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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 Channel modeling and characterization for VLC-based medical body sensor networks: trends and challenges(IEEE, 2021-11-15) Dönmez, Barış; Mitra, Rangeet; Miramirkhani, FarshadOptical Wireless Communication (OWC) refers to transmission in unguided propagation media through the use of optical carriers, i.e., visible, Infrared (IR), and Ultraviolet (UV) bands. In this paper, we focus on indoor Visible Light Communication (VLC)-based Medical Body Sensor Networks (MBSNs) which allow the Light Emitting Diodes (LEDs) to communicate between on-body sensors/subdermal implants and on-body central hubs/monitoring devices while also serving as a luminaire. Since the Quality-of-Service (QoS) of the communication systems depends heavily on realistic channel modeling and characterization, this paper aims at presenting an up-to-date survey of works on channel modeling activities for MBSNs. The first part reviews existing IR-based MBSNs channel models based on which VLC channel models are derived. The second part of this review provides details on existing VLC-based MBSNs channel models according to the mobility of the MBSNs on the patient’s body. We also present a realistic channel modeling approach called site-specific ray tracing that considers the skin tissue for the MBSNs channel modeling for realistic hospital scenarios.Yayın Channel modeling and characterization for VLC-based MBSNs impaired by 3D user mobility(IEEE, 2021-11-27) Dönmez, Barış; Miramirkhani, FarshadThis paper focuses on channel modeling and characterization of indoor visible light communication (VLC)-based medical body sensor networks (MBSNs) which establish links between light-emitting diodes (LEDs) and MBSNs nodes couple with photodetectors (PDs) placed on the shoulder (D1), wrist (D2), and ankle (D3) of the mobile user who walks over random trajectories in 3D scenarios of ICU ward and family type patient room. We adopt non-sequential ray-tracing to obtain channel impulse responses (CIRs) and channel characteristics over random trajectories. Based on simulation results, it is observed that channel DC gains exhibit sinusoidal behaviour for D1 and D2 except for D3 (i.e., due to the number of diffuse rays received at D3), as the user approaches and moves away from the luminaries. It is also revealed that a flat fading channel can be modeled if a data rate lower than 7.03 Mbit/s, i.e., sufficient for MBSNs applications, is chosen.Yayın A path loss model for vehicle-to-vehicle visible light communications(Institute of Electrical and Electronics Engineers Inc., 2019-07) Eldeeb, Hossien Badr; Miramirkhani, Farshad; Uysal, MuratThe increasing adoption of LEDs in exterior automotive lighting makes visible light communication (VLC) a natural solution for vehicular networking. In this paper, we consider a vehicle-to-vehicle link and propose a path loss expression as a function of distance and different weather conditions. We conduct ray tracing simulations and verify the accuracy of proposed expression. We further use this expression to derive the achievable transmission distance for a targeted data rate while satisfying a given value of bit error rate. Numerical results are presented to demonstrate the achievable distances for single and dual photodetector deployment cases.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.
