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dc.contributor.authorHulede, Ian Ellis L.
dc.contributor.authorKwon, Hyuck M.
dc.date.accessioned2023-02-13T20:13:25Z
dc.date.available2023-02-13T20:13:25Z
dc.date.issued2022-12-02
dc.identifier.citationI. E. L. Hulede and H. M. Kwon, "Allan Deviation of Social Learning Distributed Network Time Synchronization," MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM), Rockville, MD, USA, 2022, pp. 235-240, doi: 10.1109/MILCOM55135.2022.10017979.
dc.identifier.issn2155-7586
dc.identifier.urihttps://doi.org/10.1109/MILCOM55135.2022.10017979
dc.identifier.urihttps://soar.wichita.edu/handle/10057/25025
dc.descriptionClick on the DOI to access this article (may not be free).
dc.description.abstractThe objective of this paper is to demonstrate that the concept of a recently published social learning distributed network time synchronization (SLDNTS) algorithm under a Global Positioning System (GPS) denial environment can be implementable and perform better than the existing consensus distributed network time synchronization (CDNTS). Practical clock parameters such as time offsets, frequency offsets, phase offsets, and observation noises are referred to the International Telecommunication Union (ITU) standard and considered in the evaluation. The Allan deviation criteria is used for the comparison of the proposed SLDNTS with the CDNTS.
dc.language.isoen_US
dc.publisherIEEE
dc.relation.ispartofseriesIEEE Military Communications Conference (MILCOM)
dc.relation.ispartofseries2022
dc.subjectGPS denial
dc.subjectDistributed network
dc.subjectTime synchronization
dc.subjectSocial learning
dc.subjectConsensus
dc.subjectHypothesis
dc.subjectAllan deviation
dc.titleAllan deviation of social learning distributed network time synchronization
dc.typeConference paper
dc.rights.holder© 2022 IEEE


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