AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction for High-Mobility Scenarios
| Autor: | Daniel Fabian Kulzer, Martin Kasparick, Alexandros Palaios, Raja Sattiraju, Dennis Wieruch, Slawomir Stanczak, Oscar D. Ramos-Cantor, Jens Schwardmann, Gerhard Fettweis, Hugues Tchouankem, Fabian Gottsch, Hans D. Schotten, Philipp Geuer |
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| Rok vydání: | 2021 |
| Předmět: |
Vehicular ad hoc network
Computer science business.industry Distributed computing Industrial production Quality of service Automotive industry 020206 networking & telecommunications Provisioning 02 engineering and technology Communications system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Use case Resource management business |
| Zdroj: | VTC Spring |
| DOI: | 10.1109/vtc2021-spring51267.2021.9449059 |
| Popis: | The integration of functions into future communication systems that predict crucial Quality of Service (QoS) parameters is expected to enable many new or enhanced use cases, for example, in vehicular networks and Industry 4.0. Especially with high user mobility, QoS prediction is required in an End-to-End (E2E) fashion to guarantee uninterrupted connectivity and provisioning of real-time applications. In this paper, we present a concise list of mobility use cases, both from automotive and industrial production domains, that benefit from Artificial Intelligence-based QoS prediction. These applications are investigated in the publicly-funded research project AI4Mobile by a representative consortium of industry and academia. Based on a literature review, we identify the main challenges in realizing predictive QoS at high mobility, and we propose research directions to enable the envisioned E2E solutions. |
| Databáze: | OpenAIRE |
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