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dc.contributor.authorShaham, Sina
dc.contributor.authorDing, Ming
dc.contributor.authorKokshoorn, Matthew
dc.contributor.authorLin, Zihuai
dc.contributor.authorDang, Shuping
dc.contributor.authorAbbas, Rana
dc.date.accessioned2020-01-28T10:37:45Z
dc.date.available2020-01-28T10:37:45Z
dc.date.issued2019-09-28
dc.date.submitted2019-09-03
dc.identifier.citationShaham, S., Ding, M., Kokshoorn, M., Lin, Z., Dang, S., & Abbas, R. (2019). Fast Channel Estimation and Beam Tracking for Millimeter Wave Vehicular Communications. IEEE Access, 7, 141104–141118. doi:10.1109/access.2019.2944308
dc.identifier.doi10.1109/ACCESS.2019.2944308
dc.identifier.urihttp://hdl.handle.net/10754/661258
dc.description.abstractMillimeter wave (mmWave) has been claimed to be the only viable solution for high-bandwidth vehicular communications. However, frequent channel estimation and beamforming required to provide a satisfactory quality of service limits mmWave for vehicular communications. In this paper, we propose a novel channel estimation and beam tracking framework for mmWave communications in a vehicular network setting. For channel estimation, we propose an algorithm termed robust adaptive multi-feedback (RAF) that achieves comparable estimation performance as existing channel estimation algorithms, with a significantly smaller number of feedback bits. We derive upper and lower bounds on the probability of estimation error (PEE) of the RAF algorithm, given a number of channel estimations, whose accuracy is verified through Monte Carlo simulations. For beam tracking, we propose a new practical model for mmWave vehicular communications. In contrast to the prior works, the model is based on position, velocity, and channel coefficient, which allows a significant improvement of the tracking performance. Focused on the new beam tracking model, we re-derive the equations for Jacobian matrices, reducing the complexity for vehicular communications. An extensive number of simulations is conducted to show the superiority of our proposed channel estimation method and beam tracking algorithm in comparison with the existing algorithms and models. Our simulations suggest that the RAF algorithm can achieve the desired PEE, while on average, reducing the feedback overhead by 75.5% and the total channel estimation time by 14%. The beam tracking algorithm is also shown to significantly improve beam tracking performance, allowing more room for data transmission.
dc.description.sponsorshipThis work was supported by the Australian Research Council (ARC) Discovery under Project DP190101988.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8851151/
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleFast Channel Estimation and Beam Tracking for Millimeter Wave Vehicular Communications
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE Access
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Engineering, The University of Sydney, Sydney, NSW, Australia
dc.contributor.institutionData61, Sydney, NSW, Australia
dc.identifier.arxivid1806.00161
kaust.personDang, Shuping
dc.date.accepted2019-09-23
refterms.dateFOA2020-01-28T10:38:47Z
dc.date.published-online2019-09-28
dc.date.published-print2019


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(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Except where otherwise noted, this item's license is described as (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.