IMR: MT: Fine-Grained Telemetry for Next-Generation Cellular Access Networks (NG-Scope)

IMR：MT：下一代蜂窝接入网络的细粒度遥测（NG-Scope）

• 批准号: 2223556
• 负责人: Kyle Jamieson
• 金额: $ 60万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223556&HistoricalAwards=false
• 关键词: IMR; MT; Fine; Grained; Telemetry

The Next Generation Cellular Wireless Radio Access Network (RAN) is emerging as the dominant data network for Internet access, supporting capacity-enhanced mobile broadband, ultra low latency, and high connectivity machine-to-machine scenarios alike. Insightful performance measurement of such applications depends on one or more of delay, jitter, and network capacity measurement data (telemetry) at millisecond-level timescales. This necessitates a clean-slate approach that marries high resolution physical-layer telemetry with end-to-end network- and transport-layer telemetry. This proposal develops NG-Scope, a measurement tool that provides a network telemetry stream to higher software layers. NG-Scope's telemetry succinctly summarizes wireless capacity, RAN-added latency, and network management data. Rather than relying on crowd-sourced data from deployment on most or all user equipment, NG-Scope provides visibility into all users' telemetry data in a given cell. NG-Scope accounts for and leverages the latest 5G innovations, but abstracts these details behind a proposed standardized API (Application Program Interface) that measurement tools, applications, and transport layer protocols access.NG-Scope gives network measurement researchers visibility into the RAN at fidelity levels and user population scales not previously possible. Its proposed RAN-specific processing techniques bridge the gap between physical- and transport-layer telemetry data, enabling inference of the sources of network phenomena not previously observable, such as the ability to differentiate between wireless channel conditions and RAN versus core network congestion as the cause of packet latency and jitter. NG-Scope does not require the incentivization or cooperation of any service provider, enabling unfettered access to RAN telemetry and thus broadly accelerating innovation in network measurement and design.NG-Scope will benefit basic longitudinal internet measurement research, network operators' network monitoring tasks, and internet application architects' design and development efforts. It will be developed in an open-source model so that internet measurement researchers can deploy the tool locally. Results of data measurement campaigns will be made publicly available, to facilitate trace-driven emulation based experiments of other researchers and network protocol designers. Researchers will be able to remotely prototype and evaluate their own applications and network protocols in a virtual programming environment with remote access. The project will engage with under-represented groups through summer research opportunities, REU sites, and the undergraduate junior/senior thesis. Project website: ngscope.cs.princeton.eduThis 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.

下一代蜂窝无线电接入网络（RAN）正在成为用于互联网接入的主导数据网络，支持容量增强的移动的宽带、超低延迟和高连接性机器对机器场景等。 这些应用的深入性能测量取决于毫秒级时间尺度的延迟、抖动和网络容量测量数据（遥测）中的一个或多个。 这就需要一种全新的方法，将高分辨率物理层遥测与端到端网络和传输层遥测结合起来。 该提案开发了NG-Scope，这是一种测量工具，可向更高的软件层提供网络遥测流。 NG-Scope的遥测技术简洁地总结了无线容量、RAN增加的延迟和网络管理数据。 NG-Scope不依赖于大多数或所有用户设备上部署的众包数据，而是提供对给定小区中所有用户遥测数据的可见性。 NG-Scope考虑并利用了最新的5G创新，但将这些细节抽象为拟议的标准化API（应用程序接口），测量工具，应用程序和传输层协议访问。NG-Scope使网络测量研究人员能够以保真度水平和用户数量规模了解RAN，这在以前是不可能的。 其提出的RAN特定处理技术弥合了物理层和传输层遥测数据之间的差距，从而能够推断出以前无法观察到的网络现象的来源，例如区分无线信道条件和RAN与核心网络拥塞的能力，这些拥塞是数据包延迟和抖动的原因。 NG-Scope不需要任何服务提供商的激励或合作，能够不受限制地访问RAN遥测，从而广泛加速网络测量和设计的创新。NG-Scope将有利于基本的纵向互联网测量研究，网络运营商的网络监控任务，以及互联网应用架构师的设计和开发工作。 它将以开源模式开发，以便互联网测量研究人员可以在本地部署该工具。 数据测量活动的结果将公开提供，以促进其他研究人员和网络协议设计人员基于跟踪驱动仿真的实验。 研究人员将能够在具有远程访问的虚拟编程环境中远程原型设计和评估自己的应用程序和网络协议。 该项目将通过夏季研究机会，REU网站和本科生初级/高级论文参与代表性不足的群体。 项目网站：该奖项反映了NSF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。

项目成果

Cellular-Assisted, Deep Learning Based COVID-19 Contact Tracing

• DOI: 10.1145/3550332
• 发表时间: 2022-09
• 期刊: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
• 作者: Fan Yi;Yaxiong Xie;Kyle Jamieson