Self-Attention-Based Uplink Radio Resource Prediction in 5G Dual Connectivity

Abstract

Mobile communication technology is evolving rapidly and becoming increasingly ubiquitous, thereby increasing the demand for uplink data-intensive applications (e.g., personal broadcasting and live augmented/virtual reality videos). Recently, to facilitate a cost-effective and smooth transition from 4G to 5G networks, most carriers leverage existing 4G infrastructures using a dual connectivity (DC) feature. DC increases uplink throughput and mobility robustness; however, it also causes unprecedented dynamic fluctuations in radio channels due to the coverage discrepancy between 4G and 5G networks. Thus, in this paper, we propose a self-attention-based deep learning model to predict uplink radio resources in 5G DC. We trained the proposed model on commercial 5G DC traffic data from three major carriers in South Korea and obtained an average prediction accuracy of 95.08% under various mobility and cell-load conditions. The proposed model explains the rationale for the obtained predictions by highlighting the parts of the input time-series data that are important to realize accurate prediction. We also demonstrate the usability of the proposed model using a network emulator based on real-world 5G trace data. Extensive evaluations demonstrate that the existing congestion control algorithms can achieve excellent performance when used with the proposed model.