"Source detection and automatic modulation classification for modern an" by Jayakrishnan Vijayamohanan
 

Electrical and Computer Engineering ETDs

Publication Date

Fall 12-15-2024

Abstract

Source detection and automatic modulation classification are two of the most important steps in any array processing task. In this research a novel deep learning model referred to as RadioNet, is proposed that is focused on solving both these problems by reformulating them as a multi-label classification problem. Traditional approaches to both these topics face challenges in scenarios with noise, interference, fewer number of snapshots, and high number of sources. The limitations of the conventional models are investigated and overcome by the proposed solution. RadioNet is also compared with other existing state-of-the-art machine learning based solutions. The introduced model is also implemented and validated using over-the-air measurements. Finally, the proposed framework is shown to perform both source detection and modulation classification at the same time for a limited number of sources and modulation schemes. Thus, this way the effectiveness of the proposed methodologies is evaluated and demonstrated.

Keywords

antenna array processing, source detection, modulation classification, deep learning

Document Type

Dissertation

Language

English

Degree Name

Electrical Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Christos Christodoulou

Second Committee Member

Mark Gilmore

Third Committee Member

Jehanzeb Chaudhry

Third Advisor

Arjun Gupta

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