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Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks
Reza Bayat Rizi , Amir R. Forouzan , Farshad Miramirkhani , Mohamad F. Sabahi
Abstract:   (66 Views)
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.
Keywords: VLC-based MBSNs, adaptive modulation, machine learning, reinforcement learning.
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Type of Study: Closed - 2024 Special Issue on Applications of Deep Learning in Electrical and Electronic Engineerin | Subject: Optical Communications
Received: 2024/08/10 | Revised: 2024/12/15 | Accepted: 2024/12/14
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.