Vehicular Visible Light Communication System: Modeling and Visualizing Critical Outdoor Propagation Characteristics

Sharda, Pranav; Bhatnagar, Manav R.

doi:10.1109/tvt.2023.3282615

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…They proposed a path loss model considering vertical displacement and oscillation effects and studied the optimal position of photodetectors to reduce the error rate performance. Sharda and Bhatnagar [293] modeled the impact of actual outdoor propagation characteristics to present LoS and NLoS V2V-VLC models and analyzed the path loss and data rate of this model.…”

Section: Visible Light Communicationmentioning

confidence: 99%

A Survey on Video Streaming for Next-Generation Vehicular Networks

Huang,

Cheng,

Lien

et al. 2024

Electronics

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As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission.

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Section: Visible Light Communicationmentioning

confidence: 99%

A Survey on Video Streaming for Next-Generation Vehicular Networks

Huang,

Cheng,

Lien

et al. 2024

Electronics

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“…It also helps in improving the phase, capacity, and frequency transformations of IRS elements installed in the infrastructure of roads, cars, bus stations, and buildings. Moreover, weather conditions like rain, fog, ice, and snow affect the strength of the LoS links, and these effects can also cause reflectivity [ 23 , 69 ].…”

Section: Vehicular Vlc Overviewmentioning

confidence: 99%

“…It considers lateral shift; link distance; clear, rainy, and foggy weather types; receiver aperture diameter and transmitter beam divergence angle; and asymmetrical light. In [ 69 ], the effect of shadowing and weather disturbances on V2V VLC communication performance was studied, as shading causes blockage of the line-of-sight path. Furthermore, the system was analyzed using diversity–multiplexing tradeoff, as it provides an optimal tradeoff between multiplex gain and diversity gain.…”

Section: Vehicular Vlc Overviewmentioning

confidence: 99%

“…For example, when the size of the PD receiving area is large, or the field of view (FOV) is large, it will allow the capture of a large number of optical signals in addition to allowing the reception of other unwanted signals, which causes system degradation and thus reduces the signal-to-noise ratio (SNR) [ 17 , 18 , 133 ]. Researchers and academics have considered the effect of the active area size parameter for vehicular VLC [ 37 , 38 , 56 , 69 , 84 , 134 , 135 ]. It is possible to improve VLC receiver performance by utilizing an optical filter.…”

Section: Vehicular Vlc Channel Modeling and Propagation Characteristicsmentioning

confidence: 99%

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A Survey of Vehicular VLC Methodologies

Al Hasnawi,

Marghescu

2024

Sensors

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Visible Light Communication (VLC) has recently emerged as an alternative to RF-based wireless communications. VLC for vehicles has demonstrated its potential for Intelligent Transportation Systems (ITSs) to exchange information between vehicles and infrastructure to achieve ITS core goals, such as improving road safety, passenger comfort, and traffic flow. This paper seeks to provide a detailed survey of vehicular VLC systems. This paper presents an overview of current developments in vehicular VLC systems and their benefits and limitations for experienced researchers and newcomers.

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“…A significant concern is the potential for delay in data packet transmission from an emergency vehicle, which could jeopardize a patient's timely arrival at a medical facility. While there is extensive literature on channel characterization across urban, rural, and suburban areas [2][3][4][5][6], only a few have explored the potential of ANN, stochastic models, and machine learning for channel modeling [7][8][9][10]. For example, Ref.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks

Muktar,

Fono,

Zongo

2023

Electronics

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In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Such obstacles lead to multipath effects, which could substantially impact data transmission due to fading. Therefore, quantifying the impact of buildings on transmission quality is a key parameter of the propagation model, especially in critical scenarios involving emergency vehicles where reliable communication is of utmost importance. In this research, we propose a supervised learning approach based on Artificial Neural Networks (ANNs) to develop a predictive model capable of estimating the level of signal degradation, represented by the Bit Error Rate (BER), based on the obstacles perceived by moving emergency vehicles. By establishing a relationship between the level of signal degradation and the encountered obstacles, our proposed mechanism enables efficient routing decisions being made prior to the transmission process. Consequently, data packets are routed through paths that exhibit the lowest BER. To collect the training data, we employed Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban MObility (SUMO) simulator, leveraging real-world data sourced from the OpenStreetMap (OSM) geographic database. OSM enabled us to gather geospatial data related to the Two-Dimensional (2D) geometric structure of buildings, which served as input for our Artificial Neural Network (ANN). To determine the most suitable algorithm for our ANN, we assessed the accuracy of ten learning algorithms in MATLAB, utilizing five key metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R), and Maximum Prediction Error (MaxPE). For each algorithm, we conducted fifteen iterations based on ten hidden neurons and gauged its accuracy against the aforementioned metrics. Our analysis highlighted that the ANN underpinned by the Conjugate Gradient With Powell/Beale Restarts (CGB) learning algorithm exhibited superior performance in terms of MSE, RMSE, MAE, R, and MaxPE compared to other algorithms such as Levenberg–Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Fletcher–Powell Conjugate Gradient (CGF), Polak–Ribiére Conjugate Gradient (CGP), One-Step Secant (OSS), and Variable Learning Rate Backpropagation (GDX). The BER prediction by our ANN incorporates the TWO-RAY Ground (TRG) propagation model, an adjustable parameter within NS-3. When subjected to 300 new samples, the trained ANN’s simulation outcomes illustrated its capability to learn, generalize, and successfully predict the BER for a new data instance. Overall, our research contributes to enhancing the performance and reliability of communication in urban VANET environments, especially in critical scenarios involving emergency vehicles, by leveraging supervised learning and artificial neural networks to predict signal degradation levels and optimize routing decisions accordingly.

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Vehicular Visible Light Communication System: Modeling and Visualizing Critical Outdoor Propagation Characteristics

Cited by 5 publications

References 31 publications

A Survey on Video Streaming for Next-Generation Vehicular Networks

A Survey on Video Streaming for Next-Generation Vehicular Networks

A Survey of Vehicular VLC Methodologies

Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks

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