C24. Automatic modulation recognition in wireless systems using cepstral analysis and neural networks

Author

Al-Makhlasawy, Rasha M. ; Elnaby, Mustafa M Abd ; El-Khobby, Heba A.

Author_Institution

Fac. of Eng., Tanta Univ., Tanta, Egypt

fYear

2012

fDate

10-12 April 2012

Firstpage

353

Lastpage

362

Abstract

Modulation type is one of the most important characteristics used in signal waveform identification for wireless communications. In this paper, a cepstral algorithm for Automatic Digital Modulation Recognition (ADMR) is proposed. This algorithm uses Mel-Frequency Cepstral Coefficients (MFCCs) to extract the features of the modulated signal and a multi-layer feed-forward Artificial Neural Network (ANN) to classify the modulation type and its order. The proposed algorithm is capable of recognizing the modulation scheme with high accuracy in the presence of Additive White Gaussian Noise (AWGN) over a wide Signal-to-Noise Ratio (SNR) range.

Keywords

AWGN; cepstral analysis; feature extraction; modulation; multilayer perceptrons; radiocommunication; telecommunication computing; ADMR; ANN; AWGN; MFCC; SNR range; additive white Gaussian noise; automatic digital modulation recognition; cepstral algorithm; cepstral analysis; feature extraction; mel-frequency cepstral coefficient; modulated signal; modulation scheme; modulation type; multilayer feed-forward artificial neural network; signal waveform identification; wide signal-to-noise ratio; wireless communication; wireless system; Discrete cosine transforms; Discrete wavelet transforms; Feature extraction; Modulation; Training; ADMR; ANNs; MFCCs;

fLanguage

English

Publisher

ieee

Conference_Titel

Radio Science Conference (NRSC), 2012 29th National

Conference_Location

Cairo

Print_ISBN

978-1-4673-1884-6

Type

conf

DOI

10.1109/NRSC.2012.6208542

Filename

6208542