Performance evaluation of the correntropy coefficient in automatic modulation classification

Automatic modulation classification (AMC) techniques have applications in a variety of wireless communication scenarios, such as adaptive systems, cognitive radio, and surveillance systems. However, a common requirement to most of the AMC techniques proposed in the literature is the use of signal preprocessing modules, which can increase the computational cost and decrease the scalability of the AMC strategy. This work proposes the direct use of a similarity measure based on information theory for the automatic recognition of digital modulations, which is known as correntropy coefficient. The performance of correntropy in AMC applied to channels subject to additive white Gaussian noise (AWGN) is evaluated. Specifically, the influence of the kernel size on the classifier performance is analyzed, since it is the only free parameter in correntropy. Besides, a relationship between its respective value and the signal-to-noise ratio (SNR) of the channel is also proposed. Considering the investigated modulation techniques, numerical results obtained by simulation demonstrate that there are high accuracy rates in classification, even at low SNR values. By using correntropy, AMC task becomes simpler and more efficient.