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Machine Learning Algorithm for NLOS Millimeter Wave in 5G V2X Communication

机译:5G V2X通信中NLOS毫米波的机器学习算法

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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-ofsight) 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.
机译:用于自主和半自动驾驶的5G车辆到一切(V2X)通信利用用于通信的无线技术,并且在这种车辆网络应用中广泛实现毫米波带。本文的主要目的是将MMWAVE基站的消息从LOS(Line-ofSight)和NLOS(非LOS)广播到车辆。使用机器学习(RML)算法的继电器被配制起来训练MMB,用于识别其覆盖区域内的堵塞,并使用LOS节点作为继电器将消息广播到NLO的车辆。信息传输具有更高的吞吐量,并且涵盖了更广泛的带宽,因此当在MMB的MMB的覆盖区域内执行机器学习时,可以利益。提出了一种与机器学习结合的中继机构的独特方法,以与NLOS的移动节点进行通信。

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