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

基本信息

批准号：
2129015
负责人：
Nicolo Michelusi
金额：
$ 48.77万
依托单位：
Arizona State 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 2026-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129015&HistoricalAwards=false
关键词：
CAREER Adaptive Communications Trajectory Design

项目摘要

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不断增长，无人机 (UAV) 被认为是 5G 无线技术及其他技术的关键组成部分：由于其成本低廉、视距优于地面基站。和可控的移动性，它们将实现低成本的无线宽带接入。然而，由于轨迹设计和需要共同优化的通信资源之间的复杂耦合，无人机与无线网络的集成对网络和物理层提出了独特的挑战。此外，无人机需要在无线网络典型的随机性和不确定性源下无缝运行，该项目旨在设计技术以实现通信资源的实时物理层适应。自适应轨迹设计可优化系统的通信性能和能源效率。这项研究通过将无人机经济有效地集成到无线网络中，满足了全球工业和社会对无处不在的无线宽带接入的需求。旨在培养通过与亚利桑那州立大学和当地高中的项目合作开展的活动，激发了电气工程领域代表性不足的学生的研究兴趣和参与。该项目开发了一种新颖的决策框架，以满足在不确定性下运行的无人机辅助无线网络适应的关键需求开发的自适应技术利用无人机的高机动性来优化通信指标，例如延迟、吞吐量、中断概率、区域频谱效率、能源效率，重点关注网络级优化和物理层通信、轨迹设计之间的相互作用，和一个关键的新颖之处是实现可扩展设计的多尺度决策框架，该框架利用轨迹和信道传播条件之间的耦合引起的多个时空尺度来集中慢时间尺度轨迹决策和分散快速时间尺度通信决策。第二个目标是利用单天线和多天线的独特功能，在 6GHz 以下或毫米波频率下运行，并适应不确定和动态的信道条件。包括无人机选择、用户关联、资源分配、实现不间断运行的最佳充电计划，以及基于竞争的接入方案以提高覆盖范围和无授权接入。实验结果被整合到理论模型中，以进行持续改进和测试。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。