Deep Learning Based Speed Profiling for Mobile Users in 5G Cellular Networks

Future mobile networks promise to be more intelligent and to guarantee a better service for users. This intelligence can be highly accentuated by cognition of mobile users' behavior and conditions. The user speed is an important element of user profile. We are interested in real time speed profiling by detecting speed range of an active user. We refer to this as Mobile Speed Profiling (MSP) of users. Indeed, performing MSP can notably improve the self-adaptation and self-optimization capabilities of these networks. It can help mobile network in resource management and handover management. This paper introduces a Deep Learning based solution to automatically construct the MSP of a mobile user. We empirically evaluate the effectiveness of our approach using real-time and highly representative radio data that best captures the real daily movements of users. This data includes ground truth information and the whole dataset has been gathered massively from many diversified mobility situations. Results show that the profiling of UE's speed with fine granularity or on multiple ranges can be achieved with high accuracy on real data measured in heterogeneous deployments for 5G networks.