Novel Automatic Modulation Classification using Correntropy Coefficient

This paper deals with automatic modulation classification (AMC) of communication signals. A new method for the automatic classification using a similarity measure derived from Information Theoretic Learning (ITL), called correntropy coefficient, is proposed. Unlike many of the conventional methods, the proposed method does not require any signal pre-processing. Further, the proposed AMC technique uses a simple scheme of evaluating the correntropy coefficients, calculated over templates containing the common features of digitally modulated signals, in the classification task. The performance of the classifier is presented in the form of classification hit-rates under AWGN noisy conditions, with Signal-to-Noise Ratios (SNRs) at the range of −5 dB to 15 dB. Simulation results with binary modulations show classification hit-rates of 83% for −5 dB and 99% for 0 dB. Keywords— Correntropy Coefficient, Automatic Modulation Classification, Radio Cognitivo.