Deep Learning-Based Handover Prediction for 5G and Beyond Networks

Abstract

Although the 5G New Radio standard empowers the mobile communication networks with diverse technologies such as Massive MIMO, mmWave deployments, and much more, some network functionalities still do not explore the potential of assembling Artificial Intelligence to their methodologies. The handover procedure is planned very similarly to in older 3GPP networks, based on simple power measurement comparisons and rudimentary parameter tuning, such as Time-To-Trigger and Hysteresis. This work develops and evaluates with simulations and real network data a new Deep Learning approach to support the handover triggering decision toward a data-driven procedure for next-generation networks. Our solution relies on predicting future samples of standard Reference Signals using Long Short-Term Memory Networks (LSTM) in the first stage. After, the predicted power samples are sent to a binary classification algorithm to identify if the time series will lead or not to a handover triggering. The results show a mean absolute error of around 0.6 dB predicting power signal samples and over 97% of accuracy, indicating the future handover trigger moment. Finally, we discuss possible use cases to implement our model, including Open RAN and MEC architectures.