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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2011 Vol.1(3): 305-310 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.45

Universal Neural Network Demodulator for Software Defined Radio

M. Amini and E. Balarastaghi

Abstract—In this paper a universal demodulator based on probabilistic neural network is presented. it is a kind of modulation (schemes) free demodulator, i.e. it can be trained for different schemes of digital modulation in order to detect incoming data bits without changing or replacing receiver hardware so it can be easily used in software defined radio structure and military communication. Furthermore it has some advantages over the other types of neural network demodulators such as fast training and fast data processing ability to detect data bits since there is no feedback layer in its structure. There is also no need to design special kind of filters except for those which are used to limit the input noise power.

Index Terms—Probabilistic Neural Network, Demodulator, Simulation, Matlab, Software Radio.

MM. Amini and E. Balarastaghi, Academic member, Islamic Azad University,Boroujerd Branch, Iran
1 Distributed Time Delay Neural Network
2 Artificial Neural Network

[PDF]

Cite: M. Amini and E. Balarastaghi, "Universal Neural Network Demodulator for Software Defined Radio," International Journal of Machine Learning and Computing vol. 1, no. 3, pp. 305-310, 2011.

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