Automatic Digital Modulation Identification Using an Intelligent Method
Author:
, , ,Year
: 2006
Abstract: Automatic identification of digital signal types is of interest for both civilian and military applications. Most of methods can only identify a few kinds of digital signal and usually need high levels of signal to noise ratio (SNR). This paper presents an efficient signal type identifier (ESTI) that includes a variety of digital signals. In this method, a combination of higher order moments (HOM) and higher order cumulants (HOC) are proposed as the features. A multi-layer perceptron neural network with resilient back propagation learning algorithm is proposed to determine the membership of received signal. We apply the genetic algorithm for feature selection in order to reduction of computational complexity. Simulation results show that the proposed method has high performance to identify the different types of digital signal even at low levels of SNR. This high performance is achieved with only seven features selected using genetic algorithm
Keyword(s): Automatic Digital Modulation Identification Using an Intelligent Method
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contributor author | عطاالله ابراهیم زاده شرمه | en |
contributor author | سیدعلی رضا سیدین | en |
contributor author | Ataollah Ebrahimzadeh Shermeh | fa |
contributor author | Seyed Alireza Seyedin | fa |
date accessioned | 2020-06-06T13:47:19Z | |
date available | 2020-06-06T13:47:19Z | |
date issued | 2006 | |
identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/3369050?locale-attribute=en | |
description abstract | Automatic identification of digital signal types is of interest for both civilian and military applications. Most of methods can only identify a few kinds of digital signal and usually need high levels of signal to noise ratio (SNR). This paper presents an efficient signal type identifier (ESTI) that includes a variety of digital signals. In this method, a combination of higher order moments (HOM) and higher order cumulants (HOC) are proposed as the features. A multi-layer perceptron neural network with resilient back propagation learning algorithm is proposed to determine the membership of received signal. We apply the genetic algorithm for feature selection in order to reduction of computational complexity. Simulation results show that the proposed method has high performance to identify the different types of digital signal even at low levels of SNR. This high performance is achieved with only seven features selected using genetic algorithm | en |
language | English | |
title | Automatic Digital Modulation Identification Using an Intelligent Method | en |
type | Journal Paper | |
contenttype | External Fulltext | |
subject keywords | Automatic Digital Modulation Identification Using an Intelligent Method | en |
journal title | WSEAS Transactions on Communications | fa |
pages | 1266-1273 | |
journal volume | 5 | |
journal issue | 7 | |
identifier link | https://profdoc.um.ac.ir/paper-abstract-168.html | |
identifier articleid | 168 |