CAREER: Adaptive Communications and Trajectory Design for UAV-assisted Wireless Networks: a Multi-Scale Decision Framework

职业：无人机辅助无线网络的自适应通信和轨迹设计：多尺度决策框架

• 批准号: 2046034
• 负责人: Nicolo Michelusi
• 金额: $ 48.77万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046034&HistoricalAwards=false
• 关键词: CAREER; Adaptive; Communications; Trajectory; Design

The demand for wireless broadband is growing in the United States and across the world. Unmanned aerial vehicles (UAVs) are envisioned as key components of 5G wireless technology and beyond: thanks to their low cost, improved line-of-sight over terrestrial base stations, and controllable mobility, they will enable low-cost wireless broadband access. Nonetheless, UAVs’ integration into wireless networks poses unique challenges on the network and physical layers, due to the intricate coupling between trajectory design and communication resources to be jointly optimized, and uncertain air-to-ground channel propagation conditions. Furthermore, UAVs need to seamlessly operate under sources of randomness and uncertainty typical of wireless networks. This project aims to design techniques to enable real-time physical-layer adaptation of the communication resources, and adaptive trajectory designs to optimize communication performance and energy-efficiency of the system. This research addresses the global industrial and societal need for ubiquitous wireless broadband access by enabling a cost-effective integration of UAVs into wireless networks. This research integrates an educational and outreach program designed to foster research interests and participation of underrepresented students in electrical engineering, through activities created in collaboration with programs at ASU and local high schools.This project develops a novel decision-making framework to address the critical need for adaptation in UAV-assisted wireless networks operating under uncertainty. Adaptive techniques are developed that leverage the high mobility of UAVs to optimize communication metrics such as latency, throughput, outage probability, area spectral efficiency, energy efficiency, by focusing on the interplay between network-level optimization and physical-layer communication, trajectory design, and control. A key novelty is a multi-scale decision framework to achieve scalable design. The framework leverages multiple spatio-temporal scales induced by the coupling between trajectory and channel propagation conditions to centralize slow timescale trajectory decisions and decentralize fast timescale communications decisions. The design aspect leverages unique features of single- and multi-antennas, operating at sub-6GHz or millimeter-wave frequencies, and provides adaptation to uncertain and dynamic channel conditions. The second goal consists of designing adaptive multi-UAV wireless systems, including UAV selection, user association, resource allocation, optimal charging schedules to enable uninterrupted operation, and contention-based access schemes to improve coverage and grant-free access. The research results are tested experimentally on NSF PAWR AERPAW by designing a software-defined-radio implementation. The experimental results are integrated into theoretical models for continuous improvement and testing.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.

美国和世界各地对无线宽带的需求正在增长。无人机(UAV)被设想为5G无线技术及更高级别的关键组件：由于其低成本、地面基站的视线改善和可控的移动性，它们将实现低成本的无线宽带接入。然而，由于弹道设计和要联合优化的通信资源之间的复杂耦合，以及不确定的空对地信道传播条件，无人机集成到无线网络中对网络和物理层构成了独特的挑战。此外，无人机需要在无线网络典型的随机性和不确定性来源下无缝运行。该项目旨在设计能够实现通信资源的实时物理层适配的技术，以及用于优化通信性能和系统能量效率的自适应轨迹设计。这项研究解决了全球工业和社会对无处不在的无线宽带接入的需求，使无人机能够经济高效地集成到无线网络中。这项研究整合了一个教育和推广计划，旨在通过与亚利桑那州立大学和当地高中的计划合作创建的活动，培养未被充分代表的学生对电气工程的研究兴趣和参与。该项目开发了一个新的决策框架，以满足在不确定条件下运行的无人机辅助无线网络中的适应的关键需求。开发了自适应技术，通过关注网络级优化和物理层通信、轨迹设计和控制之间的相互作用，利用无人机的高移动性来优化通信指标，如延迟、吞吐量、中断概率、区域频谱效率和能源效率。一个关键的新颖性是一个多尺度决策框架，以实现可扩展的设计。该框架利用轨迹和信道传播条件之间的耦合产生的多个时空尺度来集中慢时间尺度的轨迹决策和分散快速时间尺度的通信决策。设计方面利用了工作在低于6 GHz或毫米波频率的单天线和多天线的独特功能，并提供了对不确定和动态信道条件的适应。第二个目标是设计自适应多无人机无线系统，包括无人机选择、用户关联、资源分配、支持不间断操作的最优计费计划以及基于竞争的接入方案以提高覆盖和免授权接入。通过设计一个软件无线电实现，在NSF PAWR AERPAW上对研究结果进行了实验验证。实验结果被整合到理论模型中进行持续改进和测试。这一奖项反映了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。