Machine Learning Algorithm for NLOS Millimeter Wave in 5G V2X Communication
This work aims to improve reliable communication for autonomous and semi-autonomous vehicles in 5G V2X environments by overcoming NLOS issues, which is an incremental improvement for vehicular communication.
This paper addresses the challenge of Non-Line-of-Sight (NLOS) communication in 5G V2X millimeter wave networks. It proposes a Relay using Machine Learning (RML) algorithm that trains the millimeter wave Base Station (mmBS) to identify blockages and use Line-of-Sight (LOS) nodes as relays to broadcast messages to NLOS vehicles, resulting in faster information transmission with higher throughput and wider bandwidth reuse.
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.