Learnable Statistical Moments Pooling for Automatic Modulation Classification

Abstract

We introduce a differentiable statistical moment aggregation layer, enabling networks to learn the optimal method of statistical moment pooling for automatic modulation classification. Statistical pooling, a cornerstone of convolutional networks, consolidates activations into fixed-length representations. Traditionally, this entails mean, variance, and higher-ordered statistics pooling defined as fixed hyperparameters. By enabling the statistics layer to become differentiable, networks are able to optimize the method of statistical aggregations, transcending predefined hyperparameters. With our approach, the statistical moment order is differentiable. Our results demonstrate learned statistical moments are able to outperform fixed-moments—improving modulation classification performance of a time-domain signal. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>