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

下一代（NextG）无线网络预计将彻底改变各种应用，如增强现实（AR）等交互式实时应用，同时满足用户期望的高体验质量（QoE）要求。为了实现这些目标，NextG网络正在向白盒架构转型，其特点是开放性、智能化和关注用户需求。因此，在NextG范例中处理自治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向白盒网络架构的过渡，这将显著降低成本并提高其核心的QoE性能。该项目的研究结果将纳入所有参与机构的课程。此外，该项目致力于通过在各自机构开展研究机会和外联活动，促进妇女和代表性不足的少数民族学生的参与。将建立机制，促进URM各小组在俄勒冈州立大学举行的备受瞩目的年度研究研讨会上的领导和参与。O-RAN是一个由运营商驱动的联盟，致力于将无线接入网络（RAN）推向开放架构。本研究的重点是利用O-RAN的先进功能，特别强调边缘辅助低延迟AR作为关键用例，以解决NextG范式中的自主资源管理问题。该研究采用跨时间尺度的数据驱动方法，使用贝叶斯优化（BO）作为样本高效的在线学习和黑盒优化工具。该研究开发了多种技术和构建块来优化QoE性能，围绕三个相互关联的重点：(i)开发一个可证明有效的多时间尺度数据驱动的BO框架，与O-RAN集成，（ii）实现多ran学习和优化的协作BO，以及（iii）将开发的BO框架应用于边缘辅助低延迟AR应用。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。评估过程包括在ns-3中基于3GPP标准的模拟，以及与包括at&t、高通和诺基亚贝尔实验室在内的行业合作伙伴的合作。真实世界的跟踪数据和生产级O-RAN平台将被用于评估目的。这项研究的结果不仅有助于推进机器学习支持的NextG系统设计的知识，而且还解决了更广泛的机器学习和网络研究社区的关键需求。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.