Automatic Digital Signal Types Recognition Using SI-NN and HOS

Ebrahimzadeh, Ataollah; Ardebilipour, Mehrdad; Movahedian, Alireza

doi:10.1109/icc.2007.456

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“…al. used the radial basis function neural network as the classifier after extracting the features using a combination of higher orders of statistics (HOS) [10]. A combination of WT key features and Support Vector Machine (SVM) for classification is found in [5].…”

Section: Motivation and Previous Workmentioning

confidence: 99%

Exploring New Features for a Wavelet Neural Digital Modulation Recognition System-eng

Alkababji¹

2013

(AREJ)

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Modulation recognition has been an important problem in both commercial and military wireless communication. Modulation recognition can be divided into two categories: identification between categories and identification in category. In this work a system is proposed for identification between categories of different digital modulated signals using a combination of discrete wavelet transform (DWT) and the linear predictive coding (LPC) with the probabilistic neural network (PNN) as a classification tool. It was found that the proposed system out performed any of the existing systems by using six DWT decomposition levels and 20 LPC coefficients. The symlet 20 wavelet filter proved to be the best candidate. The results showed that a 100% recognition can be achieved at a signal to noise ratio (SNR) of 2db for the digitally modulated signal.

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Section: Motivation and Previous Workmentioning

confidence: 99%