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

合作研究：SWIFT：先进制造可重构智能表面工业无线网络中的数据驱动学习和优化

• 批准号: 2128656
• 负责人: Hao Xu
• 金额: $ 20万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128656&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; Data; Driven

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 algorithms. 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.

下一代智能工厂需要一个高质量且可靠的无线网络，该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。但是，传统的无线网络技术无法直接应用于制造工厂，因为它们在狭窄的工厂空间，不确定的无线环境以及未知的干扰或干扰以及安全问题中的严格延迟和可靠性要求。另一方面，新兴的可重构智能表面（RIS）技术是一种有希望的解决方案，可显着提高传统无线网络的质量（例如，降低延迟，可靠性提高等），并在复杂的动态无线环境（例如制造工厂）下提供安全性。因此，该项目的目的是提供由硬件驱动的在线学习和RIS增强工业无线网络优化的新颖框架。为了实现这一目标，拟议的研究将提供关键的组成部分，以促进固定用户和移动用户的工业无线网络的可靠设计，并促进其采用。一项全面的教育计划还补充了这项研究，包括课程开发，实验室增强功能以​​及涉及本科生和研究生研究。计划与来自两个HBCUS，一个MSI和其他机构的K-12和代表性不足的学生与K-12和不足的学生互动。 这项研究将开发可重新配置智能表面（RIS）硬件驱动的跨层优化和支持数据的在线学习算法开发的基础分析和实验方法。 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支持动态梁转向能力的RIS单元，以及用于评估实际设置中开发的RIS增强工业无线网络的硬件测试台。此外，该项目将在工业无线网络优化，机器学习和弹性计算方面引导新的方向，并为基于实时学习的优化算法铺平道路。拟议的研究将有助于未来的无线革命和高级制造业，这是国家优先事项。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准来评估的。

项目成果

Reinforcement Learning based Optimal Dynamic Resource Allocation for RIS-aided MIMO Wireless Network with Hardware Limitations

基于强化学习的 RIS 辅助硬件限制 MIMO 无线网络最优动态资源分配

• DOI: 10.1109/icnc57223.2023.10074116
• 发表时间: 2023
• 期刊: Networking and Communications (ICNC
• 作者: Zhang, Yuzhu;Qian, Lijun;Eroglu, Abdullah;Yang, Binbin;Xu, Hao
• 通讯作者: Xu, Hao

Data-Enabled Learning based Intelligent Resource Allocation for Multi-RIS Assisted Dynamic Wireless Network

基于数据支持学习的多RIS辅助动态无线网络智能资源分配

• DOI: 10.1109/gcwkshps56602.2022.10008775
• 发表时间: 2022
• 期刊: 2022 IEEE Globecom Workshops (GC Wkshps
• 作者: Zhang, Yuzhu;Xu, Hao
• 通讯作者: Xu, Hao

Optimal Resource Allocation for Reconfigurable Intelligent Surface Assisted Dynamic Wireless Network via Online Reinforcement Learning

• DOI: 10.1109/seconworkshops56311.2022.9926399
• 发表时间: 2022-09
• 期刊: 2022 IEEE International Conference on Sensing, Communication, and Networking (SECON Workshops)
• 作者: Yuzhu Zhang;Hao Xu