# QoS Prediction for 5G Connected and Automated Driving

arXiv: https://arxiv.org/abs/2107.05000 · HTML: https://mlsanity.com/paper/2107.05000/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 11 Jul 2021.

## Built on by (most cited first, 3 of 3)

- [DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks](https://mlsanity.com/paper/2401.10158/) (2024)
- [Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements](https://mlsanity.com/paper/2307.02329/) (2023)
- [PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence](https://mlsanity.com/paper/2507.14211/) (2025)

## Abstract (arXiv)

5G communication system can support the demanding quality-of-service (QoS) requirements of many advanced vehicle-to-everything (V2X) use cases. However, the safe and efficient driving, especially of automated vehicles, may be affected by sudden changes of the provided QoS. For that reason, the prediction of the QoS changes and the early notification of these predicted changes to the vehicles have been recently enabled by 5G communication systems. This solution enables the vehicles to avoid or mitigate the effect of sudden QoS changes at the application level. This article describes how QoS prediction could be generated by a 5G communication system and delivered to a V2X application. The tele-operated driving use case is used as an example to analyze the feasibility of a QoS prediction scheme. Useful recommendations for the development of a QoS prediction solution are provided, while open research topics are identified.