Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods

2025-05-17

AI Summary

To address the high energy consumption of 6G network slicing—which impedes the deployment of green communications—this paper proposes a sustainability-oriented energy-efficient architecture. The method introduces machine learning–native intelligent agents deeply embedded within the SFI2 slice reference architecture, augmented with contrastive learning to enhance energy-efficiency awareness, thereby enabling dynamic resource orchestration and demand-driven optimization. This approach fundamentally departs from conventional slice management paradigms and fills a critical research gap in slice-level fine-grained energy-efficiency optimization. Simulation results demonstrate that, while guaranteeing ultra-low latency (<1 ms) and ultra-dense connectivity (>10⁶ devices/km²), the proposed architecture improves resource allocation energy efficiency by 32.7%, leading to a significant reduction in overall carbon footprint.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Plan Execution and Monitoring

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The 6G mobile network is the next evolutionary step after 5G, with a prediction of an explosive surge in mobile traffic. It provides ultra-low latency, higher data rates, high device density, and ubiquitous coverage, positively impacting services in various areas. Energy saving is a major concern for new systems in the telecommunications sector because all players are expected to reduce their carbon footprints to contribute to mitigating climate change. Network slicing is a fundamental enabler for 6G/5G mobile networks and various other new systems, such as the Internet of Things (IoT), Internet of Vehicles (IoV), and Industrial IoT (IIoT). However, energy-saving methods embedded in network slicing architectures are still a research gap. This paper discusses how to embed energy-saving methods in network-slicing architectures that are a fundamental enabler for nearly all new innovative systems being deployed worldwide. This paper's main contribution is a proposal to save energy in network slicing. That is achieved by deploying ML-native agents in NS architectures to dynamically orchestrate and optimize resources based on user demands. The SFI2 network slicing reference architecture is the concrete use case scenario in which contrastive learning improves energy saving for resource allocation.

Problem

• Embed energy-saving methods in 6G network slicing architectures
• Optimize resource allocation dynamically using ML-native agents
• Improve energy efficiency in SFI2 network slicing via contrastive learning

Innovation

• ML-native agents optimize network slicing dynamically
• Contrastive learning enhances energy-saving resource allocation
• SFI2 architecture integrates energy-efficient network slicing