Automatic Modulation Classification with Deep Neural Networks

TL;DR

This paper evaluates various deep neural network architectures for automatic modulation classification, identifying key design elements that improve performance and establishing a new state-of-the-art method.

Contribution

It provides a comprehensive analysis of CNN architectures for modulation classification and introduces a novel combination of design elements that achieves superior accuracy.

Findings

Dilated convolutions, statistics pooling, and squeeze-and-excitation units enhance classification performance.

The proposed architecture outperforms existing methods across multiple metrics.

Analysis of misclassifications and performance on short signal bursts demonstrates robustness.

Abstract

Automatic modulation classification is a desired feature in many modern software-defined radios. In recent years, a number of convolutional deep learning architectures have been proposed for automatically classifying the modulation used on observed signal bursts. However, a comprehensive analysis of these differing architectures and importance of each design element has not been carried out. Thus it is unclear what tradeoffs the differing designs of these convolutional neural networks might have. In this research, we investigate numerous architectures for automatic modulation classification and perform a comprehensive ablation study to investigate the impacts of varying hyperparameters and design elements on automatic modulation classification performance. We show that a new state of the art in performance can be achieved using a subset of the studied design elements. In particular, we show that a combination of dilated convolutions, statistics pooling, and squeeze-and-excitation units results in the strongest performing classifier. We further investigate this best performer according to various other criteria, including short signal bursts, common misclassifications, and performance across differing modulation categories and modes.