ML-Aided Traffic-Aware Base Station Sleep Threshold Design with User Throughput Guarantees

As the demand for mobile data continues to grow, the energy consumption of mobile networks becomes a major concern. Specifically, base stations account for over 76% of energy usage in mobile networks. We consider a two-tier cellular system, one offering basic coverage while the other offers extra capacity. To save energy, existing network features can opportunistically shut down capacity layer cells when physical resources are lightly utilized. Nevertheless, this is performed without guaranteeing coverage cells can maintain the sought user-centric service quality. To address this challenge, we propose a machine learning (ML)-aided search approach that dynamically designs energy-saving configurations for each cell in each hour while being constrained with a pre-defined quality of service (QoS) measure. This ML model was trained using data collected from 10,283 cells of a live network. We introduce two different approaches to provide these settings: Adaptive QoS Threshold Optimization Algorithm (AQTOA) and an Exhaustive Search (ES) baseline. AQTOA is a low complexity ML-aided search algorithm designed to determine the optimal shutdown threshold for capacity cells while ensuring that QoS requirements are met. Through extensive live network experimentation, the AQTOA results indicate a 1.8% improvement in energy savings compared to the earlier static settings models while maintaining a more strict QoS level than the one addressed in the previous work.