论文标题
NLOS毫米波在5G V2X通信中的机器学习算法
Machine Learning Algorithm for NLOS Millimeter Wave in 5G V2X Communication
论文作者
论文摘要
用于自主和半自治驾驶的5G车辆到所有设施（V2X）通信利用无线技术进行通信，毫米波带在这种车辆网络应用中广泛实现。本文的主要目的是将消息从MMWave基站广播到LOS（视线）和NLOS（NON-LOS）的车辆。使用机器学习（RML）算法的继电器旨在训练MMB，以识别其覆盖范围内的障碍物，并使用Los Nodes作为继电器将消息传播到NLOS的车辆。信息的传输速度更快，吞吐量较高，并且涵盖了更宽的带宽，因此，当在MMBS的覆盖范围内执行机器学习时，NLOS中大多数车辆都可以受益。提出了一种与机器学习结合的独特方法，以与NLOS的移动节点进行通信。
The 5G vehicle-to-everything (V2X) communication for autonomous and semi-autonomous driving utilizes the wireless technology for communication and the Millimeter Wave bands are widely implemented in this kind of vehicular network application. The main purpose of this paper is to broadcast the messages from the mmWave Base Station to vehicles at LOS (Line-of-sight) and NLOS (Non-LOS). Relay using Machine Learning (RML) algorithm is formulated to train the mmBS for identifying the blockages within its coverage area and broadcast the messages to the vehicles at NLOS using a LOS nodes as a relay. The transmission of information is faster with higher throughput and it covers a wider bandwidth which is reused, therefore when performing machine learning within the coverage area of mmBS most of the vehicles in NLOS can be benefited. A unique method of relay mechanism combined with machine learning is proposed to communicate with mobile nodes at NLOS.
