Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks
Visible Light Communication, a key optical wireless technology, offers reliable, high-bandwidth, and secure communication, making it a promising soloution for a variety of applications. Despite its many advantages, optical wireless communication faces challenges in medical environments due to fluctu...
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2024-11-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
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| Online Access: | http://ijeee.iust.ac.ir/article-1-3407-en.pdf |
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| author | Reza Bayat Rizi Amir R. Forouzan Farshad Miramirkhani Mohamad F. Sabahi |
| author_facet | Reza Bayat Rizi Amir R. Forouzan Farshad Miramirkhani Mohamad F. Sabahi |
| author_sort | Reza Bayat Rizi |
| collection | DOAJ |
| description | Visible Light Communication, a key optical wireless technology, offers reliable, high-bandwidth, and secure communication, making it a promising soloution 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. |
| format | Article |
| id | doaj-art-6c90a3917f1c40288e7c9bcb0e1dbeac |
| institution | Kabale University |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-6c90a3917f1c40288e7c9bcb0e1dbeac2025-01-09T18:47:15ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902024-11-012047990Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor NetworksReza Bayat Rizi0Amir R. Forouzan1Farshad Miramirkhani2Mohamad F. Sabahi3 Dept. of Electrical Engineering, University of Isfahan Dept. of Electrical Engineering, University of Isfahan Department of Electrical and Electronics Engineering, Isik University Dept. of Electrical Engineering, University of Isfahan Visible Light Communication, a key optical wireless technology, offers reliable, high-bandwidth, and secure communication, making it a promising soloution 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.http://ijeee.iust.ac.ir/article-1-3407-en.pdfvlc-based mbsnsadaptive modulationmachine learningreinforcement learning. |
| spellingShingle | Reza Bayat Rizi Amir R. Forouzan Farshad Miramirkhani Mohamad F. Sabahi Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks Iranian Journal of Electrical and Electronic Engineering vlc-based mbsns adaptive modulation machine learning reinforcement learning. |
| title | Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks |
| title_full | Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks |
| title_fullStr | Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks |
| title_full_unstemmed | Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks |
| title_short | Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks |
| title_sort | machine learning driven adaptive modulation for vlc enabled medical body sensor networks |
| topic | vlc-based mbsns adaptive modulation machine learning reinforcement learning. |
| url | http://ijeee.iust.ac.ir/article-1-3407-en.pdf |
| work_keys_str_mv | AT rezabayatrizi machinelearningdrivenadaptivemodulationforvlcenabledmedicalbodysensornetworks AT amirrforouzan machinelearningdrivenadaptivemodulationforvlcenabledmedicalbodysensornetworks AT farshadmiramirkhani machinelearningdrivenadaptivemodulationforvlcenabledmedicalbodysensornetworks AT mohamadfsabahi machinelearningdrivenadaptivemodulationforvlcenabledmedicalbodysensornetworks |