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

TL;DR

This paper proposes a machine learning-based relay algorithm for 5G V2X communication that improves message broadcasting to vehicles in NLOS conditions by identifying blockages and utilizing LOS nodes as relays.

Contribution

It introduces a novel relay mechanism combined with machine learning to enhance NLOS communication in 5G V2X networks, improving speed and coverage.

Findings

Faster information transmission with higher throughput

Wider bandwidth reuse in NLOS conditions

Effective blockage identification using ML

Abstract

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.