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Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond

Peng Zhang, Yong Xiao, Yingyu Li, Xiaohu Ge, Guangming Shi, Yang Yang

TL;DR

The paper tackles the challenge of achieving net-zero carbon emissions for AI-enabled 6G networks by proposing an evaluation framework for the lifecycle emissions of network AI and a joint Dynamic Energy Trading and Task Allocation (DETA) optimization. It demonstrates the approach on a Federated Edge Intelligence (FEI) case study, showing significant emissions reductions (up to 74.9%) through energy trading and task offloading across edge and cloud resources. The work also discusses open problems, including joint lifecycle optimization, new emission metrics, and improving model generalization to curb energy costs. Overall, the contributions provide a framework and practical direction for greener network AI in 6G and beyond, balancing performance with environmental impact.

Abstract

A global effort has been initiated to reduce the worldwide greenhouse gas (GHG) emissions, primarily carbon emissions, by half by 2030 and reach net-zero by 2050. The development of 6G must also be compliant with this goal. Unfortunately, developing a sustainable and net-zero emission systems to meet the users' fast growing demands on mobile services, especially smart services and applications, may be much more challenging than expected. Particularly, despite the energy efficiency improvement in both hardware and software designs, the overall energy consumption and carbon emission of mobile networks are still increasing at a tremendous speed. The growing penetration of resource-demanding AI algorithms and solutions further exacerbate this challenge. In this article, we identify the major emission sources and introduce an evaluation framework for analyzing the lifecycle of network AI implementations. A novel joint dynamic energy trading and task allocation optimization framework, called DETA, has been introduced to reduce the overall carbon emissions. We consider a federated edge intelligence-based network AI system as a case study to verify the effectiveness of our proposed solution. Experimental results based on a hardware prototype suggest that our proposed solution can reduce carbon emissions of network AI systems by up to 74.9%. Finally, open problems and future directions are discussed.

Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond

TL;DR

The paper tackles the challenge of achieving net-zero carbon emissions for AI-enabled 6G networks by proposing an evaluation framework for the lifecycle emissions of network AI and a joint Dynamic Energy Trading and Task Allocation (DETA) optimization. It demonstrates the approach on a Federated Edge Intelligence (FEI) case study, showing significant emissions reductions (up to 74.9%) through energy trading and task offloading across edge and cloud resources. The work also discusses open problems, including joint lifecycle optimization, new emission metrics, and improving model generalization to curb energy costs. Overall, the contributions provide a framework and practical direction for greener network AI in 6G and beyond, balancing performance with environmental impact.

Abstract

A global effort has been initiated to reduce the worldwide greenhouse gas (GHG) emissions, primarily carbon emissions, by half by 2030 and reach net-zero by 2050. The development of 6G must also be compliant with this goal. Unfortunately, developing a sustainable and net-zero emission systems to meet the users' fast growing demands on mobile services, especially smart services and applications, may be much more challenging than expected. Particularly, despite the energy efficiency improvement in both hardware and software designs, the overall energy consumption and carbon emission of mobile networks are still increasing at a tremendous speed. The growing penetration of resource-demanding AI algorithms and solutions further exacerbate this challenge. In this article, we identify the major emission sources and introduce an evaluation framework for analyzing the lifecycle of network AI implementations. A novel joint dynamic energy trading and task allocation optimization framework, called DETA, has been introduced to reduce the overall carbon emissions. We consider a federated edge intelligence-based network AI system as a case study to verify the effectiveness of our proposed solution. Experimental results based on a hardware prototype suggest that our proposed solution can reduce carbon emissions of network AI systems by up to 74.9%. Finally, open problems and future directions are discussed.
Paper Structure (14 sections, 5 figures, 1 table)

This paper contains 14 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Life Cycle Assessment of Network AI
  3. Carbon Emission Sources of 6G Network AI
  4. Dynamic Energy and Task Allocation Framework
  5. Case Study: An FEI-based Network AI Implementation
  6. Architecture Overview
  7. Experimental Setup
  8. Experimental Results
  9. Open Problems
  10. Joint Lifecycle Optimization
  11. New Evaluation Metrics
  12. Low-cost Model Generalization and Transfer
  13. Conclusion
  14. Carbon Impact Statement

Figures (5)

  • Figure 1: Major components and carbon emission sources of a 6G Networking System.
  • Figure 2: An FEI-based network AI implementation in a networking system as well as the corresponding lifecycle of the implementation.
  • Figure 3: Energy consumption and carbon emissions of an FEL network with different numbers of edge servers.
  • Figure 4: Carbon emissions of an FEL network at different stages of its lifecycle and the portions of different individual stages in the total carbon emissions under different target model accuracy levels.
  • Figure 5: Comparison of carbon emissions of network AI implementation when using different optimization solutions.