RINGS: Learning-Enabled Ground and Air Integrated Networks (GAINs)

RINGS：支持学习的地面和空中综合网络 (GAIN)

• 批准号: 2148212
• 负责人: Lingjia Liu
• 金额: $ 79.4万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148212&HistoricalAwards=false
• 关键词: RINGS; Learning; Enabled; Ground; Air

The explosive growth of mobile data traffic is in part a response to the proliferation of mobile access services in recent years. However, not all mobile users are able to enjoy stable and reliable broadband connections due to limited network capacities and limited coverage areas. Therefore, in next generation (NextG) mobile broadband networks it is necessary to integrate terrestrial and non-terrestrial networks to democratize wireless access, by providing seamless wireless coverage and supporting heterogeneous service requirements. To meet this goal, this project will develop the fundamental research necessary to integrate and operate terrestrial and non-terrestrial networks, termed Ground and Air Integrated Networks (GAINs). The research project is highly interdisciplinary at the interface of machine learning and wireless networks, providing graduate and undergraduate students with the skills needed to thrive in either community, as well as to bridge them either in academia or in industry. Software and hardware testbeds will provide proof of concept demonstrations for academic, industry and government partners. The overarching objective of this research program is to develop fundamental enabling communication and computing technologies for resilient and intelligent Ground and Air Integrated Networks (GAINs) based on waveform design, real-time machine learning, resource scheduling, distributed computing and learning. This research program makes the sparse representation of the propagation environment visible to machine learning algorithms by designing signals and controlling networks in the delay-Doppler domain, rather than the time-frequency domain. The research program is streamlined into four interconnected research thrusts: 1) Waveform design to enable machine learning; 2) multi-agent reinforcement learning-enabled resilient scheduling for terrestrial networks; 3) distributed and resilient computing in GAINs; and 4) proof-of-concept development and system evaluation. A new suite of distributed and resilient machine learning algorithms that are communication-efficient and heterogeneity-aware will be tailored to information processing in GAINs at the speed of the next generation (NextG) networks.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.

移动的数据业务的爆炸式增长部分地是对近年来移动的接入服务的激增的响应。然而，由于有限的网络容量和有限的覆盖区域，并非所有移动的用户都能够享受稳定可靠的宽带连接。因此，在下一代（NextG）移动的宽带网络中，有必要通过提供无缝无线覆盖和支持异构服务需求来集成地面和非地面网络以使无线接入大众化。为了实现这一目标，该项目将开展必要的基础研究，以整合和运营地面和非地面网络，称为地面和空中综合网络（GAIN）。该研究项目在机器学习和无线网络的界面上具有高度的跨学科性，为研究生和本科生提供在两个社区中蓬勃发展所需的技能，并将其与学术界或工业界联系起来。软件和硬件测试平台将为学术界、工业界和政府合作伙伴提供概念验证演示。该研究计划的总体目标是基于波形设计，实时机器学习，资源调度，分布式计算和学习，为弹性和智能的地面和空中集成网络（GAIN）开发基本的通信和计算技术。该研究计划通过在延迟-多普勒域而不是时频域中设计信号和控制网络，使传播环境的稀疏表示对机器学习算法可见。该研究计划被简化为四个相互关联的研究重点：1）波形设计，以实现机器学习; 2）多智能体强化学习支持的陆地网络弹性调度; 3）GAIN中的分布式和弹性计算;以及4）概念验证开发和系统评估。一套新的分布式和弹性的机器学习算法，是通信效率和异构性意识将被定制的信息处理在GAIN在下一代（NextG）网络的速度。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression

• 发表时间: 2022-01
• 期刊: ArXiv
• 作者: Haoyu Zhao;Boyue Li;Zhize Li;Peter Richt'arik;Yuejie Chi

Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization

• DOI: 10.1109/cdc51059.2022.9993175
• 发表时间: 2022-04
• 期刊: 2022 IEEE 61st Conference on Decision and Control (CDC)
• 作者: Shicong Cen;Fan Chen;Yuejie Chi

SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression

• DOI: 10.48550/arxiv.2206.09888
• 发表时间: 2022-06
• 期刊: ArXiv
• 作者: Zhize Li;Haoyu Zhao;Boyue Li;Yuejie Chi

Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression

• DOI: 10.48550/arxiv.2310.19059
• 发表时间: 2023-10
• 作者: Sijin Chen;Zhize Li;Yuejie Chi

Asynchronous Gradient Play in Zero-Sum Multi-agent Games

• DOI: 10.48550/arxiv.2211.08980
• 发表时间: 2022-11
• 期刊: ArXiv
• 作者: Ruicheng Ao;Shicong Cen;Yuejie Chi