Modulation Classification using Joint Time and Frequency-domain Data

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In this paper we propose a modulation classification scheme based on deep learning and convolutional neural networks. The proposed solution is based on I/Q amplitude samples that are classified into a set of 24 modulations, including analog and digital modulations. We investigate the adoption of different features in the training data. The features are computed using I/Q amplitude samples and include statistical information obtained in the time-domain and frequency-domain. Given the high amount of features considered in the training data, we investigate the possibility of projecting the features' data into a set of uncorrelated variables through the Principal Component Analysis procedure. We evaluate the classification performance for the different modulations and signal-to-noise ratio (SNR) values. Moreover, we also quantity the performance of the classifier when the features' data is projected into a subset of the uncorrelated variables. Finally, we compare the performance of the classifier with other works already available in the literature, assessing the effective performance gains of the proposed solution.

Original languageEnglish
Title of host publication2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728152073
DOIs
Publication statusPublished - May 2020
Event91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
Duration: 25 May 202028 May 2020

Publication series

NameIEEE Vehicular Technology Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2020-May
ISSN (Print)1550-2252

Conference

Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020
CountryBelgium
CityAntwerp
Period25/05/2028/05/20

Keywords

  • Automatic Modulation Recognition
  • Machine Learning
  • Neural Networks
  • Performance Analysis

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