%0 Journal Article
%T 基于GRNN的协作车联网的中断性能预测<br>Prediction of Outage Probability of Cooperative Vehicular Network Based on GRNN
%A 李亚
%A 张宇
%J Hans Journal of Wireless Communications
%P 1-11
%@ 2163-3991
%D 2023
%I Hans Publishing
%R 10.12677/HJWC.2023.131001
%X 针对车联网通信系统，为了提升系统的传输性能，建立多中继多天线协作车联网模型，并设计了一种基于广义回归神经网络(GRNN)的中断概率预测算法。通信链路服从级联Nakagmi-m分布，中继使用混合译码放大转发(HDAF)协议，目的端运用等增益合并。针对多天线，设计天线选择方案，并推导出中断概率表达式；进而构建预测中断概率的数据集，建立GRNN模型。实验结果表明，信道级联、HDAF协议和等增益合并能够提升中断性能，GRNN能有效实现中断概率的预测。<br />
For the Internet of vehicles (IoV) communication system, for the sake of improving the transmission performance, a multi-relay and multi-antenna cooperative vehicular network (CVN) model was es-tablished, and an outage probability (OP) prediction algorithm based on generalized regression neural network (GRNN) was designed. The communication links follow the cascaded Nakagmi-m distribution, the relays use hybrid decode-amplify farward (HDAF) protocol, and the destination uses equal gain combining. For MIMO system, the antenna selection scheme is proposed and the OP expression is derived. Then the data set to predict the OP is constructed, and the GRNN model is es-tablished. Experimental results show that the channel cascade, HDAF protocol and equal gain com-bination can improve the interrupt performance, and GRNN can effectively predict the OP.
%K 协作车联网(CVN)，混合译码放大转发协议(HDAF)，天线选择，中断性能预测，广义回归神经网络(GRNN)；Cooperative Vehicular Network (CVN)
%K Hybrid Decode-Amplify Farward (HDAF)
%K Antenna Selection
%K Outage Performance Prediction
%K Generalized Regression Neural Network (GRNN)
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=63783