SpecEES: Collaborative Research: DroTerNet: Coexistence between Drone and Terrestrial Wireless Networks

SpecEES：协作研究：DroTerNet：无人机与地面无线网络的共存

• 批准号: 1923774
• 负责人: Fred Daneshgaran
• 金额: $ 10.74万
• 依托单位: California State L A University Auxiliary Services Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923774&HistoricalAwards=false
• 关键词: SpecEES; Collaborative; Research; DroTerNet; Coexistence

There is tremendous recent interest in drones with applications ranging from public safety, first responders, surveillance, to package delivery. Drones are also being considered as flying wireless nodes to augment the capabilities of current terrestrial communication networks. Irrespective of the application, drones need radio frequency (RF) spectrum to communicate with their ground control stations as well as with other drones and terrestrial nodes. Since transmissions from higher altitude have the potential of interfering with other wireless services over a large area, it is currently being debated whether and under what rules should drones share spectrum with existing networks or whether it is better to operate them over specifically licensed frequencies. In order to answer such important and timely questions, this project develops a new cross-disciplinary approach to the design and analysis of coexisting drone and terrestrial networks (DroTerNets) by blending ideas from multiple disciplines, such as spectrum sharing, communication theory, propagation science, test-bed development, machine learning, and stochastic network modeling. This research will inform both industry and government on spectrum usage by providing a scientific basis for the high-stakes ruling on spectrum for drones. Further broader impacts will be through student training and wide dissemination of results. The overarching goal of this research is to develop a holistic new approach to the spectral and energy efficiency analysis of DroTerNets, yielding the following key innovations: (i) A new learning framework based on the idea of determinantal point processes (DPPs) will be developed to facilitate both simulation-based and analytical characterization of the locations of simultaneously active nodes in a given frequency band for a variety of coexistence schemes, (ii) Drawing on multi-label classification in machine learning, a novel deep DPP-based channel assignment algorithm will be developed by utilizing the structure of DPP kernels to limit the search space, (iii) Non-linear receiver characteristics will be included in the learning framework to both quantify their effect on the energy and spectral efficiency of DroTerNets and to develop novel receiver-aware channel assignment schemes, (iv) Mobility constraints and characteristics of drones that result from the opportunistic access of the channel will be characterized and incorporated in the analysis, (v) Measurements and models of air-to-ground (A2G) channels in a variety of environments with particular emphasis on directional characteristics that determine the effectiveness of multi-antenna receivers will be obtained, and (vi) Experimental investigation and modeling of the correlation between terrestrial and A2G links will be performed to provide a solid foundation for coexistence margins.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.

最近，人们对无人机的应用产生了极大的兴趣，从公共安全、急救人员、监视到包裹递送。无人机也被认为是飞行无线节点，以增强当前地面通信网络的能力。无论何种应用，无人机都需要射频（RF）频谱与地面控制站以及其他无人机和地面节点进行通信。由于高空传输有可能干扰大面积的其他无线服务，目前正在讨论无人机是否以及在什么规则下应该与现有网络共享频谱，还是在特定许可的频率上运行更好。为了回答这些重要而及时的问题，该项目通过融合多学科的思想，如频谱共享、通信理论、传播科学、试验台开发、机器学习和随机网络建模，开发了一种新的跨学科方法来设计和分析共存的无人机和地面网络（DroTerNets）。这项研究将为无人机频谱的高风险裁决提供科学依据，从而为行业和政府提供频谱使用方面的信息。更广泛的影响将通过培训学生和广泛传播成果。本研究的总体目标是开发一种全面的新方法来分析DroTerNets的频谱和能源效率，产生以下关键创新：(i)将开发一个基于确定性点过程（DPP）思想的新学习框架，以促进在给定频带内各种共存方案中同时活动节点的位置的基于模拟和分析表征；（ii）借鉴机器学习中的多标签分类，将利用DPP核的结构来限制搜索空间，开发一种新的基于深度DPP的信道分配算法；（iii）非线性接收器特性将包括在学习框架中，以量化它们对DroTerNets的能量和频谱效率的影响，并开发新的接收器感知的信道分配方案。（iv）由于通道的机会访问而导致的无人机的机动性限制和特性将被表征并纳入分析。(v)将获得各种环境中空对地信道的测量和模型，特别强调确定多天线接收器有效性的方向特性；（vi）将对地面和空对地链路之间的相关性进行实验调查和建模，为共存边际提供坚实的基础。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。