Reduction of packet error rate in V2V communication based on machine learning

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dc.authorid0000-0001-5988-8882en_US
dc.contributor.authorAlyassri, Salam
dc.contributor.authorIlyas, Mohammad
dc.contributor.authorMarhoon, Ali
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2022-01-14T12:57:51Z
dc.date.available2022-01-14T12:57:51Z
dc.date.issued2021en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractReducing the accidents that resulted from traffic is a highly concerning issue. Automating cars or emergency communication are attempting the decrease these accidents. So improving the conditions of communication between vehicles under research. One of the improvements comes by standardizing transmitting packets, which is best executed by the 802.11p protocol. 802.11p encompasses a field that has the size value of the transmitted packet.. So this work centering on developing a powerfully adaptable packet size framework, which depends on the value of the signal-to-noise ratio (SNR). There is neural network (NN) controls to the framework, which prepared by candidate values of packet size come from an equation derived practically by testing it numerous times the connection between packet error ratios (PERs) and packet size values. The usage of the proposed framework comes about in a noteworthy diminishment in PER, and the comes about were appeared by practice the proposed framework and two other frameworks, one of them is satisfied without channel tracking whereas the moment had channel tracking. The comparison for band 10MHz appears there's a 46.6 percent and 36.88 percent average decrease in PER values compared to not tracking the channel and tracking the channel, separately. Comes about average improving the PER values for band 20MHz were 18 percent for the without-channeltracking and 66.91 percent for the with-channel-tracking frameworks. As a result, the inspiration to utilize NN, which is one field of machine learning (ML), was figured out. Besides, this work persuades more researches that got to be done applying the ML into the vehicle to vehicle (V2V) communication.en_US
dc.identifier.citationAlyassri, S., Ilyas, M., Marhoon, A., & Bayat, O. (2021, June). Reduction of Packet Error Rate in V2V Communication Based on Machine Learning. In 2021 International Conference on Communication & Information Technology (ICICT) (pp. 110-115). IEEE.en_US
dc.identifier.endpage115en_US
dc.identifier.scopus2-s2.0-85118443952
dc.identifier.scopusqualityN/A
dc.identifier.startpage110en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2145
dc.indekslendigikaynakScopus
dc.institutionauthorAlyassri, Salam
dc.institutionauthorIlyas, Mohammad
dc.institutionauthorBayat, Oğuz
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.ispartofIEEE
dc.relation.isversionof10.1109/ICICT52195.2021.9568487en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject802.11p Protocolen_US
dc.subjectNeural Networken_US
dc.subjectVehicle to Vehicleen_US
dc.titleReduction of packet error rate in V2V communication based on machine learning
dc.typeArticle

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