Collaborative Research: SWIFT: LARGE: AI-Enabled Spectrum Coexistence between Active Communications and Passive Radio Services: Fundamentals, Testbed and Data

合作研究：SWIFT：大型：主动通信和无源无线电服务之间人工智能支持的频谱共存：基础知识、测试平台和数据

• 批准号: 2202972
• 负责人: Fatemeh Afghah
• 金额: $ 20万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2202972&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; LARGE; AI

Passive remote sensing services are indispensable in modern society. One important remote sensing application for Earth science and climate studies is soil moisture monitoring, which provides crucial information for agricultural management; forecasting severe weather, floods and droughts; and climate modeling and prediction. In parallel, modern society also depends heavily on active wireless communications technologies for commerce, transportation, health, science, and defense. Unfortunately, the growth of active wireless systems often increases radio frequency (RF) interference (RFI) experienced by passive systems. At best, RFI may reduce the accuracy of the passive system's measurements; at worst, it may render them useless. The goal of this project is to develop advanced signal processing, resource management and artificial intelligence (AI) techniques at the active and passive users to enable them to coexist in the same RF bands, thereby making more spectrum available to active systems while protecting the passive systems from RFI. The results will be presented to scientists, regulators, industry and standardization bodies that shape future wireless systems and spectrum access rules. The project will support the PIs’ efforts to broaden the participation of students from underrepresented minority groups in engineering in collaboration with well-established programs at their institutions. Students trained through this project will be positioned to pioneer advanced wireless systems that are adaptable and can operate outside of dedicated RF spectrum. The testbed technology, methodology, and collected datasets will be shared with the scientific community and public through repositories and community research testbeds. This project combines emerging technologies to address research challenges across multiple layers of the network protocol stack and across active and passive RF systems to tackle the critical problem of active-passive RF spectrum coexistence. It develops novel sparsity and AI-based RFI detection and mitigation techniques at the physical and application layers of passive sensing systems. It introduces a wireless channel virtualization and waveform optimization framework at the physical layer of active transceivers—applicable to current and next generation wireless systems—to enable AI-based sparse time-frequency scheduling at the active transmitter's physical and medium access control layers. The proposed algorithms and waveforms will be co-optimized with the passive sensing system's RFI detection and mitigation strategy using offline training to further improve spectrum coexistence. To this end, the project is designing and developing a one-of-a-kind testbed in collaboration with NASA for collecting, processing and sharing remote sensing datasets in conjunction with ground and drone-based active communication systems with ground truth data.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.

被动遥感服务在地球科学和气候研究中不可或缺的一项重要应用是土壤湿度监测，它为农业管理、预测灾害性天气、洪水和干旱以及气候建模和预测提供了重要信息。现代社会在商业、交通、健康、科学和国防方面也严重依赖有源无线通信技术，不幸的是，有源无线系统的发展往往会增加无源系统所经历的射频 (RF) 干扰 (RFI)。 RFI 可能会降低准确度无源系统的测量；在最坏的情况下，可能会使它们毫无用处。该项目的目标是为有源和无源用户开发先进的信号处理、资源管理和人工智能（AI）技术，使它们能够在同一射频中共存。频段，从而为有源系统提供更多频谱，同时保护无源系统免受 RFI 影响。研究结果将提交给制定未来无线系统和频谱访问规则的科​​学家、监管机构、行业和标准化机构。该项目将支持 PI 的工作。扩大学生的参与范围通过该项目接受培训的学生将能够在工程领域与所在机构的成熟项目合作，开发具有适应性且可以在专用射频频谱之外运行的先进无线系统。该项目将通过存储库和社区研究测试平台与科学界和公众共享，以解决跨网络协议栈多层以及有源和无源射频系统的研究挑战，从而解决有源-无源射频的关键问题。光谱它在无源传感系统的物理层和应用层开发了新颖的稀疏性和基于人工智能的 RFI 检测和缓解技术，在有源收发器的物理层引入了无线通道虚拟化和波形优化框架，适用于当前和下一代。无线系统——在有源发射机的物理和介质访问控制层实现基于人工智能的稀疏时频调度。所提出的算法和波形将与无源传感系统的 RFI 检测和传输共同优化。为此，该项目正在与 NASA 合作设计和开发一种独一无二的测试平台，用于结合地面和无人机收集、处理和共享遥感数据集。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

Meta-Learning for Wireless Interference Identification

无线干扰识别的元学习

• DOI: 10.1109/wcnc55385.2023.10119039
• 发表时间: 2023-03
• 期刊: 2023 IEEE Wireless Communications and Networking Conference (WCNC
• 作者: Owfi, Ali;Afghah, Fatemeh;Ashdown, Jonathan
• 通讯作者: Ashdown, Jonathan

An Exhaustive Study of Using Commercial LTE Network for UAV Communication in Rural Areas

农村地区使用商用 LTE 网络进行无人机通信的详尽研究

• DOI: 10.1109/iccworkshops50388.2021.9473547
• 发表时间: 2021-06
• 期刊: 2021 IEEE International Conference on Communications Workshops (ICC Workshops
• 作者: Gharib, Mohammed;Nandadapu, Shashidhar;Afghah, Fatemeh
• 通讯作者: Afghah, Fatemeh

A Meta-learning based Generalizable Indoor Localization Model using Channel State Information

使用信道状态信息的基于元学习的可推广室内定位模型

• DOI: 10.1109/globecom54140.2023.10436827
• 发表时间: 2023-05-22
• 期刊: GLOBECOM 2023 - 2023 IEEE Global Communications Conference
• 作者: Ali Owfi;ChunChih Lin;Linke Guo;F. Afghah;J. Ashdown;K. Turck
• 通讯作者: K. Turck

Joint 3D Placement and Interference Management for Drone Small Cells

无人机小型基站的联合 3D 放置和干扰管理

• DOI: 10.1109/ieeeconf53345.2021.9723350
• 发表时间: 2021-10
• 期刊: and Computers ASILOMAR
• 作者: Namvar, Nima;Afghah, Fatemeh
• 通讯作者: Afghah, Fatemeh

UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning

利用模仿学习在偏远灾区进行无人机辅助通信

• DOI: 10.1109/ojcoms.2021.3067001
• 发表时间: 2021-01
• 期刊: IEEE Open Journal of the Communications Society
• 影响因子: 7.9
• 作者: Shamsoshoara, Alireza;Afghah, Fatemeh;Blasch, Erik;Ashdown, Jonathan;Bennis, Mehdi
• 通讯作者: Bennis, Mehdi