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，特别是对带宽要求高和延迟敏感的应用。尽管下一代无线网络（例如5G/6G和毫米波/ Tera赫兹）具有显著优势，但实现它们面临几个关键的部署和评估挑战：1)如何加快新型但复杂的下一代网络技术的部署；2)如何提供灵活、高可用性的试验台设施。在这方面，迫切需要一种虚拟解决方案，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，并在完全实现真实系统之前帮助解决上述障碍。为此，本项目探索了运行复制物理NextG网络的忠实数字网络双胞胎的方法，然后在考虑通信、计算和网络资源约束的情况下，在实际网络上构建和优化双胞胎。已建网络双胞胎提供了一个涉及物理网络全生命周期的总体架构，服务于网络规划、建设、优化和预测评估等创新技术的关键应用，提高无线网络的自动化和智能水平。这一变革性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，从而促进具有重大社会影响的未来网络系统的部署和运行。本研究提出了一个新的框架，该框架融合了机器学习、通信理论和分布式优化工具，为数字网络双胞胎奠定了基础，以推进网络技术的发展：1)新的映射方法，集成了数据驱动建模、光线追踪分析、无线信道推导和基于回归的预测，将NextG无线网络映射为数字网络双胞胎，然后自适应地进化映射的双胞胎；2)结合图神经网络、分布式学习和强化学习的新型数字网络孪生管理和优化框架，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率，然后在实际网络环境中协作最大化数字网络孪生性能；3)基于仿真和实验的孪生平台设计和评估方法，验证了构建的网络双胞胎的保真度、有效性和最优性。该项目提供了丰富的环境和虚拟化平台，便于在多个层次上对学生进行教育和培训。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.