Machine Learning Based Channel Modeling for Vehicular Visible Light Communication
نویسندگان
چکیده
Vehicular Visible Light Communication (VVLC) is preferred as a vehicle to everything (V2X) communications scheme due its highly secure, low complexity and radio frequency (RF) interference free characteristics, exploiting the line of sight (LoS) propagation visible light usage already existing emitting diodes (LEDs). Current VVLC channel models based on deterministic stochastic methods provide limited accuracy for path loss prediction since heavily depend site-specific geometry average out model parameters without considering environmental effects. Moreover, there exists no wireless that can be adopted response (CFR) prediction. In this paper, we propose novel framework machine learning (ML) modeling with goal improving building CFR through consideration multiple input variables related mobility The proposed incorporates measurable variables, e.g., distance, ambient light, receiver inclination angle, optical turbulence, exploitation feed forward neural network multilayer perceptron (MLP-NN), radial basis function (RBF-NN) decision tree Random Forest methods. also includes data pre-processing step, synthetic minority over sampling technique (SMOTE) balancing, hyper-parameter tuning iterative grid search, further improve accuracy. ML demonstrated real world vehicle-to-vehicle (V2V) communication data. MLP-NN, RBF-NN generate accurate predictions 3.53 dB, 3.81 3.95 dB root mean square error (RMSE), whereas best performing two-term exponential fitting provides 7 RMSE. MLP-NN are predict 3.78 3.60 RMSE, respectively.
منابع مشابه
The Case for Vehicular Visible Light
The Case for Vehicular Visible Light Communication (VLC): Architecture, Services and Experiments
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ژورنال
عنوان ژورنال: IEEE Transactions on Vehicular Technology
سال: 2021
ISSN: ['0018-9545', '1939-9359']
DOI: https://doi.org/10.1109/tvt.2021.3107835