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.

这个小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是将最先进的地面5G技术扩展到非地面卫星网络。非地面网络(NTN)是许多涉及监测、侦察和遥感的商业应用的理想主机，例如农业种植、远程工厂运营和工业自动化。这些应用将需要来自NTN的大量计算和带宽资源。软件定义网络(SDN)和网络切片等虚拟化技术是地面网络中类似应用的关键推动因素，但卫星网络对现有5G技术构成了一系列独特的挑战。NTN具有复杂和高度动态的拓扑，这既是由于卫星的可预测运动，也是由于不可预测的天气事件干扰卫星对地链路。将5G算法扩展到NTN可以实现新的应用，并在不发射任何新卫星的情况下释放额外的网络容量。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，无需了解不同星座的不同接口。这个第一阶段的项目将探索更有效的网络解决方案，卫星网络运营商可以使用这些解决方案来满足日益增长的卫星互联网需求。这个SBIR第一阶段项目探索了将SDN和虚拟网络切片技术扩展到空间的方法，考虑到NTN拓扑构成的挑战。首先，它提出了使用现有5G技术的非地面SDN的“NextG”框架，其核心协调器可以创建端到端的切片，以在地面和NTN上无缝通信。在SDN中，一个或多个称为控制器的软件实体负责控制网络。可以选择网络中控制器的数量和位置以及这些控制器通信的频率，以平衡NTN中的延迟和发送同步消息的开销成本。该项目探索使用深度强化学习算法来学习最优控制器配置和同步策略。其次，网络切片是共享公共网络资源基础设施的独立虚拟网络。离线算法用于根据虚拟网络运营商的请求将网络资源分配给切片。随后，在线算法根据切片的实际资源使用情况实时动态地重新调配网络资源。该团队将研究将该规模嵌入到更大的NTN拓扑中的虚拟网络的整数编程技术，以及用于在线资源扩展的深度强化学习代理。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。