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  • Küçük Resim
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    Machine learning for adaptive modulation in medical body sensor networks using visible light communication
    (Institute of Electrical and Electronics Engineers Inc., 2024) Rizi, Reza Bayat; Forouzan, Amir Reza; Miramirkhani, Farshad; Sabahi, Mohamad Farzan
    In 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.
  • Küçük Resim
    Yayın
    Machine learning-driven adaptive modulation for VLC-enabled medical body sensor networks
    (Iran University of Science and Technology, 2024-12) Rizi, Reza Bayat; Forouzan, Amir R.; Miramirkhani, Farshad; Sabahi, Mohamad F.
    Visible 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.