Robert Falkenberg

TL;DR: Long-term field measurements reveal that FALCON reduces errors in average by three orders of magnitude compared to currently the best approach, and allows observations at locations with interference and enables mobile applications with single short-term tracking of the local load situation.

TL;DR: A novel machine learning-based approach for forecasting the resulting uplink transmission power used for data transmissions based on the available passive network quality indicators and application-level information and is well-suited for long-term power estimations.

TL;DR: In this paper, the authors proposed a cooperative data rate prediction approach which brings together knowledge from the client and network domains to forecast the throughput of vehicular data transmissions in a real world proof-of-concept evaluation in order to mimic the behavior of a possible network assisted information provisioning within future 6G networks.

TL;DR: It is argued that future 6G networks should go beyond network-focused approaches and actively provide load information to the UEs in order to fuel pervasive machine learning and catalyze UE-based network optimization techniques.