Digital-Twin assisted Network Energy Optimization during Low Traffic Hours
Shuvam Chakraborty, Ahmed Bedewy, Wenjun Li, Navid Abedini
TL;DR
This work tackles energy efficiency in idle-mode operations for future 6G RANs by using a high-fidelity digital twin to analyze system-level optimization across cells and beams. It formulates three NES strategies—local beam-level, global cell-level, and joint cell-beam optimization—and demonstrates how increasingly global optimization yields greater energy savings. Through a mmWave urban DT, the authors report up to 44% network energy savings, along with insights on practicality, deployment policies, and the impact on UE operation. The study provides a practical framework and guidelines for DT-driven, energy-conscious network management in low-traffic conditions.
Abstract
As wireless network technology advances towards the sixth generation (6G), increasing network energy consumption has become a critical concern due to the growing demand for diverse services, radio deployments at various frequencies, larger bandwidths, and more antennas. Network operators must manage energy usage not only to reduce operational cost and improve revenue but also to minimize environmental impact by reducing the carbon footprint. The 3rd Generation Partnership Project (3GPP) has introduced several network energy savings (NES) features. However, the implementation details and system-level aspects of these features have not been thoroughly investigated. In this paper, we explore system-level resource optimization for network energy savings in low-traffic scenarios. We introduce multiple NES optimization formulations and strategies, and further analyze their performance using a detailed network digital twin. Our results demonstrate promising NES gains of up to 44%. Additionally, we provide practical considerations for implementing the proposed schemes and examine their impacts on user equipment (UE) operation.