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Yayın A path loss model for link budget analysis of indoor visible light communications(Istanbul Univ-Cerrahpasa, 2021-05) Miramirkhani, FarshadIn the context of beyond 5G indoor communication systems, visible light communications (VLC) has emerged as a viable supplement for existing radio frequency based systems and as an enabler for high data rate communications. However, the existing indoor VLC systems are limited by detrimental outages caused by fluctuations in the VLC channel gain because of user mobility. In this study, we proposed a tractable path loss model for indoor VLC that reflects the effect of room size and coating material of surfaces. We performed an extensive advanced ray tracing simulation to obtain the channel impulse responses within a room and presented a path loss model as a function of distance, room size, and coating material through curve fitting. In addition, path loss parameters such as the path loss exponent and the standard deviation of the shadowing component were determined. The simulation results indicate that path loss is a linear function of distance, path loss exponent is a function of room size and coating material, and shadowing follows a log normal distribution.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.
