Abstract

Abstract—In this paper, a popular dynamic neural network structure called Cellular Neural Network (CNN) is employed as a channel equalizer in digital communications. It is shown that, this nonlinear system is capable of suppressing the effect of intersymbol interference (ISI) and the noise at the channel. The architecture is a small-scaled, simple neural network containing only 25 neurons (cells) with a neighborhood of r = 2 , thus including only 51 weight coefficients. Furthermore, a special technique called repetitive codes in equalization process is also applied to the mentioned CNN based system to show that the two-dimensional structure of CNN is capable of processing such signals, where performance improvement is observed. Simulations are carried out to compare the proposed structures with minimum mean square error (MMSE) and multilayer perceptron (MLP) based equalizers.

Highlights

  • IN DIGITAL communication systems, the signal at the receiver will be the linear combination of time delayed and original transmitted signals, as a result of reflections and diffractions at the media

  • The Cellular Neural Network (CNN) are trained and their BER performances are compared with the minimum mean square error (MMSE) and multilayer perceptron (MLP) equalizers for the two individual channels defined with hB and hC below with and without repetition codes [7]

  • As CNN’s output generates only +1 and -1 values, equalization is performed for Binary Phase Shift Keying (BPSK) signals

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Summary

INTRODUCTION

IN DIGITAL communication systems, the signal at the receiver will be the linear combination of time delayed and original transmitted signals, as a result of reflections and diffractions at the media. These are commonly used linear transversal filters in channel equalization, their Bit-Error-Rates (BER) are not satisfactory. For this reason, alternative methods were developed in literature including Neural Network based architectures [9], [10]. Alternative methods were developed in literature including Neural Network based architectures [9], [10] Even their BERs are better than the conventional techniques, because of their complex structures, they require too much computational power. The advantages and drawbacks of CNN Equalizers are discussed at the conclusion section

CHANNEL EQUALIZATION
CHANNEL EQUALIZATION WITH CNN
CNN Equalizer design with repetitive codes
CONCLUSIONS

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