عنوان البحث(Papers / Research Title)
Proposed Simulation of Modulation Identification Based On Wavelet Transform
الناشر \ المحرر \ الكاتب (Author / Editor / Publisher)
نداء عبد المحسن عباس العطوان
Citation Information
نداء,عبد,المحسن,عباس,العطوان ,Proposed Simulation of Modulation Identification Based On Wavelet Transform , Time 6/16/2011 4:46:29 PM : كلية تكنولوجيا المعلومات
وصف الابستركت (Abstract)
The Automatic identification of digitally modulated signal is considered as a rapidly evolving field
الوصف الكامل (Full Abstract)
Proposed Simulation of Modulation Identification Based On Wavelet Transform
Dr Sattar B. Sadkhan Babylon University mail; drengsattar@yahoo.com
Dr Nidaa A. Abbas, Members Babylon University mail: nidaa_muhsin@yahoo.com
Abstract :
The Automatic identification of digitally modulated signal is considered as a rapidly evolving field. It has found applications in many areas, including electronic warfare, surveillance and threat analysis. A variety of techniques have been proposed to identify digitally modulated signals such as; quadrature amplitude modulation(QAM) signal, phase shift keying(PSK) signal and frequency shift keying(FSK) signal. One of the important transformations is the Wavelet Transformation (WT). It is well different digitally modulated signals contain different transients in amplitude, frequency or phase. The performance of the identification scheme is investigated through simulations. When CNR is greater than 5 dB, the percentage of correct identification is about 97% with 50 observation symbols. The proposed method was tested via four different case studies.
Keywords : CNR, Identification, Modulation, wavelet transform
Introduction:
This paper provides the application of wavelet transform to identify a digitally modulated signal . This approach in signal classification based on using the wavelet transform to extract the transient characteristics in a digitally modulation signal, and apply the distinct pattern in wavelet transform domain for simple identification. The relevant statistics for optimum threshold selection are derived under the condition that the input noise is additive white Gaussian. Theperformance of the identification scheme is investigated through simulations. When CNR is greater than 5 dB, the percentage of correct identification is about 97% with 50 observation symbols.
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