Effect of training algorithms on performance of a developed automatic modulation classification using artificial neural network | IEEE Conference Publication | IEEE Xplore

Effect of training algorithms on performance of a developed automatic modulation classification using artificial neural network


Abstract:

As classification has become one of the active areas of applications for artificial neural networks, the major objective of this paper was to verify the impact of trainin...Show More

Abstract:

As classification has become one of the active areas of applications for artificial neural networks, the major objective of this paper was to verify the impact of training algorithms on classifiers developed using an artificial neural network. This paper presents an algorithm for classifying eight digitally modulated signals using a feature-based approach and a pattern recognition method. The developed automatic modulation classification was trained using two training algorithms often used for supervised neural networks. The performance of the developed automatic modulation classification classifier was evaluated and compared using the two training algorithms. The overall performance evaluation of the classifier using the two training algorithms shows that the developed classifier could successfully classify the eight modulated schemes considered with an average success rate above 97.0% irrespective of the signal-to-noise value. The results of the study also show that training algorithms have an impact on the performance of an artificial neural network classifier. In addition, the result of the comparative analysis carried out between the classifier reported in this paper and the one in surveyed literature shows that the signal recognition rate of this classifier is accurate and reliable.
Published in: 2013 Africon
Date of Conference: 09-12 September 2013
Date Added to IEEE Xplore: 06 March 2014
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ISSN Information:

Conference Location: Pointe aux Piments, Mauritius

I. Introduction

With the current advancement in information and communications technology (ICT), modulation schemes employ in wireless communication have become increasingly complex. One of the challenges this increase in modulation schemes' complexity has caused is how different modulation schemes can be reliably and accurately recognized. This has resulted in serious need for the development of automatic modulation classification (AMC), which can reliably and accurately classify different modulation schemes.

References

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