Novel Automatic Modulation Classification using Correntropy Coefficient

Abstract

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.

Development and Comparative Study of Effects of Training Algorithms on Performance of Artificial Neural Network Based Analog and Digital Automatic Modulation Recognition

“…Automatic modulation recognition (AMR), also known as automatic modulation classification (AMC) as reported in [3] is defined as a tool used in recognizing the type of modulation scheme used to generate a received modulated signal without any a priori knowledge of the modulated signal itself. Similarly, it was also defined in [4] as an intermediate operation between the signal detection and data demodulation, which plays an important role in both military and civilian applications such as electronic warfare, surveillance, threat analysis, signal confirmation, signal interference identification and spectrum management.…”