论文标题
基于机器学习的渠道建模用于车辆可见光通信
Machine Learning Based Channel Modeling for Vehicular Visible Light Communication
论文作者
论文摘要
光学无线通信（OWC）传播渠道表征在车辆可见光通信（VVLC）系统的设计和性能分析中起关键作用。当前基于确定性和随机方法的OWC通道模型无法解决迁移率引起的环境光，光学湍流和道路反射对通道表征的影响。因此，提出了基于机器学习（ML）的方案，考虑到环境光，光学湍流，道路反射效应，除了相互距离和几何形状外，还提出了以获得准确的VVLC通道损失和通道频率响应（CFR）。这项工作证明了通过多层感知馈电神经网络（MLP），径向基函数神经网络（RBF-NN）和随机的森林集合学习算法合成ML基于ML的VVLC通道模型框架。通过实际的道路测量收集的预测变量和响应变量用于训练和验证各种条件的拟议模型。此外，评估了不同预测变量对通道丢失和CFR的重要性，引入了测量VVLC通道的特征的归一化重要性。我们表明，与基于拟合曲线的VVLC通道模型相比，RBF-NN，随机森林和基于MLP的模型分别以3.53 dB，3.81 dB，3.95 dB的根平方误（RMSE）产生了更准确的通道损失估计。此外，RBF-NN和MLP模型被证明可以相对于距离，环境光和接收器倾斜角预测变量，分别为3.78 dB和3.60 dB RMSE。
Optical Wireless Communication (OWC) propagation channel characterization plays a key role on the design and performance analysis of Vehicular Visible Light Communication (VVLC) systems. Current OWC channel models based on deterministic and stochastic methods, fail to address mobility induced ambient light, optical turbulence and road reflection effects on channel characterization. Therefore, alternative machine learning (ML) based schemes, considering ambient light, optical turbulence, road reflection effects in addition to intervehicular distance and geometry, are proposed to obtain accurate VVLC channel loss and channel frequency response (CFR). This work demonstrates synthesis of ML based VVLC channel model frameworks through multi layer perceptron feed-forward neural network (MLP), radial basis function neural network (RBF-NN) and Random Forest ensemble learning algorithms. Predictor and response variables, collected through practical road measurements, are employed to train and validate proposed models for various conditions. Additionally, the importance of different predictor variables on channel loss and CFR is assessed, normalized importance of features for measured VVLC channel is introduced. We show that RBF-NN, Random Forest and MLP based models yield more accurate channel loss estimations with 3.53 dB, 3.81 dB, 3.95 dB root mean square error (RMSE), respectively, when compared to fitting curve based VVLC channel model with 7 dB RMSE. Moreover, RBF-NN and MLP models are demonstrated to predict VVLC CFR with respect to distance, ambient light and receiver inclination angle predictor variables with 3.78 dB and 3.60 dB RMSE respectively.
