Machine learning based channel modeling for Vehicular Visible Light Communication

Abstract

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