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

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.

下一代（NextG）无线网络为用户提供自定义的即时服务，尤其是对于挂带和对潜伏期敏感的应用程序。尽管NextG无线网络（例如5G / 6G和毫米波 / Tera Hertz）具有显着优势，但意识到它们面临着几个关键的部署和评估挑战：1）如何加快新颖而复杂的Nextg Next网络技术的部署； 2）如何提供高可用性的灵活测试设施。在这方面，迫切需要一个虚拟解决方案，该解决方案可以创建一个数字模型以尽可能准确地复制NextG网络生态系统并有助于解决上述障碍，然后才能完全实现真实系统。为此，该项目探索了运行忠实的数字网络双胞胎的方法，该双胞胎复制了物理NextG网络，然后在考虑通信，计算和网络资源约束的同时，通过实际网络构建和优化双胞胎。构建的网络双胞胎提供了一个总体架构，涉及物理网络的整个生命周期，为网络计划，构建，优化和预测性评估等创新技术的关键应用以及提高无线网络的自动化和智能水平。 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通道推导，以及基于回归的预测，以将NextG无线网络映射到数字网络双胞胎中，然后自适应地发展映射的双胞胎； 2）结合图神经网络，分布式学习和强化学习的新数字网络双胞胎管理和优化框架，以允许物理网络中的分布式设备首先独立确定其映射方法和资源利用，然后在实际网络环境中协作最大化数字网络双胞胎的性能； 3）基于仿真和实验的设计平台和评估方法的设计，以证明已建立网络双胞胎的保真度，功效和最佳性。该项目提供了一个丰富的环境和虚拟化平台，可促进多个级别的教育和培训学生。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。