Enhancement of New Channel Equalizer Using Adaptive Neuro Fuzzy Inference System

Abstract

The rapidly increasing need for information communication requires higher speed data transmission over the existing channels. The data rate over these channels is limited mainly by Inter Symbol Interference (ISI). Channel equalizers are used to reduce the effect of ISI. In this paper, a new equalizer based on Adaptive Neuro-Fuzzy Inference System is presented. The performance of the proposed equalizer is evaluated for both linear as well as non-linear channels in terms of bit-error rate for different noise powers. Simulation results show that the proposed equalizer has better Bit Error Rate (BER) performance compared to multi-layer perceptron and least mean square equalizers. However, its BER performance is slightly poorer than that of radial basis function network and optimal Bayesian equalizer but is better in terms of structural complexity. Keywords: Channel equalizer, Hybrid learning algorithm, Intersymbol interference, Membership function, optimal Bayesian equalizer. The problem of equalization is treated in two different ways. Firstly, it may be interpreted as an inverse filtering problem, so that the combination of the channel and equalizer must behave like ideal channel .The conventional equalizers such as Linear, Decision-feedback, and Fractionally- spaced equalizers are based on this approach. But their performance is poor especially when the channel response is non-linear. Secondly, the problem of equalization may be considered as a classification problem. All equalizers using neural networks are based on the latter approach.. The capabilities of neural networks for equalization of simple channels are described by Gibson, Siu and Cowan. Multi-layer perceptron (MLP) equalizers are superior to conventional linear and decision feedback equalizers in terms of probability of error, but their practical applications are severely restricted due to difficulties such as very long training time, indeterminate nature of training time, and lack of a methodology for architecture selection. Radial basis function network (RBFN) equalizer has received a great deal of attention because of its structural simplicity and more efficient learning compared to MLP. Moreover, there exists structural equivalence between RBFN equalizer and optimal symbol spaced Bayesian equalizer . However, the structure of RBFN equalizer depends on channel length, and thus requires a large number of centers in the hidden layer, which increases the computational complexity. In this paper, we propose a new channel equalizer based on adaptive neuro-fuzzy inference system, i.e., an adaptive fuzzy inference system whose parameters are adapted using neural network learning algorithms. Herein hybrid learning algorithm is used for optimizing parameters of ANFIS. The performance of the proposed equalizer is evaluated in terms of bit-error rate (BER) for different noise powers in the channel. Both linear as well as non-linear channels are considered for performance evaluation. The BER of proposed equalizer is compared with conventional least mean square (LMS) equalizer, MLP equalizer, RBFN equalizer, and optimal Bayesian equalizer. Simulation results show that proposed equalizer has better BER performance compared to MLP and LMS equalizers, but slightly poorer than RBFN and optimal Bayesian equalizers. However, the proposed equalizer has smaller structure than RBFN equalizer, thus provides a better compromise between the performance and structural complexity.