# Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

arXiv: https://arxiv.org/abs/2302.11966 · HTML: https://mlsanity.com/paper/2302.11966/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 72 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [Concept drift detection and adaptation for federated and continual learning](https://mlsanity.com/paper/2105.13309/) (2021)
- [A Systematic Study of Online Class Imbalance Learning with Concept Drift](https://mlsanity.com/paper/1703.06683/) (2017)
- [Handling Concept Drift for Predictions in Business Process Mining](https://mlsanity.com/paper/2005.05810/) (2020)
- [QoS Evaluation and Prediction for C-V2X Communication in Commercially-Deployed LTE and Mobile Edge Networks](https://mlsanity.com/paper/2002.07883/) (2020)
- [Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies](https://mlsanity.com/paper/2212.10343/) (2022)
- [On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data](https://mlsanity.com/paper/1710.04735/) (2017)
- [Machine Learning-Enabled Data Rate Prediction for 5G NSA Vehicle-to-Cloud Communications](https://mlsanity.com/paper/2109.04117/) (2021)
- [Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets](https://mlsanity.com/paper/2301.03364/) (2022)

## Built on by (most cited first, 2 of 2)

- [PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence](https://mlsanity.com/paper/2507.14211/) (2025)
- [Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning](https://mlsanity.com/paper/2509.18933/) (2025)

## Abstract (arXiv)

As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.