Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks

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dc.contributor.authorUsman Ali Khan, Mohammadcs
dc.contributor.authorInayatullah Babar, Mohammadcs
dc.contributor.authorRehman, Saeedcs
dc.contributor.authorKomosnĂ˝, Dancs
dc.contributor.authorHan Joo Chong, Petercs
dc.coverage.issue7cs
dc.coverage.volume24cs
dc.date.accessioned2024-05-14T07:45:50Z
dc.date.available2024-05-14T07:45:50Z
dc.date.issued2024-03-22cs
dc.description.abstractA Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals’ line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.en
dc.formattextcs
dc.format.extent1-14cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSENSORS. 2024, vol. 24, issue 7, p. 1-14.en
dc.identifier.doi10.3390/s24072021cs
dc.identifier.issn1424-8220cs
dc.identifier.orcid0000-0002-6551-7997cs
dc.identifier.other188324cs
dc.identifier.urihttps://hdl.handle.net/11012/245517
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofSENSORScs
dc.relation.urihttps://www.mdpi.com/1424-8220/24/7/2021cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1424-8220/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectlight fidelityen
dc.subjectWiFien
dc.subjecthandoveren
dc.subjectDNNen
dc.subjectHLWNeten
dc.titleOptimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networksen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-188324en
sync.item.dbtypeVAVen
sync.item.insts2024.05.14 09:45:50en
sync.item.modts2024.05.14 09:13:18en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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