Intelligent Mobile Handover Prediction for Zero Downtime Edge Application Mobility

Abstract

Ultra-reliable low-latency communication (URLLC) services are intrinsically challenging to deliver, with many 5G and future services, including mobile game streaming, adding further complexity by demanding zero service downtime in high-mobility scenarios. Solving these challenges is essential and must be addressed beyond mobile gaming to realise a multitude of current and future services like eX-tended/Virtual Reality(XR/VR) or holoportation in mobile scenarios. Multi-access Edge Computing (MEC) brings services “closer” to user consumption with evident advantages yet at the cost of maintaining a zero downtime guarantee when user handovers (HOs) are prevalent due to the decentralisation of services towards the network edge. In this work, we design and evaluate intelligent HO prediction models between radio 5G Base Stations. The motivation for timely user HO prediction lies in being a vital presupposition for path steering and other MANO control actions in contemporary programmable 5G networks to deliver a zero downtime perception during HO events. Our detailed simulation and actual testbed evaluation results show that effective HO prediction can be achieved using a combination of Long Short-Term Memory (LSTM) or gradient boost regression with classification models, with the latter filtering out any Reference Signal Received Power (RSRP) prediction input outliers for predicting the serving cell.