Handover Triggering Prediction with the Two-Step XGBOOST Ensemble Algorithm for Conditional Handover in Non-Terrestrial Networks

Abstract

A Non-Terrestrial Network (NTN) is a network system that enables service for areas where terrestrial networks cannot cover. An NTN provides communication services using flying objects such as UAVs, HAPs, and satellites. In the case of satellites, they move in Earth’s orbit at a constant speed. Ground services from continuously moving satellites cause frequent handovers. In addition, frequent handovers may come as a load between User Equipment (UE) and the communication system, which leads to degradation of service quality. Unlike Terrestrial Networks (TN), communication services are provided to UEs at altitudes ranging from 20 km to 35,584 km, rather than from base stations close to the ground. Service at high altitudes is unreliable due to the measurement values that were previously used as quality indicators to operate terrestrial networks. Moreover, service at high altitudes demands long-distance communication, and propagation delay occurs from the long-distance communication. In the 3GPP Rel. 17 document, it is suggested that the above problems should be solved. This paper tries to solve the problem by proposing the two-step XGBOOST, a CART-based Gradient Boosting Model. Handover in TN uses measurement-based conditional handover (CHO), but the measured values in the NTN environment are not valid. Using this, the distance between the UE and the center of the cell and the elevation angle are used to construct a model that predicts the HO triggering time point. In order to overcome the propagation delay caused by communication at a high altitude, a model that predicts the distance and elevation angle between the UE and the center of the cell considering the propagation delay is proposed. The model is composed of two-step XGBOOST. The one-step model is a model in which the UE predicts the distance and elevation angle between cell centers after propagation delay at the time when satellite position information is transmitted to the UE. The two-step model predicts handover triggering occurrence based on the data predicted by the one-step result. As a result of the experiment, the model considering the propagation delay showed about 8% better performance on average than the model not considering the propagation delay, and the XGBOOST model achieved an average F1-score of 0.9891 in the propagation delay experiments.