Reducing Latency: Improving Handover Procedure Using Machine Learning

We present a Machine Learning (ML) use-case for reducing latency in wireless networks, by replacing some of the traditional and time-consuming procedures. The proposed method reduces the time needed for collecting a large number of measurements for the target cell selection during the inter-frequency handover procedure. Our paper contributes to the current literature, firstly, by showcasing how ML can replace traditional procedures in order to reduce the latency. Secondly, by presenting results based on the real measurements from a live network. Finally, by training a huge number of models over a large geographical area, where multiple base stations are deployed, reporting the performance over all those models. This contribution extends previous work in the area, that was based on simulations and improves confidence in the applicability of the proposed solution in real networks. The proposed method not only reduces the total handover delay and service disruption but can also be considered as an enabler for new verticals and use-cases, as we discuss in this paper.