SBIR Phase I: Software-Defined Networking and Resource Virtualization in Low Earth Orbit (LEO) Satellite Constellations

SBIR 第一阶段：近地轨道 (LEO) 卫星星座中的软件定义网络和资源虚拟化

• 批准号: 2304470
• 负责人: Nicholas Nordlund
• 金额: $ 27.49万
• 依托单位: UNINET LLC
• 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: 2023-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304470&HistoricalAwards=false
• 关键词: SBIR; Phase; Software; Defined; Networking

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the extension of state-of-the-art terrestrial 5G technologies to non-terrestrial satellite networks. Non-terrestrial networks (NTNs) are the ideal hosts for many commercial applications involving monitoring, reconnaissance, and remote sensing such as agricultural planting, remote factory operations, and industrial automation. These applications will demand significant computation and bandwidth resources from NTNs. Virtualization technologies like software-defined networking (SDN) and network slicing are key enablers for similar applications in terrestrial networks, but satellite networks pose a unique set of challenges to existing 5G technologies. NTNs have complex and highly dynamic topologies caused by both the predictable movement of satellites as well as unpredictable weather events interfering with satellite-to-ground links. Extending 5G algorithms to NTNs enables new applications and unlocks additional network capacity without launching any new satellites. 5G virtualization also simplifies access to satellite constellations. Customers can interact with multiple satellite networks via the same virtual network interface, eliminating the need to learn different interfaces for different constellations. This Phase I project will explore more efficient networking solutions that satellite network operators can use to supply the growing demand for satellite internet. This SBIR Phase I project explores methods of extending SDN and virtual network slicing technologies into space given the challenges posed by NTN topologies. First, it proposes a "NextG" framework for non-terrestrial SDN using existing 5G technologies whose core orchestrator can create end-to-end slices for seamless communication over terrestrial and NTN. In SDN, one or multiple software entities called controllers are responsible for the control of the network. The number and locations of controllers in the network and how often these controllers communicate can be selected to balance latency in the NTN and the overhead costs from sending synchronization messages. This project explores the use of deep reinforcement learning algorithms to learn optimal controller placement and synchronization strategies. Second, network slices are independent virtual networks that share a common infrastructure of network resources. Offline algorithms are used to allocate network resources to slices based on the requests of the virtual network operator. Subsequently, online algorithms dynamically reprovision network resources in real-time depending on the slices' actual resource usages. The team will investigate integer programming techniques for virtual network embedding that scale to larger NTN topologies and deep reinforcement learning agents for online resource scaling.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.

这项小型企业创新研究（SBIR）I期项目的更广泛的影响/商业潜力是将最先进的陆地5G技术扩展到非事物卫星网络。非事物网络（NTN）是许多商业应用的理想宿主，涉及监测，侦察和遥感，例如农业种植，远程工厂运营和工业自动化。这些应用程序将需要NTN的大量计算和带宽资源。虚拟化技术（例如软件定义的网络（SDN）和网络切片）是地面网络中类似应用程序的关键推动力，但是卫星网络对现有的5G技术构成了一系列独特的挑战。 NTN具有由卫星的可预测运动以及无法预测的天气事件造成的复杂且高度动态的拓扑结构。将5G算法扩展到NTNS可以启用新的应用程序，并在不推出任何新卫星的情况下解锁其他网络容量。 5G虚拟化还简化了对卫星星座的访问。客户可以通过相同的虚拟网络接口与多个卫星网络进行交互，从而消除了学习不同星座的不同接口的需求。该阶段I项目将探索更有效的网络解决方案，卫星网络运营商可以用来提供对卫星互联网不断增长的需求。该SBIR I阶段项目探讨了将SDN和虚拟网络切片技术扩展到空间的方法，鉴于NTN拓扑带来的挑战。首先，它使用现有的5G技术为非事物SDN提出了一个“ NextG”框架，其核心编目可以创建端到端切片，以实现陆地和NTN的无缝通信。在SDN中，一个称为控制器的一个或多个软件实体负责网络的控制。可以选择网络中控制器的数量和位置，以及这些控制器进行交流的频率，以平衡NTN的延迟以及发送同步消息的间接费用。该项目探讨了深入增强学习算法来学习最佳控制器的放置和同步策略。其次，网络切片是独立的虚拟网络，共享网络资源的共同基础架构。离线算法用于根据虚拟网络运营商的请求分配网络资源为切片。随后，在线算法取决于切片的实际资源使用情况，以实时的方式进行了动态重复网络资源。该团队将调查用于虚拟网络的整数编程技术，以嵌入将其扩展到更大的NTN拓扑和在线资源缩放的深度强化学习推动者。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的影响审查标准来通过评估来进行评估的。