D. Reiss

D. Reiss, M. Catalan-Cid, D. Camps-Mur et al.

Despite the potential for higher energy efficiency in 5G networks, current 5G Non-Standalone (NSA) deployments often operate suboptimally due to low utilization of 4G and 5G carriers during extended periods. Since base stations are the primary contributors to network energy consumption, implementing cell on/off switching and traffic offloading strategies is crucial for enhancing energy efficiency in current deployments. This paper investigates energy-saving opportunities based on these strategies in a real 5G NSA deployment, utilizing a dataset provided by a European Mobile Network Operator. Using Key Performance Indicators from the dataset, we propose a data-driven framework to evaluate the energy-saving and QoS tradeoff when selectively deactivating underutilized 5G cells and offloading their traffic to 4G cells with enough resources within the same sector and site. Our results demonstrate network-wide cell switch-off opportunities ranging from 17% to 79%, while ensuring data rates between 25 Mbps and 5 Mbps, respectively.

D. Reiss, M. Catalan-Cid, D. Camps-Mur et al.

Energy efficiency is a critical concern in the deployment and operation of 5G networks, particularly due to the low utilization of 4G and 5G carriers during off-peak hours. While considerable research has focused on designing energy-efficient cell on/off switching strategies that avoid disrupting user connectivity, the integration of operator-specific policies to guarantee particular Quality of Service (QoS) levels has received limited attention. This paper presents a machine learning (ML)-based energy saving strategy, trained using a real-world dataset from a European mobile operator, that enforces operator-defined policies that jointly consider strong throughput requirements and maximum outage tolerance constraints. By tuning the model's class ratios during training, the proposed solution enables operators to manage the trade-off between energy savings and QoS policy compliance prior to deployment in live networks. Evaluation results show that the method provides substantial energy savings while maintaining policy-compliant service levels under realistic 5G operating conditions.

M. Catalan-Cid, D. Reiss, G. Castellanos et al.

Cellular networks management is being enhanced by O-RAN architecture and AI/ML solutions, enabling automated intelligent control loops for RAN optimization across various use cases. Ensuring energy sustainability is crucial to minimizing the impact of mobile networks on global energy consumption. This demo showcases the BeGREEN Intelligence Plane, an AI-driven solution for energy-efficient management of O-RAN networks. The presented workflow focuses on controlling the operational status of emulated cells, highlighting the integration of key components such as the AI Engine and the optimizations achieved through rApps and xApps