Collaborative Research: NeTS: Small: Digital Network Twins: Mapping Next Generation Wireless into Digital Reality

下一代（NextG）无线网络为用户提供定制的即时服务，特别是对于带宽需求和延迟敏感的应用。尽管NextG无线网络具有显著的优点（例如，5G/6 G和毫米波/ Tera Hertz），实现它们面临着几个关键的部署和评估挑战：1）如何加快新型但复杂的NextG网络技术的部署; 2）如何提供灵活的高可用性测试平台设施。在这方面，迫切需要一种虚拟解决方案，可以创建数字模型，以尽可能准确地复制NextG网络生态系统，并在完全实现真实的系统之前帮助解决上述障碍。为此，该项目探索了运行忠实数字网络双胞胎的方法，这些网络复制了物理NextG网络，然后在考虑通信、计算和网络资源限制的情况下，在实际网络上构建和优化双胞胎。已建成的网络双胞胎提供了一个涵盖物理网络全生命周期的总体架构，服务于网络规划、建设、优化、预测评估等创新技术的关键应用，提升无线网络的自动化和智能化水平。这项变革性的研究为数字网络双胞胎的实施和优化提供了一个整体框架，从而促进了具有重大社会影响的未来网络系统的部署和运营。这项拟议的研究通过开发一个新的框架，融合了机器学习，通信理论和分布式优化的工具，为数字网络双胞胎奠定了基础，以推进网络技术：1）新颖的映射方法，其集成了数据驱动建模、射线跟踪分析、无线信道推导和基于回归的预测，以将NextG无线网络映射为数字网络孪生，然后自适应地演化映射的孪生; 2）新的数字网络孪生管理和优化框架，结合了图神经网络，分布式学习和强化学习，允许物理网络中的分布式设备首先独立地确定它们的映射方法和资源利用率，然后在实际网络环境中协作地最大化数字网络孪生性能; 3）设计基于仿真和实验的孪生平台和评估方法，以证明保真度，有效性，和所建网络孪生体的最优性。该项目提供了丰富的环境和虚拟化平台，便于教育和培训学生在多个层次。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

Next-generation (NextG) wireless networks provide users with customized, instant services, especially for bandwidth-hungry and latency-sensitive applications. Despite the significant advantages of NextG wireless networks (e.g., 5G/6G and millimeter-wave / Tera Hertz), realizing them faces several key deployment and evaluation challenges: 1) how to speed up the deployment of novel yet complex NextG network technologies; and 2) how to provide flexible testbed facilities with high availability. In this regard, there is an urgent need for a virtual solution that could create a digital model to replicate as accurately as possible the NextG network ecosystem and help tackle the above obstacles before the full realization of a real system. To this end, this project explores methodologies to run faithful digital network twins that replicate the physical NextG networks, and then to build and optimize the twins over the actual networks while considering communication, computing, and networking resource constraints. The built network twins provide an overarching architecture involving the whole life cycle of physical networks, serving the critical application of innovative technologies such as network planning, construction, optimization, and predictive evaluation, and improving the automation and intelligence level of the wireless networks. This transformative research provides a holistic framework for the implementation and optimization of digital network twins, thus catalyzing the deployment and operation of future network systems with major societal impact.This proposed research lays the foundations of digital network twins by developing a novel framework that merges tools from machine learning, communication theory, and distributed optimization to advance the networking technologies in: 1) novel mapping approaches that integrate data-driven modeling, ray-tracing analysis, wireless channel derivation, and regression-based predictions to map NextG wireless networks into digital network twins and then to evolve the mapped twins adaptively; 2) new digital network twin management and optimization framework that combines graph neural networks, distributed learning, and reinforcement learning, to allow distributed devices in a physical network to first independently determine their mapping methods and resource utilization, and then collaboratively maximize the digital network twin performance over actual network environments; 3) design of the twinning platform and evaluation methodology based on simulation and experiments to demonstrate the fidelity, efficacy, and optimality of the built network twins. The project provides a rich environment and virtualized platform that facilitate educating and training students at multiple levels.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.