Publication:
Path loss estimation and jamming detection in hybrid RF-VLC vehicular networks: a machine-learning framework

dc.contributor.coauthorUllah, Arif
dc.contributor.coauthorChoi, Wooyeol
dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.kuauthorErgen, Sinem Çöleri
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2025-01-19T10:30:05Z
dc.date.issued2023
dc.description.abstractEmerging vehicle-to-everything (V2X) networks employ machine-learning (ML) techniques to provide adaptive, reliable, secure, and low-latency communication. Previously proposed fitting-based path loss and rule-based jamming detection schemes in vehicular networks lack accuracy due to the highly mobile and complex vehicular environment. Standalone ML models are utilized for different regression and classification problems in vehicular networks; however, these models still provide limited accuracy in the case of limited datasets. This article proposes a hybrid learning framework for jamming detection and path loss predictions based on the successive usage of multiple deep neural network (DNN) blocks, in which each block extends the feature set of the succeeding block for efficient and fast learning. The proposed hybrid DNN model for jamming detection comprises three DNN blocks, that is, a multilayer perceptron (MLP) block and two sequential learning blocks with bidirectional LSTM and gated recurrent unit (GRU) layers. Similarly, for path estimation, the proposed hybrid model includes one MLP block followed by two bagged tree-based learning blocks. The proposed models for jamming and path loss prediction are trained and tested on a real-world dataset. In the IEEE 802.11p link, the proposed hybrid model has been demonstrated to classify injected jamming signals with a significantly improved accuracy of 90.4% compared to existing benchmarks, including the random forest (RaFo) classifier and the deep convolutional neural network (DCNN). Similarly, the proposed hybrid DNN model for path loss estimation outperforms existing RaFo and fitting-based models, with a mean absolute error (MAE) reduction of 4.5 and 0.9 dB in the case of vehicular visible light communication (V-VLC) and IEEE 802.11p links, respectively.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.issue24
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipThis work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF- 2021R1I1A3050535 and in part by the Scientific and Technological Council of Turkey under Grant 119E35.
dc.description.volume23
dc.identifier.doi10.1109/JSEN.2023.3329490
dc.identifier.issn1530-437X
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85177077251
dc.identifier.urihttps://doi.org/10.1109/JSEN.2023.3329490
dc.identifier.urihttps://hdl.handle.net/20.500.14288/25969
dc.identifier.wos1161670600096
dc.keywordsChannel modeling
dc.keywordsHeterogeneous networks
dc.keywordsHybrid RF-VLC
dc.keywordsIEEE 80211p
dc.keywordsJamming detection
dc.keywordsMachine learning (ML)
dc.keywordsPath loss estimation
dc.keywordsVehicular visible light communication (V-VLC)
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.grantnoMinistry of Education, MOE, (NRF- 2021R1I1A3050535); National Research Foundation of Korea, NRF; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (119E35)
dc.relation.ispartofIEEE Sensors Journal
dc.subjectEngineering
dc.titlePath loss estimation and jamming detection in hybrid RF-VLC vehicular networks: a machine-learning framework
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorErgen, Sinem Çöleri
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Electrical and Electronics Engineering
relation.isOrgUnitOfPublication21598063-a7c5-420d-91ba-0cc9b2db0ea0
relation.isOrgUnitOfPublication.latestForDiscovery21598063-a7c5-420d-91ba-0cc9b2db0ea0
relation.isParentOrgUnitOfPublication8e756b23-2d4a-4ce8-b1b3-62c794a8c164
relation.isParentOrgUnitOfPublication.latestForDiscovery8e756b23-2d4a-4ce8-b1b3-62c794a8c164

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