ERI: Distributed Learning in Regulation of UAV Communication Networks with Dynamic UAV Lineup

ERI：动态无人机阵容的无人机通信网络调节中的分布式学习

• 批准号: 2138871
• 负责人: Ran Zhang
• 金额: $ 19.28万
• 依托单位: Miami 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: 2022-07-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138871&HistoricalAwards=false
• 关键词: ERI; Distributed; Learning; Regulation; UAV

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Unmanned aerial vehicles, or drones, have been demonstrating impressive potentials in next generation wireless communications. Compared to the terrestrial cellular base stations, drones equipped with wireless transceivers can serve as mobile base stations, and stand out in providing highly on-demand services with flexible 3D mobility, better wireless connectivity with higher chance of Line-of-Sight links, and much lower deployment cost with almost infrastructure-free network construction. Promising as it is, drone based communication networks still face fundamental regulation challenges: i) as drones are highly mobile, the dynamically changing network topology may make it unpractical for a centralized control unit to collect complete network information and make collective decisions; ii) the environment in which drones operate may be dynamically changing or unexplored without a priori knowledge of the environment modeling, making the conventional optimization or rule-based methods hardly applicable; iii) the research is still embryonic on how to optimally regulate the drone network when the lineup of the serving drones dynamically change, which, however, will be a common event in realistic implementation. To this end, this proposal is aimed to crack the nut of the above identified issues, and develop an effective framework for distributed network regulation solutions that are environment-model-free and well adaptive to the dynamic drone lineup. The research outcomes are expected to provide valuable inspirations and benchmarking to the distributed, scalable, and artificial-intelligence powered management of aerial access communication networks under a dynamic network setup. Such networks will be embraced as a key component in the larger-scope Space-Air-Ground Integrated Networks for the beyond-5G mobile telecommunications. The success of the project will potentially contribute to the leadership and competence of the United States worldwide in future generation mobile telecommunications as well as elevating national communication welfare with more integrated and on-demand communication infrastructure.In this project, multi-agent reinforcement learning will be applied to establish a distributed and model-free network regulation framework. The framework will feature strong capability in making sequential decisions in complex time-varying environments. Under the developed framework, the project aims to investigate how the drone communication networks should responsively handle and further proactively control the dynamic change of the drone lineup in a distributed yet coordinated manner. Specifically, responsive strategies will be first designed for a general drone communication network. The strategies will jointly optimize the radio resource management and trajectory design for the drones when the drone lineup change dynamically. The learning algorithm design will be investigated with different levels of inter-drone information exchange. The learning exploration will be promoted by adopting the structure of asynchronous parallel computing. The network will be prototyped leveraging on programmable drone products and simple-yet-effective communication protocols. To move one step further, proactive control strategies will be derived for the solar-powered self-sustainable drone communication network, which proactively control the quit and join-in of the drones by pre-shaping their solar-charging plan. The strategies will consider dynamic user spatial and traffic distributions by combining Fourier analysis, Long-Short Term Memory and Gaussian process regression for distribution prediction, and enabling predicting while learning to significantly reduce the reinforcement learning complexity. The hybrid cooperative-compete relationship among individual drones will be handled by exploiting Nash Q learning and correlated Q learning. The anxiety on the high-dimension state-action space in learning will be relieved by adopting problem decomposition techniques. The proposed project will advance the research on autonomous regulation of drone communication networks by filling the gaps of missing control strategy design to responsively handle and proactively control the drone lineup change. In addition, the introduction of game theory into the distributed framework makes the research more realistic with diversified autonomy in individual drones. The prototyping plan will complement the simulation evaluation on the time complexity and communication overhead/latency in the real-world implementation.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.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。无人机在下一代无线通信领域显示出令人印象深刻的潜力。与地面蜂窝基站相比，配备无线收发信机的无人机可以作为移动基站，并以灵活的3D移动性、更好的无线连接和更高的视距链接机会提供高度按需服务，以及几乎无需基础设施的网络建设，部署成本低得多。尽管无人机前景看好，但基于无人机的通信网络仍然面临着基本的监管挑战：i)由于无人机的高度移动性，动态变化的网络拓扑可能会使中央控制单元收集完整的网络信息和做出集体决策变得不切实际；ii)在没有环境建模的先验知识的情况下，无人机运行的环境可能是动态变化的，或者是未知的，使得传统的优化或基于规则的方法很难适用；iii)当服务无人机的阵容动态变化时，如何优化无人机网络的研究仍处于萌芽阶段，然而，这将是现实实施中的常见事件。为此，本建议旨在破解上述问题的坚果，并为分布式网络监管解决方案开发一个有效的框架，该解决方案与环境无关，并能很好地适应动态的无人机阵容。研究成果有望为动态网络架构下空中接入通信网络的分布式、可扩展和人工智能供电管理提供有价值的启发和基准。此类网络将被视为更大范围的空空地综合网络的关键组成部分，用于Beyond-5G移动通信。该项目的成功将有助于美国在全球下一代移动通信领域的领导地位和能力，以及通过更多集成和按需通信基础设施来提高国家通信福利。在该项目中，将应用多智能体强化学习来建立一个分布式的、无模型的网络监管框架。该框架将具有在复杂的时变环境中进行顺序决策的强大能力。在制定的框架下，该项目旨在调查无人机通信网络应如何以分布式但协调的方式应对和进一步主动控制无人机阵容的动态变化。具体地说，将首先为一般无人机通信网络设计应对战略。当无人机阵容动态变化时，这些策略将共同优化无人机的无线电资源管理和轨迹设计。学习算法的设计将与不同级别的无人机之间的信息交换进行研究。采用异步并行计算的结构，将促进学习探索。该网络将利用可编程无人机产品和简单而有效的通信协议进行原型设计。为了更进一步，将为太阳能供电的自我可持续的无人机通信网络推导出主动控制策略，该网络通过预先形成无人机的太阳能充电计划来主动控制无人机的退出和加入。该策略通过结合傅立叶分析、长短期记忆和高斯过程回归进行分布预测，考虑动态的用户空间和流量分布，并支持边预测边学习，显著降低了强化学习的复杂度。利用纳什Q学习和相关Q学习来处理单个无人机之间的混合合作竞争关系。采用问题分解技术可以缓解学习中对高维状态-动作空间的焦虑。该项目将推动无人机通信网络自主监管的研究，填补缺乏控制策略设计的空白，以响应和主动控制无人机阵容的变化。此外，将博弈论引入到分布式框架中，使得研究更具有现实性，个体无人机具有多样化的自治性。原型计划将补充对真实世界实施中的时间复杂性和通信开销/延迟的模拟评估。该奖项反映了NSF的法定使命，并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。