A Data Driven Framework for QoE-Aware Intelligent EN-DC Activation

Abstract

In emerging 5G networks, User Equipment camps traditionally on 4G network. Later, if the user requests a 5G service, it can simultaneously camp on 4G and 5G using EUTRAN New-Radio Dual-Connectivity (EN-DC) approach. In EN-DC, poor radio-conditions in either 4G or 5G network can be detrimental to user Quality-of-Experience (QoE). Although operators want to maximize EN-DC activation to fully utilize the 5G network, sub-optimal parameter configuration to turn on ENDC can compromise key-performance-indicators due to excessive radio-link-failures (RLFs) or voice-muting. While the need to maximize the EN-DC activation is obvious for maximizing the 5G network's utility, RLF and mute avoidance are vital to maintain the QoE requirements. To achieve aforementioned tradeoff, this paper presents the first solution to optimally configure the EN-DC activation parameters. We collect two datasets from real network to develop machine-learning-models to predict RLF and muting, respectively. We also investigate and compare the potential of various under-sampling, oversampling, and synthetic data generation techniques including Tomek-Links and Generative Adversarial Networks for their potential to address the data imbalance problem inherent in the real network training data. Leveraging these models, we formulate and solve two QoE-aware optimization problems that can maximize EN-DC activation while minimizing RLF or voice-muting. System-level simulation-based results show that compared to state-of-the-art solution that does not take into account RLF or voice-muting risk in EN-DC activation, the proposed solution can intelligently determine ENDC activation criteria that minimize the risk of RLF and voice muting while giving the operator's desired level of priority to maximize 5G network utilization.