Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

Hernangómez, Rodrigo; Geuer, Philipp; Palaios, Alexandros; Daniel, Schäufele,; Watermann, Cara; Khawla, Taleb-Bouhemadi,; Parvini, Mohammad; Krause, Anton; Partani, Sanket; Vielhaus, Christian; Kasparick, Martin; Kulzer, Daniel Fabian; Burmeister, Friedrich; Stańczak, Sławomir; Fettweis, Gerhard; Schotten, Hans D.; Fitzek, Frank H. P.

doi:10.48550/arxiv.2212.10343

Cited by 1 publication

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“…We believe that ML-based predictions do not only have the potential to improve specific use cases but also serve as an important enabler for a more proactive network. We made more datasets [65,66] available to the research community that are based on similar collection principles as the ones we described. In the future, we plan to integrate the lessons learned towards methods that can innately handle some of the dynamics we noticed in the radio environment, such as nonstationarities and concept drifts.…”

Section: Discussionmentioning

confidence: 99%

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

Palaios¹,

Vielhaus²,

Kulzer³

et al. 2023

Preprint

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As cellular networks evolve towards the 6th Generation (6G), Machine Learning (ML) is seen as a key enabling technology to improve the capabilities of the network. ML provides a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific Quality of Service (QoS) requirement. With predictive Quality of Service (pQoS), 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 HD mapping applications. We discuss the entire ML 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 ML models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on ML 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 ML models within diverse scenarios. We also use Explainable AI (XAI) to show that ML 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.

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Section: Discussionmentioning

confidence: 99%