Digital modulation recognition in noisy environment using a learning machine

Abstract EN

This paper proposes a method to identify the type of digitally modulated signals.

The modulation classification process is performed using Support Vector Machines (SVMs) with one versus all approach.

A multi-class recognition system is required.

Consequently, the Radial Basis Function (RBF) kernel is proposed.

The system is intended to classify three types of signals: ASK FSK, and PSK.

Five features are extracted from amplitude, frequency and phase of each modulated signal to be the input of the SVM classifier.

The system is simulated using MATLAB software.

The system is tested against Additive White Gaussian Noise (AWGN).

The classification rate for all modulated signals is measured at different values of SNR.

The overall performance of this classifier is around 83% at -5 dB.

Furthermore, to enhance the performance of the classifier further, the data inputs to the SVMs for each modulated signal is reduced by eliminating some key features.

These are the standard deviation of the direct value of the centered non-linear component of the instantaneous phase and the standard deviation of the absolute value of the normalized-centered of the instantaneous amplitude.

The overall performance after input data reduction is greater than 84% at -5 dB.