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

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.Comment: 18 pages, 12 Figures. Accepted on IEEE Acces

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

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the onboarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.Comment: 5 pages, 6 figures. Accepted for presentation at IEEE conference VTC2023-Spring. Available dataset at https://ieee-dataport.org/open-access/berlin-v2

Empirical spatio-temporal characterization of radio environment properties

During the last decade, the increasing popularity of wireless communications and the exponential growth in the number of wireless devices has raised the question of how the radio environment is changing. Recent developments in the areas of small-cell networks, Internet of Things devices, machine-to-machine communications, and dynamic spectrum access schemes require a better understanding of radio noise characteristics and of spectrum usage in general. The main goal of this thesis is to provide an up-to-date characterization of the radio environment below 6 GHz. Our measurements and analysis are particularly tailored to provide input to radio network researchers, industry, and regulators. The major focus of this thesis is an empirical analysis of spectrum usage based on carefully designed spectrum measurements across Europe. Our measurement campaigns are unique in capturing spectrum use characteristics in depth. This thesis has two major results. First, our concurrent measurements across Europe allow us to study the time dynamics of spectrum use, revealing common characteristics between the different sites at identical times. Second, we study the spatial variations of spectrum use in Paris and London. We use spatial statistics to capture the overall variance and to describe the dynamics present during the measurements. The different measurement approaches we adopted provide complementary views of the statistical characteristics of spectrum use in these high population density study areas. A second theme of the thesis is a noise level study in the contemporary radio environment. Experimental knowledge of radio noise characteristics has not been updated recently; in fact, most high quality analyses are based on measurements conducted decades ago. The way networks are designed and developed has, of course, changed considerably since then. Our measurements capture radio noise by-products at diverse locations stemming from devices that act as intentional, unintentional or incidental radiators. We provide detailed characterization of frequency and time characteristics of the captured noise by-products. We also discuss the issue of the spatial correlation of noise components which can vary from tens to hundreds of meters. The characteristics of the noise by-products also reveal strong deviation from the AWGN noise characteristics typically assumed in literature. We suggest that future receiver designs should assume the constant presence of noise sources with very diverse time characteristics