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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.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.Yayın Enabling 5G and 6G technologies through millimeter-wave and VLC integration for enhanced remote health monitoring systems(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2025-07-01) Dalloul, Ahmed Hany Assad; Miramirkhani, Farshad; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Elektronik Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Electronics Engineering M.S. ProgramThis thesis examines the pivotal role of wireless networks in healthcare, emphasizing the need for high-performance technologies like 5G and emerging 6G to enable efficient data transfer between medical devices such as sensors and remote monitoring equipment. We delve into the current research landscape surrounding 5G mmWave technology in remote health monitoring systems, focusing on its applications, main challenges, and future trends. We explore the wireless connectivity requirements of reconfigurable hybrid optical-radio-based Medical Body Sensor Networks (MBSNs), proposing an extension of conventional MBSNs to more flexible and generic solutions. This thesis introduces a comprehensive literature review across diverse domains including antenna design, small implantable antennas, on-body wearable solutions, and adaptable detection and imaging systems. Our research further investigates methodological approaches in monitoring systems, analyzing channel characteristics, advancements in wireless capsule endoscopy, and sensing and imaging techniques. Additionally, we explore how 6G's framework integrates Visible Light Communication (VLC) in healthcare, demonstrating how VLC-enabled MBSNs can revolutionize remote patient monitoring and real-time health data transmission by accurately estimating VLC channel parameters, such as channel DC gain and RMS delay spread. We introduce a sophisticated ray tracing technique and ML-based algorithm to model channels and estimate path loss and RMS delay spread within different hospital settings such as ICU ward and family-type patient room. The detailed results of the hospital scenarios are listed using various machine learning algorithms such as LSTM, GRU, RNN, Linear Regression SVR, and KNN. The estimation was illustrated and detailed comprehensively by choosing the best-performing ML technique.
