Novel Feature Selection Method Using Bhattacharyya Distance for Neural Networks Based Automatic Modulation Classification

In the context of Automatic Modulation Classification (AMC), some recent works have utilized multiple features to train the neural network. With an ultimate aim to develop a systematic approach to select the most diverse and unique features, we propose and demonstrate a novel method to select the most diverse ((m)(2)) features from a larger feature set. Bhattacharyya distance metric for the dissimilarity between two probability distributions is utilized to select the features with the highest distance for all modulation pairs within a test pool. The proposed approach is analyzed for three different neural networks based classifiers, amidst AWGN and frequency-selective fading channels. A substantial reduction in computational complexity is achieved with an acceptable compromise on the classification performance.