Mobility Prediction-Based Autonomous Proactive Energy Saving (AURORA) Framework for Emerging Ultra-Dense Networks

Abstract

Increased network wide energy consumption is a paramount challenge that hinders wide scale ultra-dense networks (UDNs) deployments. While several energy saving (ES) enhancement schemes have been proposed recently, these schemes have one common tenancy. They operate in reactive mode, i.e., to increase ES, cells are switched ON/OFF reactively in response to changing cell loads. Though, significant ES gains have been reported for such ON/OFF schemes, the inherent reactiveness of these ES schemes limits their ability to meet the extremely low latency and high QoS expected from future cellular networks vis-a-vis 5G and beyond. To address this challenge, in this paper we propose a novel user mobility prediction based autonomous proactive energy saving (AURORA) framework for future UDN. Instead of observing changes in cell loads passively and then reacting to them, AURORA uses past hand over traces to determine future cell loads. This prediction is then used to proactively schedule small cell sleep cycles. AURORA also incorporates the effect of cell individual offsets for balancing load among cells to ensure QoS while maximizing ES. Extensive system level simulations leveraging realistic SLAW model based mobility traces show that AURORA can achieve significant energy reduction gain without noticeable impact on QoS.