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

移动数据流量的爆炸性增长在一定程度上是对近年来移动接入服务激增的回应。然而，由于网络容量和覆盖区域有限，并不是所有的移动用户都能够享受到稳定可靠的宽带连接。因此，在下一代(NextG)移动宽带网络中，有必要整合地面和非地面网络，通过提供无缝的无线覆盖和支持不同的服务需求来实现无线接入的民主化。为了实现这一目标，该项目将开展必要的基础研究，以整合和运行地面和非地面网络，称为地面和空中综合网络(GAINS)。该研究项目在机器学习和无线网络的界面上是高度跨学科的，为研究生和本科生提供在这两个社区中茁壮成长所需的技能，以及在学术界或行业中架起桥梁。软件和硬件试验台将为学术界、产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开发基于波形设计、实时机器学习、资源调度、分布式计算和学习的弹性和智能地面和空中综合网络(GAINS)的基本使能通信和计算技术。该研究方案通过在延迟-多普勒域而不是时频域设计信号和控制网络，使得机器学习算法可以看到传播环境的稀疏表示。研究计划被简化为四个相互关联的研究推力：1)支持机器学习的波形设计；2)支持多智能体强化学习的地面网络弹性调度；3)收益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机器学习算法是通信高效和异构性感知的，将以下一代(NextG)网络的速度为信息处理量身定做。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.