SWIFT-SAT: Network Adaptation Based on Physics-Inspired Learning Framework for Radio Coexistence of Terrestrial and Satellite Information Systems

SWIFT-SAT：基于物理启发的学习框架的网络适应地面和卫星信息系统的无线电共存

• 批准号: 2332760
• 负责人: Zhi Ding
• 金额: $ 75万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2332760&HistoricalAwards=false
• 关键词: SWIFT; SAT; Network; Adaptation; Based

Major advances in information technologies have stimulated numerous novel concepts and wireless innovations including 3GPP’s 5G terrestrial and non-terrestrial network (NTN) services. As high volumes of data traffic from a rich plethora of applications ranging from artificial intelligence, automation, IoT, and virtual/augmented reality continue to cram into existing system of limited capacity, the ever-increasing spectrum need motivates the expansion of wireless networking into new radio bands. As the terrestrial radio spectrum expands, spectrum coexistence between terrestrial and satellite systems becomes increasingly critical. This collaborative SWIFT-SAT project aims to develop physics-inspired learning frameworks by integrating physical radio propagation models and data-driven learning machines to facilitate efficient and harmonious coexistence between terrestrial and satellite systems. Leveraging prior knowledge from measurement data and the constraints imposed by radio physical models, the proposed research activities are geared towards establishing accurate radio interference models and databases to empower advanced network optimization and adaptation. Accurate radio coverage map (radio-map) estimation is critical to interference-limited coexistence between terrestrial and satellite systems. Major project thrusts are directed at dynamic estimation and prediction of radio-map as well as their integration into wireless network optimization and adaptation under coexistence constraints. Requiring only sparse observations in spatial, frequency, and temporal domains, the investigators employ physics-inspired and model-driven learning approaches to accurately estimate co-channel and adjacent channel interferences based on radio-map estimation of coexisting wireless systems. The project innovation further includes learning-based approaches guided by propagation models for intelligent wireless network optimization and interference diagnosis facilitated by radio-map information. Through the proposed innovation, model-based radio coverage estimation can anticipate interference anomalies, can detect or diagnose network outages quickly, and can respond with effective network adaptation policies. The proposed solutions and project outcomes are expected to significantly impact future technology development for interference-limited coexistence between terrestrial and satellite systems. More broadly, this work promotes reliable and intelligent wireless systems across data acquisition, communication, sensing, and distributed computing. Broader impacts from this project include many new educational opportunities stimulated by this research. Already in partnership with local inner-city schools, the project team plans to incorporate into this project learning opportunities in STEM for K-12 and under-represented talents.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.

信息技术的重大进步刺激了许多新颖的概念和无线创新，包括3GPP的5G陆上和非事物网络（NTN）服务。由于大量的数据流量从人工智能，自动化，物联网和虚拟/增强现实等广泛的应用程序中，继续将RAPM持续到现有有限容量的系统中，因此不断增加的频谱需求激发了无线网络扩展到新广播频段中。随着陆地无线电光谱的扩展，陆地系统和卫星系统之间的频谱共存变得越来越重要。这个协作的Swift-SAT项目旨在通过整合物理无线电传播模型和数据驱动的学习机，以促进地面和卫星系统之间的有效和和声共存，以开发受物理启发的学习框架。从测量数据和无线电物理模​​型施加的约束中利用先验知识，拟议的研究活动旨在建立准确的无线电干扰模型和数据库，以增强先进的网络优化和适应性。准确的无线电覆盖图（射击图）估计对于陆地和卫星系统之间的干扰限制共存至关重要。主要的项目推力针对动态估计和无线电图的预测，以及它们在共存约束下的无线网络优化和适应性的集成。仅需要在空间，频率和临时域中进行稀疏观察，研究人员采用物理启发和模型驱动的学习方法来准确估算基于共存无线系统的射击图估计的射击图估计，并基于无线系统的射击估算。该项目创新进一步包括基于学习的方法，这些方法是通过传播模型的智能无线网络优化和干扰诊断来指导的。通过拟议的创新，基于模型的无线电覆盖估计可以预测干扰异常，可以快速检测或诊断网络中断，并可以通过有效的网络适应策略做出响应。拟议的解决方案和项目成果有望显着影响未来的技术开发，以实现陆地和卫星系统之间的干扰限制共存。从更广泛的角度来看，这项工作促进了跨数据采集，通信，传感和分布式计算的可靠且智能的无线系统。该项目的更广泛影响包括这项研究刺激的许多新的教育机会。该项目团队已经与当地内城学校合作，计划将K-12和代表性不足的人才纳入该项目学习机会中。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的评估标准通过评估来获得的支持。