Collaborative Research: NeTS: Medium: Black-box Optimization of White-box Networks: Online Learning for Autonomous Resource Management in NextG Wireless Networks

合作研究：NeTS：中：白盒网络的黑盒优化：下一代无线网络中自主资源管理的在线学习

• 批准号: 2312835
• 负责人: Xingyu Zhou
• 金额: $ 30万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312835&HistoricalAwards=false
• 关键词: Collaborative; Research; NeTS; Medium; Black

Next-generation (NextG) wireless networks are anticipated to revolutionize various applications, such as interactive real-time applications like Augmented Reality (AR), while meeting the high Quality-of-Experience (QoE) requirements expected by users. To achieve these goals, NextG networks are undergoing a transformation toward a white-box architecture, characterized by openness, intelligence, and a focus on user needs. Therefore, it is both timely and important to address autonomous resource management within the NextG paradigm. This project aims to facilitate the transition from traditional black-box network designs to a white-box network architecture, which will significantly reduce costs and enhance QoE performance at its core. The findings of this project will be integrated into the curricula of all participating institutions. Furthermore, this project is committed to promoting the engagement of women and underrepresented minority (URM) students through research opportunities and outreach activities at their respective institutions. Mechanisms will be established to foster leadership and participation from URM groups in an annual high-profile research workshop held at OSU.O-RAN is an operator-driven alliance dedicated to the advancement of radio access networks (RAN) toward an open architecture. This research focuses on harnessing the advanced capabilities of O-RAN, with a specific emphasis on edge-assisted low-latency AR as a key use case, to address autonomous resource management in the NextG paradigm. The research employs a data-driven approach across multiple time scales, using Bayesian optimization (BO) as a sample-efficient online learning and black-box optimization tool. The research develops versatile techniques and building blocks to optimize the QoE performance, structured around three interconnected thrusts: (i) developing a provably efficient multi-time-scale data-driven BO framework integrated with O-RAN, (ii) achieving collaborative BO for multi-RAN learning and optimization, and (iii) applying the developed BO frameworks to edge-assisted low-latency AR applications. The research establishes the analytical foundations and algorithmic frameworks that will be integrated with open-source full-stack O-RAN implementations. The evaluation process involves simulations based on 3GPP standards in ns-3, as well as collaborations with industry partners including AT&T, Qualcomm, and Nokia Bell Labs. Real- world trace data and production-grade O-RAN platforms will be leveraged for evaluation purposes. The outcomes of this research not only contribute to advancing knowledge in machine-learning-enabled NextG systems design but also address critical needs within the broader machine learning and networking research communities.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.

预计下一代（NextG）无线网络将彻底改变各种应用程序，例如增强现实（AR）等交互式实时应用程序，同时满足用户预期的高质量体验（QOE）要求。为了实现这些目标，NextG网络正在向白框体系结构进行转换，其特征是开放性，智能和关注用户需求。因此，解决NextG范式中的自主资源管理既及时又重要。该项目旨在促进从传统的黑盒网络设计到白盒网络体系结构的过渡，这将大大降低成本并提高其核心性能。该项目的发现将集成到所有参与机构的课程中。此外，该项目致力于通过各自机构的研究机会和外展活动来促进妇女和代表性不足的少数民族（URM）学生的参与。将在OSU.O-RAN举行的年度备受瞩目的研究研讨会上建立机制，以促进URM团体的领导和参与，这是一个专门促进无线电访问网络（RAN）迈向开放建筑的运营商驱动的联盟。这项研究的重点是利用O-Ran的先进功能，特别强调边缘辅助的低延迟AR作为关键用例，以解决NextG范式中的自主资源管理。该研究使用贝叶斯优化（BO）作为样本有效的在线学习和黑盒优化工具采用了多个时间尺度的数据驱动方法。 The research develops versatile techniques and building blocks to optimize the QoE performance, structured around three interconnected thrusts: (i) developing a provably efficient multi-time-scale data-driven BO framework integrated with O-RAN, (ii) achieving collaborative BO for multi-RAN learning and optimization, and (iii) applying the developed BO frameworks to edge-assisted low-latency AR applications.该研究建立了将与开源全栈O-RAN实现集成的分析基础和算法框架。评估过程涉及基于NS-3中3GPP标准的模拟，以及与AT＆T，高通公司和诺基亚·贝尔实验室（Nokia Bell Labs）在内的行业合作伙伴的合作。现实世界跟踪数据和生产级的O-RAN平台将用于评估目的。这项研究的结果不仅有助于促进机器学习的Nextg系统设计的知识，而且还可以满足更广泛的机器学习和网络研究社区中的关键需求。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知识分子优点和更广泛的审查标准来通过评估来获得支持的。