%A Kawamoto, Darek T
%A McGwier, Robert W.
%D 2016
%T Rigorous Moment-Based Automatic Modulation Classification
%K
%X In this paper we develop the connection between the high-order moments, orthogonal polynomials, and probability densities representing signal constellations with AWGN in order to improve moment-based Automatic Modulation Classifi- cation (AMC). The result is that an approximate weighted L 2 distance between probability densities can be computed using a Euclidean distance on vectors consisting of series expansion coefficients. This analysis justifies the use of high-order moments in AMC. A discriminative Deep Neural Network (DNN) is trained to perform AMC, resulting in near-maximum likelihood performance at marginal SNR.
%U https://pubs.gnuradio.org/index.php/grcon/article/view/7
%J Proceedings of the GNU Radio Conference
%0 Journal Article
%V 1
%N 1
%8 2016-09-06