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: 1923601
• 负责人: Andreas Molisch
• 金额: $ 26.76万
• 依托单位: University of Southern California
• 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 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923601&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.

最近人们对无人机产生了极大的兴趣，其应用范围包括公共安全、急救人员、监视和包裹递送。无人机也被视为飞行无线节点，以增强当前地面通信网络的功能。无论何种应用，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。由于来自较高高度的传输可能会干扰大范围内的其他无线服务，因此目前正在争论无人机是否以及在什么规则下应与现有网络共享频谱，或者是否最好在专门许可的频率上运行它们。为了回答这些重要而及时的问题，该项目通过融合频谱共享、通信理论、传播科学等多个学科的思想，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） 、测试台开发、机器学习和随机网络建模。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有关频谱使用的信息。通过学生培训和成果的广泛传播将产生更广泛的影响。 这项研究的总体目标是开发一种用于 DroTerNet 频谱和能源效率分析的整体新方法，从而产生以下关键创新：（i）将开发基于行列式点过程（DPP）思想的新学习框架便于对给定频段中同时活动节点的位置进行基于仿真和分析的表征，以实现多种共存方案，(ii) 利用机器学习中的多标签分类，一种基于深度 DPP 的新颖方法信道分配算法将通过利用 DPP 内核的结构来限制搜索空间来开发，(iii) 非线性接收器特性将包含在学习框架中，以量化它们对 DroTerNets 的能量和频谱效率的影响并开发新颖的接收器感知信道分配方案，（iv）由于机会性访问信道而产生的无人机的移动性约束和特征将被表征并纳入分析中，（v）空对地的测量和模型(A2G) 各种环境中的信道，特别强调决定多天线接收机有效性的方向特性，以及 (vi) 将进行地面和 A2G 链路之间相关性的实验研究和建模，以提供共存利润的坚实基础。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。

项目成果

Energy Efficiency of Uplink Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channel

• DOI: 10.1109/ojcas.2021.3125894
• 发表时间: 2021-11
• 期刊: IEEE Open Journal of Circuits and Systems
• 影响因子: 2.6
• 作者: Thomas Choi;Masaaki Ito;I. Kanno;Jorge Gómez-Ponce;Colton Bullard;T. Ohseki;K. Yamazaki;A. Molisch

Using a Drone Sounder to Measure Channels for Cell-Free Massive MIMO Systems

使用无人机测深仪测量无蜂窝大规模 MIMO 系统的信道

• DOI: 10.1109/wcnc51071.2022.9771649
• 发表时间: 2022
• 期刊: IEEE WCNC
• 作者: Choi, Thomas;Gomez-Ponce, Jorge;Bullard, Colton;Kanno, Issei;Ito, Masaaki;Ohseki, Takeo;Yamazaki, Kosuke;Molisch, Andreas F.
• 通讯作者: Molisch, Andreas F.

Air-to-Ground Directional Channel Sounder With Drone and 64-antenna Dual-polarized Cylindrical Array

带无人机和 64 天线双极化圆柱形阵列的空对地定向通道探测仪

• DOI: 10.1109/iccworkshops50388.2021.9473627
• 发表时间: 2021
• 期刊: IEEE ICC Workshops
• 作者: Gomez-Ponce, Jorge;Choi, Thomas;Abbasi, Naveed A.;Adame, Aldo;Alvarado, Alexander;Bullard, Colton;Shen, Ruiyi;Daneshgaran, Fred;Dhillon, Harpreet S.;Molisch, Andreas F.
• 通讯作者: Molisch, Andreas F.

Uplink Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channels

• DOI: 10.1109/sips52927.2021.00037
• 发表时间: 2021-08
• 期刊: 2021 IEEE Workshop on Signal Processing Systems (SiPS)
• 作者: Thomas Choi;Masaaki Ito;I. Kanno;Takeo Oseki;K. Yamazaki;A. Molisch