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Yayın Channel modelling for indoor visible light communications(Royal Society Publishing, 2020-04-17) Miramirkhani, Farshad; Uysal, MuratVisible light communication (VLC) allows the dual use of light-emitting diodes (LEDs) for wireless communication purposes in addition to their primary purpose of illumination. As in any other communication system, realistic channel modelling is a key for VLC system design, analysis and testing. In this paper, we present a comprehensive survey of indoor VLC channel models. In order to set the background, we start with an overview of infrared (IR) channel modelling, which has received much attention in the past, and highlight the differences between visible and IR optical bands. In the light of these, we present a comparative discussion of existing VLC channel modelling studies and point out the relevant advantages and disadvantages. Then, we provide a detailed description of a site-specific channel modelling approach based on non-sequential ray tracing that precisely captures the optical propagation characteristics of a given indoor environment. We further present channel models for representative deployment scenarios developed through this approach that were adopted by the Institute of Electrical and Electronics Engineering (IEEE) as reference channel models. Finally, we consider mobile VLC scenarios and investigate the effect of receiver location and rotation for a mobile indoor user. This article is part of the theme issue ‘Optical wireless communication’.Yayın A review of recent innovations in remote health monitoring(Multidisciplinary Digital Publishing Institute (MDPI), 2023-12) Dalloul, Ahmed Hany; Miramirkhani, Farshad; Kouhalvandi, LidaThe development of remote health monitoring systems has focused on enhancing healthcare services’ efficiency and quality, particularly in chronic disease management and elderly care. These systems employ a range of sensors and wearable devices to track patients’ health status and offer real-time feedback to healthcare providers. This facilitates prompt interventions and reduces hospitalization rates. The aim of this study is to explore the latest developments in the realm of remote health monitoring systems. In this paper, we explore a wide range of domains, spanning antenna designs, small implantable antennas, on-body wearable solutions, and adaptable detection and imaging systems. Our research also delves into the methodological approaches used in monitoring systems, including the analysis of channel characteristics, advancements in wireless capsule endoscopy, and insightful investigations into sensing and imaging techniques. These advancements hold the potential to improve the accuracy and efficiency of monitoring, ultimately contributing to enhanced health outcomes for patients.Yayın Intelligent health monitoring in 6G networks: machine learning-enhanced VLC-based medical body sensor networks(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-23) Antaki, Bilal; Dalloul, Ahmed Hany; Miramirkhani, FarshadRecent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions.
