Clustering-Based Scenario-Aware LTE Grant Prediction

Abstract

Reducing the energy consumption of mobile phones is a crucial design goal for cellular modem solutions for LTE and 5G standards. Recent approaches for dynamic power management incorporate traffic prediction to power down components of the modem as often as possible. These predictive approaches have been shown to still provide substantial energy savings, even if trained purely on-line. However, a higher prediction accuracy could be achieved when performing predictor training off-line. Additionally, having pre-trained predictors opens up the ability to successfully employ predictive techniques also in less favorable situations such as short intervals of stable traffic patterns. For this purpose, we introduce a notion of similarity, based on which a clustering is performed to identify similar traffic patterns. For each resulting cluster, i.e., an identified traffic scenario, one predictor is designed and trained off-line. At run time, the system selects the pre-trained predictor with the lowest average short-term false negative rate allowing for energy-efficient and highly accurate on-line prediction. Through experiments, it is shown that the presented mixed static/dynamic approach is able to improve the prediction accuracy and energy savings compared to a state-of-the-art approach by factors of up to 2 and up to 1.9, respectively.