Exploring CNN-Based Automatic Modulation Classification Using Small Modulation Sets

Abstract

We investigate the effect of a reduced modulation scheme pool on a CNN-based automatic modulation classifier. Similar classifiers in literature are typically used to classify sets of five or more different modulation types [1] [2], whereas our analysis is of a CNN classifier that classifies between two modulation types, 16-QAM and 8-PSK, only. While implementing the network, we observe that the network’s classification accuracy improves for lower SNR instead of reducing as expected. This analysis exposes characteristics of such classifiers that can be used to improve CNN classifiers on larger sets of modulation types. We show that presenting the SNR data as an extra data point to the network can significantly increase classification accuracy