13 papers · 1 filter
Towards Cooperative Data Rate Prediction for Future Mobile and Vehicular 6G Networks
Benjamin Sliwa, Robert Falkenberg, Christian Wietfeld
Machine learning-based data rate prediction is one of the key drivers for anticipatory mobile networking with applications such as dynamic Radio Access Technology (RAT) selection,…
FALCON: An accurate real-time monitor for client-based mobile network data analytics
Robert Falkenberg, Christian Wietfeld
Network data analysis is the fundamental basis for the development of methods to increase service quality in mobile networks. This requires accurate data of the current load in the…
Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction
Benjamin Sliwa, Robert Falkenberg, Thomas Liebig +2
The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. Ho…
Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks
Benjamin Sliwa, Thomas Liebig, Robert Falkenberg +2
While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly up-to-date informatio…
Machine Learning Based Uplink Transmission Power Prediction for LTE and Upcoming 5G Networks using Passive Downlink Indicators
Robert Falkenberg, Benjamin Sliwa, Nico Piatkowski +1
Energy-aware system design is an important optimization task for static and mobile Internet of Things (IoT)-based sensor nodes, especially for highly resource-constrained vehicles…
Resource-efficient Transmission of Vehicular Sensor Data Using Context-aware Communication
Benjamin Sliwa, Thomas Liebig, Robert Falkenberg +2
Upcoming Intelligent Traffic Control Systems (ITSCs) will base their optimization processes on crowdsensing data obtained for cars that are used as mobile sensor nodes. In conclusi…