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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

基本信息

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

项目摘要

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.

被动遥感服务是现代社会不可缺少的服务。遥感在地球科学和气候研究方面的一个重要应用是土壤湿度监测，这为农业管理、预报恶劣天气、洪水和干旱以及气候建模和预测提供了重要信息。与此同时，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，有源无线系统的增长经常增加无源系统所经历的射频（RF）干扰（RFI）。在最好的情况下，RFI可能会降低无源系统测量的准确性;在最坏的情况下，它可能会使它们变得无用。该项目的目标是在有源和无源用户处开发先进的信号处理、资源管理和人工智能（AI）技术，使它们能够共存于相同的RF频段，从而为有源系统提供更多的频谱，同时保护无源系统免受RFI的影响。研究结果将提交给科学家、监管机构、行业和标准化机构，以塑造未来的无线系统和频谱接入规则。该项目将支持PI的努力，以扩大来自代表性不足的少数群体的学生在工程与他们的机构完善的计划合作的参与。通过该项目培训的学生将被定位为先进的无线系统的先驱，这些系统具有适应性，可以在专用RF频谱之外运行。测试平台的技术、方法和收集的数据集将通过知识库和社区研究测试平台与科学界和公众分享。该项目结合了新兴技术，以解决跨网络协议栈的多个层以及跨有源和无源RF系统的研究挑战，以解决有源-无源RF频谱共存的关键问题。它在被动传感系统的物理和应用层开发了新的稀疏性和基于AI的RFI检测和缓解技术。它在主动发射机的物理层引入了无线信道虚拟化和波形优化框架-适用于当前和下一代无线系统-以在主动发射机的物理层和介质访问控制层实现基于AI的稀疏时频调度。所提出的算法和波形将与使用离线训练的无源感测系统的RFI检测和缓解策略共同优化，以进一步改善频谱共存。为此，该项目正在与美国航天局合作设计和开发一个独一无二的试验平台，用于收集、处理和共享遥感数据集，并与地面和无人机主动通信系统一起使用地面实况数据。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning

DOI：
10.1109/ojcoms.2021.3067001
发表时间：
2021-04
期刊：
IEEE Open Journal of the Communications Society
影响因子：
7.9
作者：
Alireza Shamsoshoara;F. Afghah;Erik Blasch;J. Ashdown;M. Bennis
通讯作者：
Alireza Shamsoshoara;F. Afghah;Erik Blasch;J. Ashdown;M. Bennis

Heterogeneous Airborne mmWave Cells: Optimal Placement for Power-Efficient Maximum Coverage

异构机载毫米波蜂窝：最佳放置以实现节能的最大覆盖范围

DOI：
10.1109/infocomwkshps54753.2022.9798023
发表时间：
2022
期刊：
IEEE INFOCOM Workshop on Artificial Intelligence and Blockchain-Enabled Secure and Privacy-Preserving Air and Ground Smart Vehicular Networks (AIBESVN
影响因子：
0
作者：
Namvar, Nima;Afghah, Fatemeh
通讯作者：
Afghah, Fatemeh

Electromagnetic Spectrum Contribution in Astronomy, Health, Atmospheric, Geology and Environment Applications

DOI：
10.1007/s10776-022-00558-7
发表时间：
2022-08
期刊：
International Journal of Wireless Information Networks
影响因子：
2.5
作者：
S. Zekavat;F. Afghah;R. Askari;J. Delabrouille;Nancy H F French;J. C. Furtado;S. Hanany;V. Lubecke-V.-Lu
通讯作者：
S. Zekavat;F. Afghah;R. Askari;J. Delabrouille;Nancy H F French;J. C. Furtado;S. Hanany;V. Lubecke-V.-Lu

Autoencoder-based Radio Frequency Interference Mitigation for SMAP Passive Radiometer

基于自动编码器的 SMAP 无源辐射计射频干扰缓解