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Yayın Path loss and RMS delay spread model for VLC-based patient health monitoring system(Institute of Electrical and Electronics Engineers Inc., 2022-05-13) Dönmez, Barış; Miramirkhani, FarshadVisible Light Communication (VLC) emerges as a supplementary technology to ubiquitous Radio Frequency (RF) since VLC meets the very high data rate, very high reliability, and ultra-low latency requirements driven by the trends in beyond-5G communication systems. Since VLC offers a solution to Electromagnetic Interference (EMI) and security problems in hospital environments, it becomes a better alternative for Medical Body Sensor Networks (MBSNs). Nonetheless, user mobility in a 3D environment causes a degradation in channel DC gain that leads to an optical path loss and also affects the time dispersive properties of multipath channels. In our paper, we adopt a ray tracing-based site-specific channel modeling method to characterize VLC-based MBSNs channel parameters. Based on the channel characteristics, we propose statistical models for path loss and Root Mean Square (RMS) delay spread in realistic Intensive Care Unit (ICU) ward and Family-Type Patient Room (FTPR) where user upon which three MBSNs nodes placed walks over extensive realistic random trajectories. The simulation results indicate that both path loss and RMS delay spread follow a log-normal distribution.Yayın 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 FarzanIn 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.
