论文标题
无处不在智能的延迟保证6G：网络演算方法
Latency Guarantee for Ubiquitous Intelligence in 6G: A Network Calculus Approach
论文作者
论文摘要
随着5G的逐渐部署以及边缘智能（EI）的持续普及，网络边缘数据的爆炸性增长促进了6G和无处不在的智能（UBII）的快速发展。本文旨在探索一种新方法，以确保6G在Terahertz（THZ）环境中的极端随机性，THZ通道尾巴行为以及UBII随机组件产生的延迟分布尾部特性，并找到最佳的解决方案，以最大程度地减少UBII的最佳解决方案。在本文中，网络演算的到达曲线和服务曲线可以很好地表征无线通道的随机性质，无线系统的尾巴行为和网络计算的E2E服务曲线可以建模UBII中延迟分布的尾部特征。具体而言，我们首先提出了6G，边缘计算（EC），Edge Deep Leaver（DL）和UBII面临的需求和挑战。然后，我们提出了基于网络计算的UBII系统的层次结构，网络模型和服务延迟模型。此外，两项案例研究表明，网络演算方法在分析和建模6G中UBII的延迟保证方面的有用性和有效性。最后，概述了有关UBII延迟保证的未来开放研究问题。
With the gradual deployment of 5G and the continuous popularization of edge intelligence (EI), the explosive growth of data on the edge of the network has promoted the rapid development of 6G and ubiquitous intelligence (UbiI). This article aims to explore a new method for modeling latency guarantees for UbiI in 6G given 6G's extremely stochastic nature in terahertz (THz) environments, THz channel tail behavior, and delay distribution tail characteristics generated by the UBiI random component, and to find the optimal solution that minimizes the end-to-end (E2E) delay of UbiI. In this article, the arrival curve and service curve of network calculus can well characterize the stochastic nature of wireless channels, the tail behavior of wireless systems and the E2E service curve of network calculus can model the tail characteristic of the delay distribution in UbiI. Specifically, we first propose demands and challenges facing 6G, edge computing (EC), edge deep learning (DL), and UbiI. Then, we propose the hierarchical architecture, the network model, and the service delay model of the UbiI system based on network calculus. In addition, two case studies demonstrate the usefulness and effectiveness of the network calculus approach in analyzing and modeling the latency guarantee for UbiI in 6G. Finally, future open research issues regarding the latency guarantee for UbiI in 6G are outlined.
