Collaborative Research:SWIFT: Exploiting Application Semantics in Intelligent Cross-Layer Design to Enhance End-to-End Spectrum Efficiency

合作研究：SWIFT：利用智能跨层设计中的应用语义来提高端到端频谱效率

• 批准号: 2128489
• 负责人: Zhi-Li Zhang
• 金额: $ 44.5万
• 依托单位: University of Minnesota-Twin Cities
• 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=2128489&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; Exploiting; Application

The growing demands for network capacity have spurred existing wireless networks to expand into millimeter-wave radio bands with enormous spectrum resources. Theoretically, wireless link capacity grows linearly with the amount of spectrum. Unfortunately, the capacity gain does not straightforwardly translate into improvement of application-layer quality of experience (QoE). This project advocates for the key notion of end-to-end spectrum efficiency and argues that it is imperative to intelligently exploit application semantics to achieve high end-to-end spectrum efficiency. The basic idea is that wireless networks should be made aware of the “utility” of application data when allocating radio resources, while applications should refactor data to better expose and encode application utility and service requirements for end-to-end spectrum efficiency. This project will contribute to a new paradigm for re-architecturing 5G and future wireless networks to support and enable a wide range of innovative future applications, many yet to be imagined, thereby bringing significant benefits to society at large. The outcomes from this research project will be incorporated into the academic curriculum to equip the workforce with the skills needed to develop future wireless networks. The project team will actively recruit, engage, and mentor a diverse group of undergraduate students and budding researchers, with an emphasis on broadening participation by under-represented groups in advanced wireless and computing research.This project advances a vertically integrated, machine-learning-guided, intelligent cross-layer framework to exploit application semantics for end-to-end spectrum efficiency. It aims to re-architect the radio network protocol stack for 5G & beyond networks through fine-grained refactoring of application data in accordance with application semantics and service needs. The proposed framework is designed based on three key principles: 1) exploiting application semantics and data refactoring, so that the radio networks can intelligently allocate the heterogeneous radio resources with different reliability-efficiency properties to match the utility of data; 2) adopting learning as a guiding principle in wireless system design and integrating learning-based methods across the entire network stack, instead of piecewise application; 3) incorporating intelligent real-time decision mechanisms to mitigate the long-tail performance of machine learning methods. This project will spur the broader research community and industry in exploring new directions in enhancing spectrum efficiency from an end-to-end application/service-centric perspective. It will produce open-source hardware, software, and datasets. The project team will engage students at all levels for integrated research and education activities, and will contribute to the Broadening Participation in Computing programs in the PIs' institutions.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.

对网络容量日益增长的需求促使现有无线网络向拥有巨大频谱资源的毫米波无线电频段扩展。理论上，无线链路容量随频谱数量线性增长。不幸的是，容量的增加并不能直接转化为应用层体验质量（QoE）的改善。本项目倡导端到端频谱效率的关键概念，并认为智能利用应用语义是实现高端到端频谱效率的必要条件。基本思想是，在分配无线电资源时，无线网络应该意识到应用程序数据的“效用”，而应用程序应该重构数据，以更好地公开和编码应用程序效用和服务需求，以实现端到端频谱效率。该项目将有助于构建5G和未来无线网络的新范式，以支持和实现许多尚未想象的广泛创新未来应用，从而为整个社会带来巨大利益。该研究项目的成果将被纳入学术课程，以使劳动力具备开发未来无线网络所需的技能。项目团队将积极招募、参与和指导多样化的本科生和崭露头角的研究人员，重点是扩大未被充分代表的群体在先进无线和计算研究中的参与。该项目提出了一个垂直集成、机器学习引导的智能跨层框架，以利用端到端频谱效率的应用语义。它的目标是通过根据应用语义和服务需求对应用数据进行细粒度重构，为5G和其他网络重新构建无线网络协议栈。该框架的设计基于三个关键原则：1)利用应用语义和数据重构，使无线网络能够智能地分配具有不同可靠效率属性的异构无线资源，以匹配数据的效用；2)将学习作为无线系统设计的指导原则，在整个网络堆栈中集成基于学习的方法，而不是分段应用；3)结合智能实时决策机制来缓解机器学习方法的长尾性能。此计划将刺激更广泛的研究界和业界从端到端应用/服务为中心的角度探索提高频谱效率的新方向。它将生产开源硬件、软件和数据集。项目团队将让各级学生参与综合研究和教育活动，并将为扩大pi机构对计算机项目的参与做出贡献。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。

项目成果

Kaala: scalable, end-to-end, IoT system simulator

Kaala：可扩展、端到端的物联网系统模拟器

• DOI: 10.1145/3538393.3544937
• 发表时间: 2022
• 期刊: SIGCOMM workshop on networked sensing systems for a sustainable society
• 作者: Dayalan, Udhaya Kumar;Fezeu, Rostand A.;Salo, Timothy J.;Zhang, Zhi-Li
• 通讯作者: Zhang, Zhi-Li

A Comparative Measurement Study of Commercial 5G mmWave Deployments

• DOI: 10.1109/infocom48880.2022.9796693
• 发表时间: 2022-05
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
• 作者: Arvind Narayanan;M. I. Rochman;A.E.A. Hassan;Bariq S. Firmansyah;V. Sathya;Monisha Ghosh;Feng Qian-Feng-Q

Multi-Modal Vehicle Data Delivery via Commercial 5G Mobile Networks: An Initial Study

通过商用 5G 移动网络进行多模式车辆数据传输：初步研究

• DOI: 10.1109/icdcsw60045.2023.00026
• 发表时间: 2023
• 期刊: 2023 IEEE 43rd International Conference on Distributed Computing Systems Workshops (ICDCSW
• 作者: Carpenter, Jason;Ye, Wei;Qian, Feng;Zhang, Zhi-Li
• 通讯作者: Zhang, Zhi-Li

An In-Depth Measurement Analysis of 5G mmWave PHY Latency and Its Impact on End-to-End Delay

• DOI: 10.1007/978-3-031-28486-1_13
• 发表时间: 2023
• 作者: Rostand A. K. Fezeu;Eman Ramadan;Wei Ye;Benjamin Minneci;Jack Xie;Arvind Narayanan;Ahmad Hassan;Feng Qian;Zhi-Li Zhang;J. Chandrashekar;Myungjin Lee

Vues: Practical Mobile Volumetric Video Streaming Through Multiview Transcoding

Vues：通过多视图转码实现实用的移动体积视频流

• DOI: 10.1145/3495243.3517027
• 发表时间: 2022
• 期刊: ACM MobiCom 2022
• 作者: Liu, Yu;Han, Bo;Qian, Feng;Narayanan, Arvind;Zhang, Zhi-Li
• 通讯作者: Zhang, Zhi-Li