Blind Modulation Classification based on MLP and PNN

TL;DR

This paper presents a blind modulation classification system using wavelet-based feature extraction and neural network classifiers, demonstrating superior accuracy and robustness to noise and phase offsets.

Contribution

It introduces a combined feature extraction and neural network classification approach, comparing PNN and MLP, with PNN showing better performance.

Findings

PNN outperforms MLP in accuracy and speed

The system is robust to phase offset and Gaussian noise

Wavelet and PCA effectively extract discriminative features

Abstract

In this work, a pattern recognition system is investigated for blind automatic classification of digitally modulated communication signals. The proposed technique is able to discriminate the type of modulation scheme which is eventually used for demodulation followed by information extraction. The proposed system is composed of two subsystems namely feature extraction sub-system (FESS) and classifier sub-system (CSS). The FESS consists of continuous wavelet transform (CWT) for feature generation and principal component analysis (PCA) for selection of the feature subset which is rich in discriminatory information. The CSS uses the selected features to accurately classify the modulation class of the received signal. The proposed technique uses probabilistic neural network (PNN) and multilayer perceptron forward neural network (MLPFN) for comparative study of their recognition ability. PNN have been found to perform better in terms of classification accuracy as well as testing and training time than MLPFN. The proposed approach is robust to presence of phase offset and additive Gaussian noise.