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: 2412393
• 负责人: Ran Zhang
• 金额: $ 19.28万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2412393&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源于《2021 年美国救援计划法案》（公法 117-2）。无人驾驶飞行器或无人机在下一代无线通信领域已展现出令人印象深刻的潜力。与地面蜂窝基站相比，配备无线收发器的无人机可以充当移动基站，并在提供高度按需服务方面脱颖而出，具有灵活的3D移动性、更好的无线连接和更高的视距链路机会，以及几乎无需基础设施的网络建设，大大降低了部署成本。尽管前景广阔，基于无人机的通信网络仍然面临着根本性的监管挑战：i）由于无人机具有高度移动性，动态变化的网络拓扑可能使集中控制单元收集完整的网络信息并做出集体决策变得不切实际； ii) 无人机运行的环境可能会动态变化或未经探索，而无需先验了解环境建模，这使得传统的优化或基于规则的方法几乎不适用； iii）当服务无人机的阵容动态变化时如何优化调节无人机网络的研究仍处于萌芽阶段，但这将是现实实施中的常见事件。为此，该提案旨在破解上述问题的核心，并为分布式网络监管解决方案开发一个有效的框架，该框架无需环境模型且能够很好地适应动态无人机阵容。研究成果预计将为动态网络设置下空中接入通信网络的分布式、可扩展和人工智能驱动的管理提供宝贵的启发和基准。此类网络将成为超 5G 移动通信更大范围的天地一体化网络的关键组成部分。该项目的成功将有可能有助于美国在下一代移动电信领域的全球领导地位和能力，并通过更加集成和按需的通信基础设施提升国家通信福利。在该项目中，多智能体强化学习将应用于建立分布式、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做出顺序决策的强大能力。在所开发的框架下，该项目旨在研究无人机通信网络如何以分布式且协调的方式响应地处理并进一步主动控制无人机阵容的动态变化。具体来说，响应策略将首先针对通用无人机通信网络进行设计。这些策略将在无人机阵容动态变化时共同优化无人机的无线电资源管理和轨迹设计。将通过不同级别的无人机间信息交换来研究学习算法设计。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。更进一步，将为太阳能自给自足的无人机通信网络导出主动控制策略，通过预先制定太阳能充电计划来主动控制无人机的退出和加入。该策略将通过结合傅里叶分析、长短期记忆和高斯过程回归进行分布预测来考虑动态用户空间和流量分布，并在学习的同时进行预测，从而显着降低强化学习的复杂性。个体无人机之间的混合合作竞争关系将通过利用纳什 Q 学习和相关 Q 学习来处理。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。该项目将通过填补控制策略设计缺失的空白，推进无人机通信网络自主调控的研究，以响应地处理和主动控制无人机阵容的变化。此外，将博弈论引入分布式框架使得个体无人机多样化自主的研究更加现实。原型设计计划将补充对现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点和更广泛的影响审查标准进行评估，被认为值得支持。