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.

对网络容量不断增长的需求促使现有的无线网络扩展到具有巨大频谱资源的毫米波无线电频段。理论上，无线链路容量随着频谱数量线性增长。不幸的是，容量增益并不能直接转化为应用层体验质量（QoE）的改善。该项目倡导端到端频谱效率的关键概念，并认为必须智能地利用应用语义来实现高的端到端频谱效率。其基本思想是，无线网络在分配无线电资源时应了解应用数据的“效用”，而应用应重构数据以更好地暴露和编码应用效用和服务要求，从而实现端到端频谱效率。该项目将为5G和未来无线网络的重新架构提供新的范例，以支持和实现广泛的创新未来应用，其中许多尚未想象，从而为整个社会带来重大利益。该研究项目的成果将被纳入学术课程，以使员工具备开发未来无线网络所需的技能。该项目团队将积极招募、吸引和指导一批多样化的本科生和初露头角的研究人员，重点是扩大代表性不足的群体在先进无线和计算研究中的参与。该项目推进了一个垂直集成、机器学习引导的智能跨层框架，以利用应用语义实现端到端频谱效率。它旨在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，重新架构5G超越网络的无线电网络协议栈。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性-效率属性的异构无线资源，以匹配数据的效用; 2）在无线系统设计中采用学习作为指导原则，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; 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。该项目将激励更广泛的研究社区和行业从端到端应用/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。 该项目团队将让各级学生参与综合研究和教育活动，并将为PI机构中的“扩大参与计算机计划”做出贡献。该奖项反映了NSF的法定使命，并通过使用基金会的评估被认为值得支持智力优点和更广泛的影响审查标准。

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