Gated Transformer-Based Architecture for Automatic Modulation Classification

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dc.contributor.authorSahu, Antoripen
dc.contributor.committeechairJones, Creed F. IIIen
dc.contributor.committeechairDietrich, Carl B.en
dc.contributor.committeememberGiles, Kendall Everetten
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2024-02-07T01:38:23Zen
dc.date.available2024-02-07T01:38:23Zen
dc.date.issued2024-02-05en
dc.description.abstractThis thesis delves into the advancement of 5G portable test-nodes in wireless communication systems with cognitive radio capabilities, specifically addressing the critical need for dynamic spectrum sensing and awareness at the radio receiver through AI-driven automatic modulation classification. Our methodology is centered around the transformer encoder architecture incorporating a multi-head self-attention mechanism. We train our architecture extensively across a diverse range of signal-to-noise ratios (SNRs) from the RadioML 2018.01A dataset. We introduce a novel transformer-based architecture with a gated mechanism, designed as a runtime re-configurable automatic modulation classification framework, which demonstrates enhanced performance with low SNR RF signals during evaluation, an area where conventional methods have shown limitations, as corroborated by existing research. Our innovative single-model framework employs distinct weight sets, activated by varying SNR levels, to enable a gating mechanism for more accurate modulation classification. This advancement in automatic modulation classification marks a crucial step toward the evolution of smarter communication systems.en
dc.description.abstractgeneralThis thesis delves into the advancement of wireless communication systems, particularly in developing portable devices capable of effectively detecting and analyzing radio signals with cognitive radio capabilities. Central to our research is leveraging artificial intelligence (AI) for automatic modulation classification, a method to identify signal modulation types. We utilize a transformer-based AI model trained on the RadioML 2018.01A dataset. Our training approach is particularly effective when evaluating low-quality signals using a gating mechanism based on signal-to-noise ratios, an area previously considered challenging in existing research. This work marks a significant advancement in creating more intelligent and responsive wireless communication systems.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:39391en
dc.identifier.urihttps://hdl.handle.net/10919/117878en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectartificial intelligenceen
dc.subjectdeep learningen
dc.subjectneural networksen
dc.subjecttransformeren
dc.subjectautomatic modulation classificationen
dc.titleGated Transformer-Based Architecture for Automatic Modulation Classificationen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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