Collaborative Research: SWIFT: Data Driven Learning and Optimization in Reconfigurable Intelligent Surface Enabled Industrial Wireless Network for Advanced Manufacturing

下一代智能工厂需要一个高质量和可靠的无线网络，可以支持共存的分布式传感器和机器之间广泛的信息交换。然而，传统的无线网络技术在有限的工厂空间、不确定的无线环境、未知的干扰或干扰等方面有严格的延迟和可靠性要求，并不能直接应用于制造工厂，同时还存在安全问题。另一方面，新兴的可重构智能表面（RIS）技术是一种很有前途的解决方案，可以显著提高传统无线网络的质量（例如降低延迟、提高可靠性等），并提供安全性，特别是在制造工厂等复杂的动态无线环境下。因此，该项目的目标是提供硬件驱动的在线学习和优化ris增强工业无线网络的新框架。为了实现这一目标，拟议的研究将提供关键组件，以促进固定和移动用户工业无线网络的可靠和优化设计，并促进其采用。这项研究还得到了一个全面的教育计划的补充，包括课程开发、实验室增强，以及让本科生和研究生参与研究。已计划开展多种外展活动，吸引来自两所hbcu、一所MSI和其他机构的K-12和代表性不足的学生。本研究将为可重构智能表面（RIS）硬件驱动的跨层优化和数据支持的在线学习算法开发开发基础分析和实验方法。该项目将提供几个新颖的贡献，包括1)未知干扰下ris辅助工业无线网络的新型硬件驱动跨层优化，2)一种新的实时数据支持学习方法，可以解决苛刻约束下复杂的跨层优化问题，3)一个鲁棒且计算高效的学习框架，可以以分布式方式优化大规模ris增强无线网络。4)设计和制造支持动态波束转向能力的RIS单元，以及用于在实际环境中评估开发的RIS增强型工业无线网络的硬件测试平台。此外，该项目将引领工业无线网络优化、机器学习和弹性计算的新方向，并进一步为基于实时学习的优化开发和实施铺平道路。这项研究将为未来的无线革命和国家优先发展的先进制造业做出贡献。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。

The next generation of smart factories needs a high-quality and reliable wireless network that can support extensive information exchange between coexisted distributed sensors and machines. However, traditional wireless network techniques cannot be directly applied to manufacturing factories due to their stringent latency and reliability requirements in confined factory space, uncertain wireless environment, and unknown disturbance or interference, as well as security concerns. On the other hand, the emerging reconfigurable intelligent surface (RIS) technique is a promising solution to significantly enhance the quality (e.g. latency reduction, reliability improvement, etc.) of traditional wireless networks and provide security especially under a complex dynamic wireless environment such as manufacturing factories. Therefore, the goal of this project is to provide a novel framework of hardware-driven online learning and optimization of RIS-enhanced industrial wireless networks. To achieve this goal, the proposed research will provide critical components in facilitating the reliable and optimal design of industrial wireless networks for both stationary and mobile users and fostering their adoption. The research is also complemented by a comprehensive educational plan including curriculum development, lab enhancements, as well as involving undergraduate and graduate students in research. Diverse outreach activities have been planned to engage K-12 and underrepresented students from two HBCUs, one MSI, and other institutions. This research will develop foundational analytical and experimental approaches for reconfigurable intelligent surface (RIS) hardware-driven cross-layer optimization and data-enabled online learning algorithm development. The project will provide several novel contributions, including 1) A new type of hardware-driven cross-layer optimization for the RIS-assisted industrial wireless network under unknown disturbance, 2) A novel real-time data-enabled learning approach that can solve the complex cross-layer optimization under harsh constraints, 3) A robust and computationally efficient learning framework that can optimize the large scale RIS-enhanced wireless network in a distributed fashion, and 4) Design and fabrication of a RIS unit that supports a dynamic beam steering capability, as well as a hardware testbed for evaluating the developed RIS-enhanced industrial wireless network in practical settings. Moreover, this project will lead a new direction in industrial wireless network optimization, machine learning, and resilient computing and further pave the way for real-time learning-based optimization development and implementation. The proposed research will contribute to future wireless revolution and advanced manufacturing which are of national priority.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.