Cooperative Localization Utilizing Reinforcement Learning for 5G Networks

TL;DR

This paper proposes a reinforcement learning-based method to enhance collaborative localization accuracy for autonomous vehicles in 5G networks, leveraging mmWave signals and D2D communication to improve positioning precision.

Contribution

It introduces a novel reinforcement learning approach to select optimal range measurement techniques in 5G-based collaborative localization for autonomous vehicles.

Findings

Reinforcement learning improves localization accuracy.

Enhanced robustness against faulty measurements.

Effective use of 5G mmWave signals and D2D communication.

Abstract

The demand for accurate localization has risen in recent years to enable the emerging of autonomous vehicles. To have these vehicles in the traffic ecosystem of smart cities, the need for an accurate positioning system is emphasized. To realize accurate positioning, collaborative localization plays an important role. This type of localization computes range measurements between vehicles and improves the accuracy of position by correcting the possibly faulty values of one of them by using the more accurate values of the other. 5G signals with the technology of Millimeter Wave (mmWave) support precise range measurements and 5G networks provide Device to Device (D2D) communication which improves collaborative localization. The aim of this paper is to provide an accurate collaborative positioning for autonomous vehicles, which is less prone to errors utilizing reinforcement learning technique for selecting the most accurate and suitable range measurement technique for the 5G signal.