Intelligent health monitoring in 6G networks: machine learning-enhanced VLC-based medical body sensor networks

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dc.authorid0009-0004-4752-0959
dc.authorid0009-0007-3815-9542
dc.authorid0000-0002-6691-9779
dc.contributor.authorAntaki, Bilalen_US
dc.contributor.authorDalloul, Ahmed Hanyen_US
dc.contributor.authorMiramirkhani, Farshaden_US
dc.date.accessioned2025-08-29T12:01:32Z
dc.date.available2025-08-29T12:01:32Z
dc.date.issued2025-05-23
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineeringen_US
dc.departmentIşık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programıen_US
dc.departmentIşık University, School of Graduate Studies, Master’s Program in Computer Engineeringen_US
dc.description.abstractRecent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationAntaki, B., Dalloul, A. H. & Miramirkhani, F. (2025). Intelligent health monitoring in 6G networks: machine learning-enhanced VLC-based medical body sensor networks. Sensors, 25(11), 1-32. doi:https://doi.org/10.3390/s25113280en_US
dc.identifier.doi10.3390/s25113280
dc.identifier.endpage32
dc.identifier.issn1424-8220
dc.identifier.issue11
dc.identifier.scopus2-s2.0-105007761392
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/6681
dc.identifier.urihttps://doi.org/10.3390/s25113280
dc.identifier.volume25
dc.identifier.wosWOS:001506141000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorAntaki, Bilalen_US
dc.institutionauthorDalloul, Ahmed Hanyen_US
dc.institutionauthorMiramirkhani, Farshaden_US
dc.institutionauthorid0009-0004-4752-0959
dc.institutionauthorid0009-0007-3815-9542
dc.institutionauthorid0000-0002-6691-9779
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofSensorsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAdaptive modulationen_US
dc.subjectArtificial intelligence (AI)en_US
dc.subjectChannel modelingen_US
dc.subjectChannel parameter estimationen_US
dc.subjectMachine Learning (ML)en_US
dc.subjectVisible Light Communication (VLC)en_US
dc.subjectChannel estimationen_US
dc.subjectElectrotherapeuticsen_US
dc.subjectLight modulationen_US
dc.subjectPulse modulationen_US
dc.subjectChannel parameters estimationen_US
dc.subjectPath lossen_US
dc.subjectRoot mean square delay spreadsen_US
dc.subjectVisible lighten_US
dc.subjectAdaptive modulationen_US
dc.subjectInterneten_US
dc.subjectImpacten_US
dc.titleIntelligent health monitoring in 6G networks: machine learning-enhanced VLC-based medical body sensor networksen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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