Classification of Spectrally Efficient Constant Envelope Modulations Based on Radial Basis Function Network and Deep Learning

摘要

Despite its significance, modulation classification of constant envelope modulations (CEM) has not gained worthy attention in AMC literature so far. Two neural network-based architectures, i.e., radial basis function network (RBFN) and sparse-autoencoder-based deep neural network (DNN) are proposed and analyzed for the classification of spectrally efficient CEM modulations. A blind classification method which does not require any a-priori information about the channel or CEM specifics is based on the effectiveness of proposed hybrid feature space (HFS), used to train the trending neural network classifiers. Classification performance of both networks is analyzed for the typical additive white Gaussian noise (AWGN) channel and less explored, unfriendly, frequency-selective fading environment under the impact of Doppler shift.