Architecture, Protocols, and Algorithms for Location-Aware Services in Beyond 5G Networks

TL;DR

This paper reviews 5G positioning frameworks for automotive and railway automation, proposing sensor and cellular data fusion to enhance high-accuracy localization in challenging environments.

Contribution

It introduces a novel architecture and protocols for fusing cellular and sensor measurements to improve real-time positioning in Beyond 5G networks.

Findings

Fusion of cellular and sensor data improves localization accuracy.

Proposed architecture supports high-precision positioning in occlusion scenarios.

Numerical example demonstrates viability of the fusion approach.

Abstract

The automotive and railway industries are rapidly transforming with a strong drive towards automation and digitalization, with the goal of increased convenience, safety, efficiency, and sustainability. Since assisted and fully automated automotive and train transport services increasingly rely on vehicle-to-everything communications, and high-accuracy real-time positioning, it is necessary to continuously maintain high-accuracy localization, even in occlusion scenes such as tunnels, urban canyons, or areas covered by dense foliage. In this paper, we review the 5G positioning framework of the 3rd Generation Partnership Project in terms of methods and architecture and propose enhancements to meet the stringent requirements imposed by the transport industry. In particular, we highlight the benefit of fusing cellular and sensor measurements and discuss required architecture and protocol support for achieving this at the network side. We also propose a positioning framework to fuse cellular network measurements with measurements by onboard sensors. We illustrate the viability of the proposed fusion-based positioning approach using a numerical example.