Near ML Modulation Classification

This paper deals with the problem of classification of digital modulation. In particular, we develop and propose a practical modulation classification scheme based on the likelihood of observations. While ML classification is well known and shows the optimal performance, its computational complexity prevents it from being easily implemented in hardware. On the contrary, our proposed scheme has low computational complexity and near optimal classification performance. Moreover, this scheme is designed to perform in fast fading channels. It is shown that our proposed classifier takes advantage of the channel variation without loosing near optimality.