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

TL;DR

This paper proposes integrating machine learning agents into 6G network slicing architectures to dynamically optimize resources and significantly enhance energy efficiency, addressing a key sustainability challenge for future networks.

Contribution

It introduces a novel approach of embedding ML-native agents within network slicing architectures to improve energy saving through dynamic resource orchestration.

Findings

ML-native agents improve energy efficiency in network slicing

Contrastive learning enhances resource allocation for energy saving

Proposed architecture demonstrates potential for sustainable 6G networks

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.