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: 2128588
• 负责人: Xinyu Zhang
• 金额: $ 30.5万
• 依托单位: University of California-San Diego
• 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=2128588&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.

对网络容量的需求不断增长，促使现有的无线网络扩展到具有增强频谱资源的毫米波无线电频段。理论上的无线链路容量随频谱的量线性增长。不幸的是，容量增益并不能直接转化为应用程序层的经验质量（QOE）的改进。该项目倡导端到端频谱效率的关键概念，并认为必须智能利用应用程序语义来实现高端到端频谱效率。基本思想是，在分配无线电资源时，应使无线网络意识到应用程序数据的“实用性”，而应用程序应重构数据以更好地揭示和编码端到端频谱效率的应用程序实用程序和服务要求。该项目将有助于重新构建5G和未来无线网络的新范式，以支持和实现广泛的创新未来应用，许多尚未想象的应用程序，从而为整个社会带来了巨大的好处。该研究项目的结果将纳入学术课程中，以使劳动力配备开发未来无线网络所需的技能。项目团队将积极招募，参与和指导一群群众的本科生和崭露头角的研究人员，重点是扩大人为不足的小组参与高级无线和计算研究。该项目逐步综合，机器学习的指导，智能的跨层框架，以利用Ensploit Spectifity for Endermum for Endermum neptrum endermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermendermend spectimend spectrum endermend eNdermendermendermend eNdermende n。它的目的是根据应用语义和服务需求对应用程序数据进行细化的重构，重新构建5G及更高网络的无线网络协议堆栈。提出的框架是基于三个关键原则设计的：1）利用应用程序语义和数据重构，以便无线电网络可以智能地分配具有不同可靠性效率属性的异质无线电资源以匹配数据的实用性； 2）将学习作为无线系统设计中的指导原则，并在整个网络堆栈中集成基于学习的方法，而不是分段应用程序； 3）结合智能实时决策机制，以减轻机器学习方法的长尾性能。该项目将刺激更广泛的研究社区和行业，从端到端应用程序/以中心/服务的角度来探索新的方向，以提高频谱效率。它将生产开源硬件，软件和数据集。该项目团队将与各级的学生一起参与综合研究和教育活动，并将有助于扩大PIS机构中计算计划的参与。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响标准来评估NSF的法定任务。

项目成果

SaTCP: Link-Layer Informed TCP Adaptation for Highly Dynamic LEO Satellite Networks

SaTCP：高动态 LEO 卫星网络的链路层通知 TCP 适配

• DOI: 10.1109/infocom53939.2023.10228914
• 发表时间: 2023
• 期刊: IEEE
• 作者: Cao, Xuyang;Zhang, Xinyu
• 通讯作者: Zhang, Xinyu