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A Generalized Transformer-based Radio Link Failure Prediction Framework in 5G RANs by Hasan, Kazi; Trappenberg, Thomas; Haque, Israat is a scholarly article available to read on EtoBox.

What is A Generalized Transformer-based Radio Link Failure Prediction Framework in 5G RANs about?

Radio link failure (RLF) prediction system in Radio Access Networks (RANs) is critical for ensuring seamless communication and meeting the stringent requirements of high data rates, low latency, and improved reliability in 5G networks. However, weather conditions such as precipitation, humidity, temperature, and wind impact these communication links. Usually, historical radio link Key Performance Indicators (KPIs) and their surrounding weather station observations are utilized for building learning-based RLF prediction models. However, such models must be capable of learning the spatial weather context in a dynamic RAN and effectively encoding time series KPIs with the weather observation data. Existing works fail to incorporate both of these essential design aspects of the prediction models. This paper fills the gap by proposing GenTrap, a novel RLF prediction framework that introduces a graph neural network (GNN)-based learnable weather effect aggregation module and employs state-of-the-art time series transformer as the temporal feature extractor for radio link failure prediction. The proposed aggregation method of GenTrap can be integrated into any existing prediction model to

Author

Hasan, Kazi; Trappenberg, Thomas; Haque, Israat

Published

2024

Language

EN