M. W. Aslam, Z. Zhu and A. K. Nandi, “Automatic Digital Modulation Classification Using Genetic Programming with K-Nearest Neighbor,” The 2010 Military Communications Conference—Unclassified Program Waveforms and Signal Processing Track.

has been cited by the following article:

• TITLE: Blind Modulation Recognition in Wireless MC-CDMA Systems Using a Support Vector Machine Classifier

  AUTHORS: Mohamed El-Hady Magdy Keshk Keshk, El-Sayed Elrabie, Fathi El-Sayed Abd El-Samie, Mohammed Abd El-Naby

  KEYWORDS: ADMR; MC-CDMA; MFCC; DT; SVM; ANN

  JOURNAL NAME: Wireless Engineering and Technology, Vol.4 No.3, July 10, 2013

  ABSTRACT: Automatic Digital Modulation Recognition (ADMR) is becoming an interesting problem with various civil and military applications. In this paper, an ADMR algorithm in Multi-Carrier Code Division Multiple Access (MC-CDMA) systems using Discrete Transforms (DTs) and Mel-Frequency Cepstral Coefficients (MFCCs) is proposed. This algorithm uses various DT techniques such as the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) with MFCCs to extract features from the modulated signal and a Support Vector Machine (SVM) to classify the modulation orders. The proposed algorithm avoids over fitting and local optimal problems that appear in Artificial Neural Networks (ANNs). Simulation results shows the classifier is capable of recognizing the modulation scheme with high accuracy up to 90% - 100% using DWT, DCT and DST for some modulation schemes over a wide Signal-to-Noise Ratio (SNR) range in the presence of Additive White Gaussian Noise (AWGN) and Rayleigh fading channel, particularly at a low Signal-to-Noise ratios (SNRs).