Collaborative Research: SWIFT: Decentralized Intelligent Spectrum Sharing in UAV Networks (DISH-uNET) via Hardware-software Co-design

合作研究：SWIFT：通过软硬件协同设计实现无人机网络中的去中心化智能频谱共享 (DISH-uNET)

• 批准号: 2229562
• 负责人: Mingyue Ji
• 金额: $ 56.05万
• 依托单位: University of Utah
• 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: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229562&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; Decentralized; Intelligent

Intelligent unmanned aerial vehicles (UAVs, or “drones”) are attracting the interest of the networking community as a “tool” to provide new capabilities, to extend the infrastructure of wireless networks and to make it more flexible and resilient. UAV-aided wireless networks will enable present and future Internet of Things (IoT) and 5G applications and be a driver for new military and civilian applications spanning battlefield inspection, border control and aerial surveillance, precision agriculture, environmental monitoring, transportation and delivery of goods. Currently, despite the great potential of UAV networks, the design of such networks faces great challenges due to the high mobility of UAVs, the limited power constraint of UAVs, and the non-stationary environment. In this project, we will develop novel approaches for decentralized intelligent spectrum sharing in mmWave UAV networks (DISH-uNET). This project will have a significant engineering and societal impact and substantially advance the state-of-the-art in the design of intelligent UAV networks with strong resiliency attributes. The proposed DISH-uNET achieves high efficiency and resilience based on hardware-software co-design through (1) domain-specific energy-efficient systolic accelerators, (2) novel learning-based transceiver design for high mobility UAVs, (3) new decentralized spectrum sharing multiple access control (MAC) for efficient spectrum management and utility optimization, (4) fast adaptation of mobility resilient mmWave beam learning for UAV networks. This project has four main thrusts. (1) Energy-efficient systolic accelerator for simultaneous real-time signal processing and machine learning. (2) Transceiver design for high mobility UAV Communication. (3) Bridging Lyapunov optimization framework, game theory, and reinforcement learning in decentralized spectrum sharing. (4) Mobility-resilient mmWave beam learning. The research team has the unique access to a large-scale UAV testbed, equipped with the state-of-the-art mmWave transceivers. This testbed will be used for extensive evaluations of the proposed DISH-uNET system.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.

智能无人机（UAV或“无人机”）作为提供新功能、扩展无线网络基础设施并使其更加灵活和有弹性的“工具”正在吸引网络社区的兴趣。无人机辅助的无线网络将支持当前和未来的物联网（IoT）和5G应用，并成为新的军事和民用应用的驱动力，包括战场检查，边境控制和空中监视，精准农业，环境监测，运输和货物交付。目前，尽管无人机网络具有巨大的潜力，但由于无人机的高机动性、无人机的有限功率约束以及非静止环境，此类网络的设计面临着巨大的挑战。在这个项目中，我们将开发毫米波无人机网络（DISH-uNET）中分散智能频谱共享的新方法。该项目将产生重大的工程和社会影响，并大大推进具有强大弹性属性的智能无人机网络设计的最新技术。所提出的DISH-uNET基于硬件-软件协同设计实现了高效率和弹性，通过（1）特定于域的节能脉动加速器，（2）用于高移动性无人机的新型基于学习的收发器设计，（3）用于有效频谱管理和效用优化的新型分散频谱共享多址接入控制（MAC），（4）用于UAV网络的移动性弹性毫米波波束学习的快速自适应。该项目有四个主要目标。(1)用于同步实时信号处理和机器学习的节能收缩加速器。(2)高机动性无人机通信收发信机设计。(3)在分散频谱共享中桥接李雅普诺夫优化框架，博弈论和强化学习。(4)移动弹性毫米波波束学习。该研究团队拥有使用大型无人机试验台的独特权限，该试验台配备了最先进的毫米波收发器。该测试平台将用于对拟议的DISH-uNET系统进行广泛的评估。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Distributed Power Allocation for 6-GHz Unlicensed Spectrum Sharing via Multi-agent Deep Reinforcement Learning

• DOI: 10.1109/icit58465.2023.10143125
• 发表时间: 2023-04
• 期刊: 2023 IEEE International Conference on Industrial Technology (ICIT)
• 作者: Xiang Zhang;Arupjyoti Bhuyan;S. Kasera;Mingyue Ji