Reference Network and Localization Architecture for Smart Manufacturing based on 5G

5G promises to shift Industry 4.0 to the next level by allowing flexible production. However, many communication standards are used throughout a production site, which will stay so in the foreseeable future. Furthermore, localization of assets will be equally valuable in order to get to a higher level of automation. This paper proposes a reference architecture for a convergent localization and communication network for smart manufacturing that combines 5G with other existing technologies and focuses on high-mix low-volume application, in particular at small and medium-sized enterprises. The architecture is derived from a set of functional requirements, and we describe different views on this architecture to show how the requirements can be fulfilled. It connects private and public mobile networks with local networking technologies to achieve a flexible setup addressing many industrial use cases.Comment: 10 pages; submitted to 6th International Conference on System-Integrated Intelligence. Intelligent, flexible and connected systems in products and production, 7-9 September Genova, Ital

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

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Hexa-X:the European 6G flagship project

Abstract Hexa-X will pave the way to the next generation of wireless networks (Hexa) by explorative research (X). The Hexa-X vision is to connect human, physical, and digital worlds with a fabric of sixth generation (6G) key enablers. The vision is driven by the ambition to contribute to objectives of growth, global sustainability, trustworthiness, and digital inclusion. Key 6G value indicators and use cases are defined against the background of technology push, society and industry pull as well as objectives of technology sovereignty. Key areas of research have been formulated accordingly to include connecting intelligence, network of networks, sustainability, global service coverage, extreme experience, and trustworthiness. Critical technology enablers for 6G are developed in the project including, sub-THz transceiver technologies, accurate stand-alone positioning and radio-based imaging, improved radio performance, artificial intelligence (AI) / machine learning (ML) inspired radio access network (RAN) technologies, future network architectures and special purpose solutions including future ultra-reliable low-latency communication (URLLC) schemes. Besides technology enablers, early trials will be carried out to help assess viability and performance aspects of the key technology enablers. The 6G Hexa-X project is integral part of European and global research effort to help define the best possible next generation of networks