面向6G及未来网络AI中的净零碳排放
Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond
AI总结：
本文针对6G网络AI系统的碳排放挑战，提出DETA联合动态能量交易与任务分配优化框架，基于联邦边缘智能案例和硬件原型验证，可减少高达74.9%的碳排放。
摘要：
一项全球性努力已经启动，旨在到2030年将全球温室气体（GHG）排放（主要是碳排放）减少一半，并在2050年实现净零排放。6G的发展也必须符合这一目标。然而，开发可持续且净零排放的系统以满足用户对移动服务（尤其是智能服务和应用）快速增长的需求，可能比预期更具挑战性。特别是，尽管软硬件设计的能效有所提升，但移动网络的总能耗和碳排放仍在以惊人的速度增长。资源需求型AI算法和解决方案的日益普及进一步加剧了这一挑战。在本文中，我们确定了主要排放源，并引入了一个评估框架来分析网络AI实现的生命周期。我们引入了一种名为DETA的新型联合动态能量交易与任务分配优化框架，以减少整体碳排放。我们以基于联邦边缘智能的网络AI系统作为案例研究，验证所提解决方案的有效性。基于硬件原型的实验结果表明，我们提出的解决方案可将网络AI系统的碳排放量减少高达74.9%。最后，讨论了开放问题和未来方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
