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

最近，人们对无人机的兴趣极大，其应用范围从公共安全、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。无论应用如何，无人机都需要射频（RF）频谱来与其地面控制站以及其他无人机和地面节点进行通信。由于来自更高海拔的传输有可能干扰大范围内的其他无线服务，目前正在讨论无人机是否应该以及在什么规则下与现有网络共享频谱，或者是否更好地在特定许可的频率上运行它们。为了回答这些重要而及时的问题，该项目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），通过融合来自多个学科的想法，如频谱共享，通信理论，传播科学，测试台开发，机器学习和随机网络建模。这项研究将通过为无人机频谱的高风险裁决提供科学依据，为行业和政府提供有关频谱使用的信息。将通过学生培训和广泛传播成果，进一步扩大影响。 这项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）将根据决定点过程的概念开发一个新的学习框架，以促进模拟-基于和分析表征对于多种共存方案在给定频带中同时活动的节点的位置，（ii）借鉴机器学习中的多标签分类，将通过利用DPP内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被纳入学习框架，以量化其对DroTerNet的能量和频谱效率的影响，并开发新型接收器，感知信道分配方案，（iv）由信道的机会主义接入引起的无人机的移动性约束和特性将被表征并并入分析中，㈤各种环境中空对地信道的测量和模型，特别强调确定多用途通信系统有效性的方向特性。该奖项反映了NSF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。

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.