发表机构
University at Buffalo; Virginia Tech; University of Minnesota - Twin Cities（布法罗大学; 弗吉尼亚理工大学; 明尼苏达大学双城分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文设计了面向智能软件定义无人机网络的可配置协议栈C2Stack，通过定制平台实现其部署测试，验证了动态空中环境下数据驱动优化的可行性，公开源代码以支撑相关实验研究。
AI 中文摘要
无人机（UAV）正成为下一代无线网络和自主系统的关键使能技术。尽管其潜力巨大，但在真实环境中部署和测试网络化无人机系统仍颇具挑战，主要原因是缺乏成熟、端到端、可直接使用的协议栈。为填补这一空白，我们提出了C2Stack，这是一个专为无人机网络的实时控制、评估与优化设计的可配置协议栈及实验框架。C2Stack包含一个名为C2Stack网络操作系统（CNOS）的模块化控制平面，以及一个可编程数据平面，该平面提供用于跨层算法开发、数字孪生集成和自主集群控制的应用程序接口（API）。本文分享了我们部署和测试C2Stack的经验。我们在一个定制的无人机集群平台上实现了C2Stack，该平台将多处理器片上系统（MPSoC）无线电与英特尔NUC计算模块集成，支持与多种射频前端互操作。现场试验在组网环境和大规模室外测试场中开展，聚焦两个代表性用例：（i）通过在线强化学习实现网络效用最大化；（ii）协作式干扰源定位。实验证明了动态空中环境中实时数据驱动优化的可行性，同时也揭示了网络化无人机系统现场部署中的实际挑战，包括功率限制、感知局限和部署后勤问题。我们已根据MIT许可将C2Stack的源代码向社区公开，目标是将其确立为智能网络化空中系统实验研究的基础框架。
英文摘要
Unmanned Aerial Vehicles (UAVs) are emerging as critical enablers of next-generation wireless networking and autonomous systems. Despite their potential, deploying and testing networked UAV systems in real-world environments remains challenging, largely due to the absence of well-developed, end-to-end, ready-to-use protocol stacks. To fill this gap, we present C2Stack, a configurable protocol stack and experimental framework designed for real-time control, evaluation, and optimization of UAV networks. C2Stack incorporates a modular control plane, referred to as the~C2Stack Network Operating System (CNOS), alongside a programmable data plane that exposes APIs for cross-layer algorithm development, digital twin integration, and autonomous swarm control. In this article, we share our experience with the deployment and testing of C2Stack. We implemented C2Stack on a custom UAV swarm platform that integrates multiprocessor system-on-chip (MPSoC) radios with Intel NUC computing modules, enabling interoperability with various RF front ends. Field trials were conducted in both netted environments and large-scale outdoor test ranges, focusing on two representative use cases: (i) network utility maximization through online reinforcement learning, and (ii) collaborative interference source localization. The experiments demonstrate the feasibility of real-time, data-driven optimization in dynamic aerial environments, while also revealing practical challenges in field deployments of networked UAV systems, including power constraints, sensing limitations, and deployment logistics. We have made C2Stack source code available to the community under the MIT License, with the goal of establishing it as a foundational framework for experimental research on intelligent networked aerial systems.
