Radio Link Failure Prediction in 5G Networks

Abstract

Radio Link Failure (RLF) is a challenging problem in 5G networks as it may decrease communication reliability and increases latency. This is against the objectives of 5G, particularly for the ultra-Reliable Low Latency Communications (uRLLC) traffic class. RLF can be predicted using radio measurements reported by User Equipment (UE)s, such as Reference Signal Receive Power (RSRP), Reference Signal Receive Quality (RSRQ), Channel Quality Indicator (CQI), and Power HeadRoom (PHR). However, it is very challenging to derive a closed-form model that derives RLF from these measurements. To fill this gap, we propose to use Machine Learning (ML) techniques, and specifically, a combination of Long Short Term Memory (LSTM) and Support Vector Machine (SVM), to find the correlation between these measurements and RLF. The RLF prediction model was trained with real data obtained from a 5G testbed. The validation process of the model showed an accuracy of 98% when predicting the connection status (i.e., RLF). Moreover, to illustrate the usage of the RLF prediction model, we introduced two use-cases: handover optimization and UAV trajectory adjustment.