Intelligent Anti-Jamming Based on Deep-Reinforcement Learning and Transfer Learning

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dc.contributor.advisorWang, Ping
dc.contributor.authorBarqi Janiar, Siavash
dc.date.accessioned2023-08-04T15:15:00Z
dc.date.available2023-08-04T15:15:00Z
dc.date.issued2023-08-04
dc.date.updated2023-08-04T15:15:00Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractOne of the security issues in a wireless network is jamming attacks, where the jammer causes congestion and significant decrement in the network throughput by obstructing channels and disrupting user signals. In this thesis, we first develop a deep reinforcement learning (DRL) model to confront the jammer. However, training a DRL model from scratch may take a long time. We further propose a transfer learning (TL) approach to enable the DRL agent to learn fast in dynamic wireless networks to confront jamming attacks effectively. To make our proposed TL method adaptive to different network environments, we propose a novel method to quantitatively measure the difference between the source and target domains, using an integrated feature extractor. Afterward, based on the measured difference, we demonstrate how it can help choosing an efficient setting for the TL model leading to a fast and energy-efficient learning. We also show that the proposed TL method can effectively reduce the training time for the DRL model and outperforms other existing TL methods.
dc.identifier.urihttps://hdl.handle.net/10315/41354
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subjectElectrical engineering
dc.subjectComputer engineering
dc.subject.keywordsTransfer learning
dc.subject.keywordsReinforcement learning
dc.subject.keywordsWireless network security
dc.subject.keywordsExplainable artificial intelligence
dc.subject.keywordsExplainable reinforcement learning
dc.titleIntelligent Anti-Jamming Based on Deep-Reinforcement Learning and Transfer Learning
dc.typeElectronic Thesis or Dissertation

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