Digital-Twin assisted Network Energy Optimization during Low Traffic Hours

TL;DR

This paper investigates system-level energy optimization in 6G wireless networks during low traffic hours using a digital twin, achieving up to 44% energy savings while considering practical implementation and user impact.

Contribution

It introduces multiple network energy savings optimization strategies and analyzes their performance through a detailed digital twin model, addressing system-level aspects.

Findings

Up to 44% energy savings achieved.

Effective strategies for low-traffic energy optimization.

Insights into practical implementation and user impact.

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.