SWIFT-SAT: Efficient and On-Demand Spectrum Coexistence for Satellite-Terrestrial Systems

SWIFT-SAT：卫星-地面系统的高效且按需频谱共存

• 批准号: 2332675
• 负责人: Yi Shi
• 金额: $ 75万
• 依托单位: Virginia Polytechnic Institute and State University
• 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=2332675&HistoricalAwards=false
• 关键词: SWIFT; SAT; Efficient; Demand; Spectrum

The escalating demand for wireless communications, fueled by emerging applications, has led to radio spectrum congestion. This project designs efficient schemes for spectrum coexistence between satellite and terrestrial systems. One spectrum sharing approach used today is called a Spectrum Access System (SAS), a centralized database and control system that assigns channels to users in real-time such that other users are protected from interference. Existing SAS solutions support terrestrial-terrestrial sharing in situations where every device transmits as well as receives. The new schemes developed in this project extend the SAS concept to manage satellite-terrestrial spectrum sharing. Key challenges addressed in the project include (a) some devices on the ground and in orbit are passive receivers that cannot be detected by spectrum sensing, and (b) the SAS must manage aggregate interference of multiple transmitters with awareness of dynamically changing receiver beams. The findings from this project will provide valuable insights for ongoing efforts by government policy-makers to open mid-bands and high-bands for spectrum sharing and commercial use.The project has three main research thrusts. The first thrust explores architectural options for the satellite-terrestrial SAS. Key considerations for the architecture include: effective data collection regarding systems, locations, and beams; efficient calculation of aggregate interference; efficient calculation of coexistence solutions, which may involve scalable decentralized algorithms run by affected users; and minimization of changes to existing satellite system protocols. The second thrust develops effective SAS-based methods to achieve interference protection for satellite systems, including channel estimation and aggregate interference computation. Factors considered include directional antennas, advanced interference management schemes such as MIMO and successive interference cancellation, terrain blockage, idle periods, partial spectrum utilization, and risk-based interference protection. The third thrust designs a multi-time scale coexistence scheme: precalculated solutions for satellites with fixed beams or ground systems, and real-time calculated solutions for satellites with unpredictable beams and mobile ground systems. The initially nonlinear optimization problem is linearized to obtain Mixed Integer Linear Program (MILP) formulations. Multiple algorithms to solve the MILP problems are developed: use subproblem partitioning to enable real time optimization on GPUs, use reinforcement learning to develop low-complexity algorithms, and use federated learning to enable decentralized solution across a network of terrestrial users. Evaluation of the research includes technical and policy assessment of candidate architectures, verification of channel predictions, and software prototypes of coexistence algorithms.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.

由于新兴应用的推动，对无线通信的需求不断上升，导致了无线电频谱拥塞。该项目为卫星和地面系统之间的频谱共存设计了有效的方案。当今使用的一种频谱共享方法称为频谱接入系统（SAS），这是一种集中式数据库和控制系统，可以实时为用户分配信道，以便保护其他用户免受干扰。现有的SAS解决方案支持在每个设备都进行传输和接收的情况下进行地-地共享。该项目开发的新方案将SAS概念扩展到管理卫星-地面频谱共享。该项目中解决的关键挑战包括：（a）地面和轨道上的一些设备是被动接收器，无法通过频谱感知检测到，以及（B）SAS必须管理多个发射器的聚合干扰，并了解动态变化的接收器波束。该项目的研究结果将为政府决策者正在进行的开放中频段和高频段以供频谱共享和商业使用的努力提供宝贵的见解。该项目有三个主要研究方向。第一个推力探讨了卫星地面SAS的建筑选择。该架构的关键考虑因素包括：关于系统、位置和波束的有效数据收集;聚合干扰的有效计算;共存解决方案的有效计算，这可能涉及受影响用户运行的可扩展分散算法;以及最大限度地减少对现有卫星系统协议的更改。第二个推力发展有效的SAS为基础的方法来实现卫星系统的干扰保护，包括信道估计和聚合干扰计算。考虑的因素包括定向天线、先进的干扰管理方案（如MIMO和连续干扰消除）、地形阻塞、空闲期、部分频谱利用率和基于风险的干扰保护。第三个推力设计了一个多时间尺度共存的方案：预先计算的解决方案，卫星与固定波束或地面系统，和实时计算的解决方案，卫星与不可预测的波束和移动的地面系统。将初始非线性优化问题线性化，得到混合线性规划（MILP）公式。开发了多种算法来解决MILP问题：使用子问题划分来实现GPU上的真实的时间优化，使用强化学习来开发低复杂度算法，以及使用联邦学习来实现跨地面用户网络的分散解决方案。研究评估包括候选架构的技术和政策评估、信道预测的验证以及共存算法的软件原型。该奖项反映了NSF的法定使命，并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，被认为值得支持。