Document Type

Article

Publication Date

2023

DOI

10.3390/s23125735

Publication Title

Sensors

Volume

23

Issue

12

Pages

5735 (1-20)

Abstract

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper outperformed alternative approaches for classifying digitally modulated signals that included conventional classifiers that employed CSP-based techniques, as well as alternative DL-based classifiers that used convolutional neural networks (CNNs) or residual networks (RESNETs) with the in-phase/quadrature (I/Q) data used for training and classification.

Rights

© 2023 by the Authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Data Availability

Article states: The datasets used to obtain the numerical results presented in this paper are openly available from the IEEE DataPort and the CSP Blog [34]

Original Publication Citation

Snoap, J. A., Popescu, D. C., Latshaw, J. A., & Spooner , C. M. (2023). Deep-learning-based classification of digitally modulated signals using capsule networks and cyclic cumulants. Sensors, 23(12), 1-20, Article 5735. https://doi.org/10.3390/s23125735

ORCID

0000-0001-8541-9764 (Snoap), 0000-0002-2102-8139 (Popescu), 0009-0001-6854-8932 (Latshaw)

Download

Share

COinS