Blind Modulation Classification based on MLP and PNN
This work addresses the need for efficient modulation classification in communication systems, but it is incremental as it combines existing methods like CWT, PCA, and neural networks without introducing a fundamentally new approach.
The paper tackles the problem of blind automatic classification of digitally modulated communication signals by proposing a system that uses continuous wavelet transform and principal component analysis for feature extraction, and compares probabilistic neural networks (PNN) and multilayer perceptron forward neural networks (MLPFN) for classification, with PNN achieving better accuracy and faster training/testing times while being robust to phase offset and Gaussian noise.
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.