Machine learning-driven adaptive modulation for VLC-enabled medical body sensor networks

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dc.authorid0000-0001-6330-2573
dc.authorid0000-0002-6691-9779
dc.authorid0000-0003-2359-2582
dc.contributor.authorRizi, Reza Bayaten_US
dc.contributor.authorForouzan, Amir R.en_US
dc.contributor.authorMiramirkhani, Farshaden_US
dc.contributor.authorSabahi, Mohamad F.en_US
dc.date.accessioned2025-08-15T07:57:11Z
dc.date.available2025-08-15T07:57:11Z
dc.date.issued2024-12
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.description.abstractVisible Light Communication, a key optical wireless technology, offers reliable, high-bandwidth, and secure communication, making it a promising solution for a variety of applications. Despite its many advantages, optical wireless communication faces challenges in medical environments due to fluctuating signal strength caused by patient movement. Smart transmitter structures can improve system performance by adjusting system parameters to the fluctuating channel conditions. The purpose of this research is to examine how adaptive modulation performs in a medical body sensor network system that uses visible light communication. The analysis focuses on various medical situations and investigates machine learning algorithms. The study compares adaptive modulation based on supervised learning with that based on reinforcement learning. The findings indicate that both approaches greatly improve spectral efficiency, emphasizing the significance of implementing link adaptation in visible light communication-based medical body sensor networks. The use of the Q-learning algorithm in adaptive modulation enables real-time training and enables the system to adjust to the changing environment without any prior knowledge about the environment. A remarkable improvement is observed for photodetectors on the shoulder and wrist since they experience more DC gain.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationRizi, R. B., Forouzan, A. R., Miramirkhani, F. & Sabahi, M. F. (2024). Machine learning-driven adaptive modulation for VLC-enabled medical body sensor networks. Iranian Journal of Electrical and Electronic Engineering, 20(4), 1-11. doi:10.22068/IJEEE.20.4.3407en_US
dc.identifier.doi10.22068/IJEEE.20.4.3407
dc.identifier.endpage11
dc.identifier.issn1735-2827
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85213513437
dc.identifier.scopusqualityQ3
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/6613
dc.identifier.urihttps://doi.org/10.22068/IJEEE.20.4.3407
dc.identifier.volume20
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMiramirkhani, Farshaden_US
dc.institutionauthorid0000-0002-6691-9779
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIran University of Science and Technologyen_US
dc.relation.ispartofIranian Journal of Electrical and Electronic Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAdaptive modulationen_US
dc.subjectMachine learningen_US
dc.subjectReinforcement learningen_US
dc.subjectVLC-based MBSNsen_US
dc.titleMachine learning-driven adaptive modulation for VLC-enabled medical body sensor networksen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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