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

• URL: http://arxiv.org/abs/2505.12132v1
• Date: Sat, 17 May 2025 20:07:34 GMT
• Title: Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods
• Authors: Rodrigo Moreira, Tereza C. M. Carvalho, Flávio de Oliveira Silva, Nazim Agoulmine, Joberto S. B. Martins,
• Abstract summary: Energy saving is a major concern for new systems in the telecommunications sector.<n>This paper's main contribution is a proposal to save energy in network slicing.<n>It is achieved by deploying ML-native agents in NS architectures to dynamically orchestrate and optimize resources based on user demands.
• Score: 0.1497962813548524
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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