Experimental Evaluation of AoA Estimation for UAV to Massive MIMO

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dc.contributor.committeeMemberKnightly, Edwarden_US
dc.contributor.committeeMemberSabharwal, Ashutoshen_US
dc.contributor.committeeMemberChi, Taiyunen_US
dc.creatorRice, Tarenceen_US
dc.date.accessioned2023-06-13T15:53:34Zen_US
dc.date.created2023-05en_US
dc.date.issued2023-04-19en_US
dc.date.submittedMay 2023en_US
dc.date.updated2023-06-13T15:53:34Zen_US
dc.description.abstractMassive MIMO (multiple-input, multiple-output) base stations are widely used for wireless networks to deploy multiple antennas, increasing their quality, throughput, and radio link capacity. Unmanned aerial vehicles (UAV) are prevalent due to their low cost and ease of use. Unmanned aerial vehicles (UAV) are prevalent due to their low cost and ease of use, allowing for multiple use cases that provide telemetry information to civilian, commercial, and military applications. In particular, we implement a suite of Angle of Arrival (AoA) estimation algorithms exploring their performance for UAV communication networks. From the evaluation of the five AoA estimations, Beamscan offers a spatial, spectral response that enables us to analyze both secondary propagation paths and the most likely AoA, providing us with a complete picture of the environment. We discovered with convergence time that when under-sampled, the AoA estimator detects the multi-path with a higher normalized power, impacting the AoA estimate result. We estimated azimuth AoA via horizontal subarrays and its effects on the multi-path AoA estimates for hovering drones. We discovered the effects of Rice football stadium seats as we decreased the number of antennas. We find that when evaluating hovering drones' azimuth and elevation AoA estimation, elevation estimation yields a median error of 13.8\degree higher error than azimuth for the 5x5 antenna scenario. Evaluated was the performance of the 2-D Beamscan spatial spectrum estimator. It provides higher accuracy between the two different channels of azimuth and elevation. This work will inform system designers on specifications from AoA estimations when designing a Massive MIMO to drone network.en_US
dc.embargo.lift2024-05-01en_US
dc.embargo.terms2024-05-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationRice, Tarence. "Experimental Evaluation of AoA Estimation for UAV to Massive MIMO." (2023) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/114906">https://hdl.handle.net/1911/114906</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/114906en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectDroneen_US
dc.subjectUnmanned Air Vehiclesen_US
dc.subjectAngle of Arrivalen_US
dc.subjectLocalizationen_US
dc.subjectMassive MIMOen_US
dc.titleExperimental Evaluation of AoA Estimation for UAV to Massive MIMOen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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