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

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

A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions

The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet goes far beyond data, audio and video delivery over fixed and mobile networks, and even beyond allowing communication and collaboration among things. It is expected to enable haptic communication and allow skill set delivery over networks. Some examples of potential applications are tele-surgery, vehicle fleets, augmented reality and industrial process automation. Several papers already cover many of the Tactile Internet-related concepts and technologies, such as haptic codecs, applications, and supporting technologies. However, none of them offers a comprehensive survey of the Tactile Internet, including its architectures and algorithms. Furthermore, none of them provides a systematic and critical review of the existing solutions. To address these lacunae, we provide a comprehensive survey of the architectures and algorithms proposed to date for the Tactile Internet. In addition, we critically review them using a well-defined set of requirements and discuss some of the lessons learned as well as the most promising research directions

TalkyCars: A Distributed Software Platform for Cooperative Perception among Connected Autonomous Vehicles based on Cellular-V2X Communication

Autonomous vehicles are required to operate among highly mixed traffic during their early market-introduction phase, solely relying on local sensory with limited range. Exhaustively comprehending and navigating complex urban environments is potentially not feasible with sufficient reliability using the aforesaid approach. Addressing this challenge, intelligent vehicles can virtually increase their perception range beyond their line of sight by utilizing Vehicle-to-Everything (V2X) communication with surrounding traffic participants to perform cooperative perception. Since existing solutions face a variety of limitations, including lack of comprehensiveness, universality and scalability, this thesis aims to conceptualize, implement and evaluate an end-to-end cooperative perception system using novel techniques. A comprehensive yet extensible modeling approach for dynamic traffic scenes is proposed first, which is based on probabilistic entity-relationship models, accounts for uncertain environments and combines low-level attributes with high-level relational- and semantic knowledge in a generic way. Second, the design of a holistic, distributed software architecture based on edge computing principles is proposed as a foundation for multi-vehicle high-level sensor fusion. In contrast to most existing approaches, the presented solution is designed to rely on Cellular-V2X communication in 5G networks and employs geographically distributed fusion nodes as part of a client-server configuration. A modular proof-of-concept implementation is evaluated in different simulated scenarios to assess the system\u27s performance both qualitatively and quantitatively. Experimental results show that the proposed system scales adequately to meet certain minimum requirements and yields an average improvement in overall perception quality of approximately 27 %