Machine learning for adaptive modulation in medical body sensor networks using visible light communication

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dc.authorid0009-0003-6542-1332
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 Rezaen_US
dc.contributor.authorMiramirkhani, Farshaden_US
dc.contributor.authorSabahi, Mohamad Farzanen_US
dc.date.accessioned2025-08-18T07:56:30Z
dc.date.available2025-08-18T07:56:30Z
dc.date.issued2024
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-Electronics Engineeringen_US
dc.description.abstractIn the context of medical body sensor networks that rely on visible light communication (VLC), adaptive modulation plays a crucial role. Despite VLC's advantages, challenges arise due to fluctuating signal strength caused by patient movement. To address this, we propose an adaptive modulation system that adjusts based on link conditions, specifically the signal-to-noise ratio (SNR). Our approach involves an uplink channel for feedback, allowing the receiver to select the appropriate modulation scheme based on measured SNR after noise mitigation. 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. By implementing a bi-directional system with real-time modulation tracking, we demonstrate the effectiveness of adaptive VLC in handling environmental changes (interference and noise). Notably, the use of the Q-learning algorithm enables real-time adaptation without prior knowledge of the environment. Our simulation results show that photodetectors placed on the shoulder and wrist benefit significantly from this approach, experiencing improved performance.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationRizi, R. B., Forouzan, A. R., Miramirkhani, F. & Sabahi, M. F. (2024). Machine learning for adaptive modulation in medical body sensor networks using visible light communication. Paper presented at the 11th International Symposium on Telecommunication: Communication in the Age of Artificial Intelligence, IST 2024, 257-262. doi:10.1109/IST64061.2024.10843598en_US
dc.identifier.doi10.1109/IST64061.2024.10843598
dc.identifier.endpage262
dc.identifier.isbn9798350356250
dc.identifier.scopus2-s2.0-85217616889
dc.identifier.scopusqualityN/A
dc.identifier.startpage257
dc.identifier.urihttps://hdl.handle.net/11729/6620
dc.identifier.urihttps://doi.org/10.1109/IST64061.2024.10843598
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMiramirkhani, Farshaden_US
dc.institutionauthorid0000-0002-6691-9779
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof11th International Symposium on Telecommunication: Communication in the Age of Artificial Intelligence, IST 2024en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdaptive modulationen_US
dc.subjectMachine learningen_US
dc.subjectReinforcement learningen_US
dc.subjectVLC-based MBSNsen_US
dc.subjectAdversarial machine learningen_US
dc.subjectBody sensor networksen_US
dc.subjectContrastive learningen_US
dc.subjectFederated learningen_US
dc.subjectLaser beamsen_US
dc.subjectLight modulationen_US
dc.subjectSelf-supervised learningen_US
dc.subjectSignal modulationen_US
dc.subjectSupervised learningen_US
dc.subjectBody sensorsen_US
dc.subjectFluctuating signalsen_US
dc.subjectMachine-learningen_US
dc.subjectNoise ratioen_US
dc.subjectSensors networken_US
dc.subjectSignal strengthsen_US
dc.subjectSignal to noiseen_US
dc.subjectVisible lighten_US
dc.subjectVisible light communication-based MBSNen_US
dc.titleMachine learning for adaptive modulation in medical body sensor networks using visible light communicationen_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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