On Deep Learning Classification of Digitally Modulated Signals Using Raw I/Q Data
Abstract
The paper considers the problem of deep-learning-based classification of digitally modulated signals using I/Q data and studies the generalization ability of a trained neural network (NN) to correctly classify digitally modulated signals it has been trained to recognize when the training and testing datasets are distinct. Specifically, we consider both a residual network (RN) and a convolutional neural network (CNN) and use them in conjunction with two different datasets that contain similar classes of digitally modulated signals but that have been generated independently using different means, with one dataset used for training and the other one for testing.
Keywords
Cite
@article{arxiv.2307.02450,
title = {On Deep Learning Classification of Digitally Modulated Signals Using Raw I/Q Data},
author = {John A. Snoap and Dimitrie C. Popescu and Chad M. Spooner},
journal= {arXiv preprint arXiv:2307.02450},
year = {2023}
}
Comments
Published in IEEE CCNC 2022: 2022 IEEE 19th Annual Consumer Communications & Networking Conference