Machine-Learning-Based Predictive Handover
Paper titled “Machine-Learning-Based Predictive Handover” by Ahmed Masri, Teemu Veijalainen, Henrik Martikainen, Stephen Mwanje, Janne Ali-Tolppa and Marton Kajo has been published in the IEEE/IFIP IM 2021, May 17-21 in Bordeaux, France. The research for this paper has been done in cooperation with Nokia Bells Labs during years 2018 and 2021.
This work proposes and investigates a machine learning method for learning the optimal time and destination for handovers in 5G radio networks, as well as how to use the learned model to trigger handovers based on the predicted radio conditions. The complete solution is analyzed and compared to the state-of-the-art mobility methods to evaluate its performance in reducing the system total outage.