CRII: CNS: Towards Robust RAN Slicing: Theories, Algorithms, and Applications

CRII：CNS：迈向稳健的 RAN 切片：理论、算法和应用

• 批准号: 2103405
• 负责人: Tu Nguyen
• 金额: $ 17.5万
• 依托单位: Kennesaw State University Research and Service Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103405&HistoricalAwards=false
• 关键词: CRII; CNS; Towards; Robust; RAN

With the rapid growth of new services and Internet applications, traditional cellular networks are now faced with a major challenge of supporting diverse applications. To address the various application demands, Radio Access Network (RAN) slicing has become one of the most promising architectural technologies for the forthcoming 5G era. RAN slicing allows the physical infrastructure resources to be shared across many RAN slices, with each slice built on top of the underlying physical RAN (substrate) is a separate logical network, which provides a set of services. Each RAN slice is constituted by various virtual network functions (VNFs) distributed geographically in numerous substrate nodes. Failures may occasionally arise from substrate. RAN configuration schemes for the network are imperative to relieve VNFs from substrate node failures (remapping/re-embedding VNFs onto live substrate nodes). This project aims to explore a new scheme and algorithms to enhance the robustness of RAN slicing by addressing the RAN configuration issue for slice recovery in a unified framework, referred to as RS-configuration. This project will build a theoretical and computational scheme that formalizes this insight and provides efficient and practical techniques for RAN slice recovery by mapping VNFs efficiently in the RAN slicing. This research program will also be integrated with education to develop new courses and a new series of case-study class modules in an advanced networking course as well as the design and analysis of algorithm courses at Kennesaw State University. Outreach programs through the university will be utilized to ensure the dissemination of the results. The PI will also release the implementation of the proposed scheme and algorithms in public domains. The goal of this project is to develop optimization models and methods for 1) establishing the theoretical foundation for using RS-configuration to construct a VNF mapping plan for RAN slice recovery optimization; 2) developing a new scheme and algorithms needed to map VNFs efficiently and finally 3) applying our theoretical and algorithmic development to investigate the robustness of RAN slicing and trade-offs between RAN slice recovery and RAN configuration in the RAN-related networking environment. Methodologies will be developed in the context of mathematical programing and graph theory that will help obtain more efficient and fast solutions performing within reasonable bounds of computational complexity. Hence, this research on new scheme and algorithms for RAN slicing will provide the computational basis towards building a robust RAN slicing and contribute to the development of new networking technologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着新服务和互联网应用程序的快速增长，传统的蜂窝网络现在面临着支持各种应用程序的重大挑战。为了满足各种应用程序需求，无线电访问网络（RAN）切片已成为即将到来的5G时代最有前途的建筑技术之一。 RAN切片允许在许多RAN SLICS上共享物理基础架构资源，每个切片都在基础物理运行（基板）顶部构建（基板）是一个单独的逻辑网络，该网络提供了一组服务。每个RAN SLICE均由各种虚拟网络函数（VNF）构成，该函数（VNF）在许多基板节点上分布在地理位置上。失败有时可能是底物引起的。网络的RAN配置方案必须从底物节点故障（重新映射/重新安装VNF上的VNF）上缓解VNF。该项目旨在探索一种新的方案和算法，以通过解决统一框架中的slice恢复的RAN配置问题来增强式切片的鲁棒性，称为RS-RS-CONFIGURATION。该项目将建立一个理论和计算方案，该方案正式化了这种见解，并通过有效地绘制vnf在切片中绘制VNF来提供有效且实用的技术来进行切片恢复。该研究计划还将与教育集成，以开发新的课程和一系列新的案例研究课程模块，并在高级网络课程中以及肯尼索州立大学算法课程的设计和分析。通过大学的外展计划将用于确保结果传播。 PI还将在公共领域中发布提议的方案和算法的实施。该项目的目的是开发优化模型和方法1）建立使用RS-Configuration来构建VNF映射计划以进行RAN RAN SLICE恢复优化的理论基础； 2）开发一种有效绘制VNF所需的新方案和算法，最后3）应用我们的理论和算法开发，以调查跑步切片的稳健性和在RAN相关的网络环境中进行切片恢复与运行配置之间的稳健性。方法论将在数学编程和图形论的背景下开发，这将有助于在计算复杂性的合理范围内获得更有效和快速的解决方案。因此，这项关于新计划和跑步算法的研究将提供计算基础，以建立坚固的切片，并为新的网络技术的发展做出贡献。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来支持的，这是值得的。

项目成果

Entanglement Routing For Quantum Networks: A Deep Reinforcement Learning Approach

量子网络的纠缠路由：一种深度强化学习方法

• DOI: 10.1109/icc45855.2022.9839240
• 发表时间: 2022
• 期刊: ICC 2022 - IEEE International Conference on Communications
• 作者: Le, Linh;Nguyen, Tu N.;Lee, Ahyoung;Dumba, Braulio
• 通讯作者: Dumba, Braulio

Robust Efficient License Plate and Character Detection System Based on Simplified CNN

• DOI: 10.1145/3564746.3587108
• 发表时间: 2023-04
• 期刊: Proceedings of the 2023 ACM Southeast Conference
• 作者: Selena He;Tu N. Nguyen;Kun Suo

5G network slicing and drone-assisted applications: a deep reinforcement learning approach

5G网络切片和无人机辅助应用：深度强化学习方法

• DOI: 10.1145/3555661.3560873
• 发表时间: 2022
• 期刊: ACM MobiCom Conference: Proceedings of the 5th International ACM Mobicom Workshop on Drone Assisted Wireless Communications for 5G and Beyond
• 作者: Le, Linh;Nguyen, Tu N.;Suo, Kun;He, Jing
• 通讯作者: He, Jing

Maximizing Key Distribution Capability: An Application in Quantum Cryptography

• DOI: 10.1109/qce57702.2023.00134
• 发表时间: 2023-09
• 期刊: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE)
• 作者: Tu N. Nguyen;Dung H. P. Nguyen;Manh V. Nguyen;Thinh V. Le;Bing-Hong Liu;Thang N. Dinh

Toward an Optimal Latency-Energy Dynamic Offloading Scheme for Collaborative Cloud Networks

• DOI: 10.1109/access.2023.3280415
• 发表时间: 2023
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Jui Mhatre;Ahyoung Lee;Tu N. Nguyen