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dc.contributor.authorGhannadrezaii, Hossein
dc.date.accessioned2022-10-24T12:45:38Z
dc.date.available2022-10-24T12:45:38Z
dc.date.issued2022-10-19
dc.identifier.urihttp://hdl.handle.net/10222/82046
dc.descriptionIn this dissertation, the correlation between different oceanic processes and the acoustic channel characteristics is investigated to define a set of tide-dependent states corresponding to a particular channel condition. To analyze the impact of flow and surface elevation variations, channel soundings from a 34-day sea trial conducted in Grand Passage, Nova Scotia, are applied to a parametric model of the propagation channel. The probabilistic parametric model forms a data set by characterizing the time varying channel impulse response and by describing the channel tapped-delay structure statistically as a function of the tide phase. The proposed Markov chain is driven by the measured channel data set and predicts the future channel characteristics one tide cycle ahead.en_US
dc.description.abstractThis dissertation investigates the design aspects of an adaptive cross-layer architecture to optimize the energy efficiency, the spectral efficiency, and the reliability of underwater acoustic multihop relaying networks by utilizing channel state information (CSI). Specifically, an energy efficient channel-aware routing protocol for reliably relaying data packets, as well as a media access control to maximize the network throughput and maintain connectivity are described. These tasks are approached by predicting CSI using a novel data-driven probabilistic model. As the main contribution, a CSI acquisition approach based on a Markov chain process is proposed that exploits information from the physical environmental conditions, including the tide phase and flow, to improve the accuracy of channel characteristics predictions. Specifically, the method is intended to obtain the channel characteristics, including the gain, delay, Doppler spread, as well as the standard deviation of intrapaths delays in time varying conditions.en_US
dc.language.isoenen_US
dc.subjectUnderwater Acoustic Networkingen_US
dc.subjectChannel State Informationen_US
dc.subjectChannel Characteristics Predictionen_US
dc.titleCHANNEL STATE INFORMATION ACQUISITION FOR ADAPTIVE UNDERWATER ACOUSTIC NETWORKINGen_US
dc.date.defence2022-09-22
dc.contributor.departmentDepartment of Electrical & Computer Engineeringen_US
dc.contributor.degreeDoctor of Philosophyen_US
dc.contributor.external-examinerDr. Cheng Lien_US
dc.contributor.graduate-coordinatorDr. Vincent Siebenen_US
dc.contributor.thesis-readerDr. Jacek Ilowen_US
dc.contributor.thesis-readerDr. David Barclayen_US
dc.contributor.thesis-supervisorDr. Jean-Francois Bousqueten_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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