Spectrum Awareness: Deep Learning and Isolation Forest Approaches for Open-set Identification of Signals

dc.contributor.authorFredieu, Christianen
dc.contributor.committeechairBuehrer, R. Michaelen
dc.contributor.committeememberSaad, Waliden
dc.contributor.committeememberHeadley, W. Chrisen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2022-06-03T14:55:44Zen
dc.date.available2022-06-03T14:55:44Zen
dc.date.issued2022en
dc.description.abstractOver the next decade, 5G networks will become more and more prevalent in everyday life. This will provide solutions to current limitations by allowing access to bands previously unavailable to civilian communication networks. However, this also provides new challenges primarily for the military operations. Radar bands have traditionally operated primarily in the sub-6 GHz region. In the past, these bands were off limits to civilian communications. However, that changed when they were opened up in the 2010's. With these bands now being forced to co-exist with commercial users, military operators need systems to identify the signals within a spectrum environment. In this thesis, we extend current research in the area of signal identification by using previous work in the area to construct a deep learning-based classifier that is able to classify a signal as either as a communication waveform (Single-Carrier (SC), Single-Carrier Frequency Division Multiple Access (SC-FDMA), Orthogonal Frequency Division Multiplexing (OFDM), Amplitude Modulation (AM), Frequency Modulation (FM)) or a radar waveform (Linear Frequency Modulation (LFM) or Phase-coded). However, the downside to this method is that the classifier is based on the assumption that all possible signals within the spectrum environment are within the training dataset. To account for this, we have proposed a novel classifier design for detection of unknown signals outside of the training dataset. This two-classifier system forms an open-set recognition (OSR) system that is used to provide more situational awareness for operators.en
dc.description.abstractgeneralOver the next decade, next-generation communications will become prevalent in everyday life providing solutions to limitation previously experienced by older networks. However, this also brings about new challenges. Bands in the electromagnetic spectrum that were reserved for military use are now being opened up to commercial users. This means that military and civilian networks now have a challenge of co-existence that must be addressed. One way to address this is being aware of what signals are operating in the bands such as either communication signals, radar signals, or both. In this thesis, we will developed a system that can do that task of identifying a signal as one of five communication waveforms or two radar waveforms by using machine learning techniques. We also develop a new technique for identifying unknown signals that might be operating within these bands to further help military and civilian operators monitor the spectrum.en
dc.description.degreeM.S.en
dc.format.mediumETDen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/110419en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectAnomaly Detectionen
dc.subjectSignal Identificationen
dc.subjectIsolation Foresten
dc.subjectDeep Learningen
dc.titleSpectrum Awareness: Deep Learning and Isolation Forest Approaches for Open-set Identification of Signalsen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameM.S.en

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