided into two types: sequence estimation and symbol detection [2]. Sequence estimation needs channel estimation, and is computationally complex. In this paper, adaptive channel equalization that realizes symbol detection technique is considered. This is a classification problem in which input baseband signal is mapped onto a feature space determined by the direct interpretation of known training sequence. Here, the aim is to separate the symbols in the output signal space whose optimal decision regions boundaries are nonlinear. Recently, different approaches have been proposed for channel equalization. Classical ap- proaches for adaptive equalizer design are based on knowledge of the parametric channel model [3]. These are implemented by identifying the dynamic of channel and then construct- ing an equalizer using the identified channel model. These processes require certain time to gather statistical data about the channel and are time consuming. Another type of adaptive equalizer is decision feedback equalizer that can be used to improve the performance of equalizer. Next approach to equalizer design is based on increasing the number of equalizer taps and choosing the coefficients from different ranges of values according to the amplitude of distorted signals [4]. In this approach a large number of coefficients and switching thresh- olds are required. Nowadays neural networks are widely used for channel equalization [5-11]. One class of nonlinear adaptive equalizer is based on multiplayer perceptions (MLP) and radial basis func- tions (RBF) [3-5,7,9,10]. Different MLP structures have been introduced for channel equali- zation [8,9]. The MLP equalizers require long training and are sensitive to the initial choice of Proceedings of the Fifth Mexican International Conference on Artificial Intelligence (MICAI'06) 0-7695-2722-1/06 $20.00 © 2006