Date of Award

Spring 5-2022

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical and Computer Engineering

Committee Director

Dimitrie C. Popescu

Committee Member

Jiang Li

Committee Member

Otilia Popescu

Abstract

Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are trained to recognize common types of PSK and QAM digital modulation schemes, and their classification performance is tested on two distinct datasets that are publicly available. Results show that capsule networks outperform convolutional neural networks and residual networks, which have been used previously to classify signals in the same datasets, and indicate that they are a meaningful alternative for machine learning approaches to digitally modulated signal classification. The thesis includes also a discussion of practical implementations of the proposed capsule networks in an FPGA-powered embedded system.

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DOI

10.25777/m0eq-fy83

ISBN

9798834003717

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