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，对无人机的兴趣非常重要，其应用程序从公共安全，急救人员，监视，到包装交付。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。无论应用如何，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陆地节点进行通信。由于从较高海拔的传输具有干扰大型领域的其他无线服务的潜力，因此目前正在争论是否以及在哪些规则下应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操作它们。为了回答这样的重要和及时的问题，该项目通过融合来自多个学科的思想，（例如传播理论，宣传科学，测试床开发，机器学习，机器学习，机器学习和现有网络模型）来开发一种新的跨学科方法，以设计和分析无人机和地面网络（Droternets）的设计和分析。这项研究将通过为无人机范围的高风险裁决提供科学依据，以频谱使用范围来为行业和政府提供信息。进一步的更广泛的影响将是通过学生培训和广泛的结果传播。 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在分析中将表征和纳入渠道的机会主义访问，（v）在各种环境中的空中（A2G）渠道的测量和模型，特别强调了方向特征，这些方向特征确定了多个安特纳接收器的有效性，并且将获得（vi）实验和构建的相关性链接的实验和模型。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。

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