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的广泛信息交换。然而，传统的无线网络技术不能直接应用于制造工厂，由于其严格的延迟和可靠性要求，在有限的工厂空间，不确定的无线环境，和未知的干扰或干扰，以及安全问题。另一方面，新兴的可重构智能表面（RIS）技术是一种有前途的解决方案，以显着提高质量（例如，延迟减少，可靠性提高等）。并提供安全性，特别是在复杂的动态无线环境下，如制造工厂。因此，本项目的目标是提供一个新的硬件驱动的在线学习和优化的RIS增强型工业无线网络的框架。为了实现这一目标，拟议的研究将提供关键组件，促进工业无线网络的可靠和优化设计的固定和移动的用户，并促进其采用。该研究还辅之以全面的教育计划，包括课程开发，实验室增强，以及参与研究的本科生和研究生。已计划开展各种外联活动，以吸引K-12和来自两个HBCU，一个MSI和其他机构的代表性不足的学生。 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基础分析和实验方法。该项目将提供几个新的贡献，包括1）一种新型的硬件驱动的跨层优化，用于未知干扰下的RIS辅助工业无线网络，2）一种新型的实时数据支持的学习方法，可以解决苛刻约束下的复杂跨层优化，3）鲁棒且计算高效的学习框架，其可以以分布式方式优化大规模RIS增强的无线网络，以及4）设计和制造支持动态波束控制能力的RIS单元，以及用于在实际设置中评估所开发的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。这项研究将有助于未来的无线革命和先进制造业，这是国家的优先事项。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的知识价值和更广泛的影响审查标准的支持。

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.