  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, opportunistic data transfer, and predictive
caching. User Equipment (UE)-based prediction approaches that rely on passive
measurements of network quality indicators have successfully been applied to
forecast the throughput of vehicular data transmissions. However, the
achievable prediction accuracy is limited as the UE is unaware of the current
network load. To overcome this issue, we propose a cooperative data rate
prediction approach which brings together knowledge from the client and network
domains. In a real world proof-of-concept evaluation, we utilize the Software
Defined Radio (SDR)-based control channel sniffer FALCON in order to mimic the
behavior of a possible network-assisted information provisioning within future
6G networks. The results show that the proposed cooperative prediction approach
is able to reduce the average prediction error by up to 30%. With respect to
the ongoing standardization efforts regarding the implementation of
intelligence for network management, we argue 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.
