Predictive Quality of Service in 5G and Beyond
Submit a paper – deadline 13 August
Automotive applications of today rely on mobile network support for connectivity and have specific Quality of Service (QoS) requirements. Due to varying network conditions, it may not be always possible to fulfil these QoS requirements. In such cases, it is useful for the vehicle to obtain an in-advance notification about any upcoming changes in the available QoS so that it can take appropriate action, such as adapting or altogether stopping applications that can’t be operated in a safe manner under the predicted QoS conditions. This concept is called ‘Predictive QoS’.
Predictive QoS is being extensively discussed in industry organizations such as 5GAA as well as standards organizations such as 3GPP. Predictive QoS could be an important part of 3GPP release 17 and beyond. Therefore, it is very pertinent for the research community working in the field of 5G and beyond wireless technologies. The proposed workshop tries to bring together different aspects of predictive QoS, namely:
- use-cases and requirements (from the automotive industry point of view)
- implementation and operation (from an MNO or service provider point of view).
Additionally, the session aims to discuss the technical challenges associated with:
- QoS prediction, e.g. accuracy of prediction, which parameters are most useful to be predicted, AI/ML models for prediction, the differences between long-term and short-term prediction
- delivering the prediction, e.g. periodic vs event-triggered delivery, subscription vs relevance model for the receiver of the prediction
- adaptation to the QoS prediction, e.g. reaction of the vehicular application to change in QoS, orchestration and/or arbitration of the adaptation.
With the increasing emphasis on achieving higher levels of automated driving, it is essential to develop connectivity concepts, such as predictive QoS, which will likely play an important role in enabling the corresponding automated driving applications. Hence, it is important for the research community to discuss such concepts presently. The outcome of such discussions will drive the timely development and standardization of these concepts.
- Machine Learning and AI for QoS prediction
- Algorithms and techniques for timely notification of QoS change
- Standardization requirements for QoS Prediction deployment
- Architectures and algorithms for QoS prediction
- Real-time QoS prediction requirements
- Use Cases and scenarios for QoS prediction e.g., V2X applications, industrial applications
- Application Adaptation based on QoS prediction
- Privacy issues for data collection to enable QoS prediction notifications
- Prototypes, and performance evaluation of QoS prediction for automated driving and other Industrial applications
- Dirk Hetzer, Senior Expert Business Innovation, T-SYSTEMS, Germany
- Massimo Condoluci, Senior Researcher, Ericsson, Sweden
- Alassane Samba, Research Engineer, Orange Labs, France
- APOSTOLOS KOUSARIDAS, Principal Research Engineer, Huawei Technologies, Germany
- TIM LEINMUELLER, Senior Technical Manager, DENSO Automotive Deutschland GmbH, Germany