Rigorous Moment-Based Automatic Modulation Classification

  • Darek T Kawamoto Hume Center, Virginia Tech
  • Robert W. McGwier Hume Center, Virginia Tech

Abstract

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.

Published
Sep 6, 2016
How to Cite
KAWAMOTO, Darek T; MCGWIER, Robert W.. Rigorous Moment-Based Automatic Modulation Classification. Proceedings of the GNU Radio Conference, [S.l.], v. 1, n. 1, sep. 2016. Available at: <http://pubs.gnuradio.org/index.php/grcon/article/view/7>. Date accessed: 26 july 2017.