The Application of Lognormal Mixture Shadowing Model for B2B Channels

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dc.creatorCheffena, Michael
dc.creatorMohamed, Marshed
dc.date2020-01-08T12:50:27Z
dc.date2020-01-08T12:50:27Z
dc.date2018-09
dc.date.accessioned2021-05-03T13:17:01Z
dc.date.available2021-05-03T13:17:01Z
dc.descriptionIn this article, a Lognormal mixture shadowing model based on a cluster concept is utilized in the modeling of body-to-body (B2B) channels for different running and cycling activities. The mixture model addresses the inaccuracies observed using a unimodal distribution that may not accurately represent the measurement dataset. Parameters of the mixture model are estimated using the expectation-maximization (EM) algorithm. The accuracy of the proposed mixture model is compared to other commonly utilized unimodal distributions showing significant improvement in representing the empirical dataset. The measured data, as well as the developed model, can be used for accurate planning and deployments of wireless B2B networks for use in various sporting and other related activities.
dc.identifierM. Cheffena and M. Mohamed, “The application of lognormal mixture shadowingmodel for B2B channels,”IEEE Sensors Letters, vol. 2, no. 3, pp. 1–4, 2018.
dc.identifier2475-1472
dc.identifierhttp://hdl.handle.net/20.500.11810/5356
dc.identifier10.1109/LSENS.2018.2848296
dc.identifier.urihttp://hdl.handle.net/20.500.11810/5356
dc.languageen
dc.publisherIEEE
dc.relationIEEE Sensors Letters;Volume: 2 , Issue: 3
dc.subjectShadow mapping , Sensors , Nakagami distribution , Histograms , Mixture models , Wireless networks
dc.titleThe Application of Lognormal Mixture Shadowing Model for B2B Channels
dc.typeJournal Article, Peer Reviewed

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Department of Electronics and Telecommunications Engineering