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激发了许多新概念和无线创新，包括3GPP的5G地面和非地面网络（NTN）服务。随着来自人工智能、自动化、物联网和虚拟/增强现实等大量应用的大量数据流量不断涌入现有容量有限的系统，不断增长的频谱需求促使无线网络扩展到新的无线电频段。随着地面无线电频谱的扩展，地面和卫星系统之间的频谱共存变得越来越重要。这一合作性的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来开发受物理启发的学习框架，以促进地面和卫星系统之间的高效和谐共存。 利用测量数据的先验知识和无线电物理模型的限制，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，以实现先进的网络优化和适应。精确的无线电覆盖图（radio-map）估计是地面和卫星系统之间的干扰限制共存的关键。主要的项目推力是针对无线电地图的动态估计和预测，以及它们的集成到无线网络的优化和适应共存的约束。只需要在空间，频率和时间域的稀疏观测，研究人员采用物理启发和模型驱动的学习方法，以准确地估计共信道和相邻信道干扰的基础上无线电地图估计共存的无线系统。 该项目创新还包括基于学习的方法，由传播模型指导，用于无线电地图信息促进的智能无线网络优化和干扰诊断。通过所提出的创新，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，并且可以使用有效的网络自适应策略进行响应。预计拟议的解决方案和项目成果将对地面和卫星系统之间干扰有限共存的未来技术发展产生重大影响。更广泛地说，这项工作促进了数据采集，通信，传感和分布式计算的可靠和智能无线系统。该项目的更广泛影响包括这项研究刺激了许多新的教育机会。该项目团队已经与当地市中心的学校合作，计划在该项目中为K-12和代表性不足的人才提供STEM学习机会。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。