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: 2128482
• 负责人: Lijun Qian
• 金额: $ 20万
• 依托单位: Prairie View A & M University
• 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=2128482&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 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.

下一代智能工厂需要一个高质量、可靠的无线网络，能够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，因为它们在受限的工厂空间、不确定的无线环境、未知的干扰或干扰以及安全方面的严格的延迟和可靠性要求。另一方面，新兴的可重构智能表面(RIS)技术是一种很有前途的解决方案，可以显著提高质量(如减少延迟、提高可靠性等)。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，本项目的目标是为RIS增强的工业无线网络提供一种硬件驱动的在线学习和优化的新框架。为了实现这一目标，拟议的研究将提供关键组成部分，以促进为固定和移动用户可靠和最佳地设计工业无线网络，并促进其采用。这项研究还得到了一项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。已计划开展不同的外联活动，以吸引来自两所HBCU、一所MSI和其他机构的K-12和代表性不足的学生。这项研究将为可重构智能表面(RIS)硬件驱动的跨层优化和数据使能的在线学习算法开发开发基础的分析和实验方法。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RIS辅助的工业无线网络在未知干扰下，2)一种新的实时数据使能学习方法可以解决苛刻约束下的复杂的跨层优化问题，3)一个健壮的和计算高效的学习框架，可以以分布式的方式优化大规模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，以及用于在实际环境中评估所开发的RIS增强的工业无线网络的硬件试验台。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，进一步为基于实时学习的优化开发和实施铺平道路。这项拟议的研究将有助于国家优先考虑的未来无线革命和先进制造。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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

Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks

• DOI: 10.1109/twc.2022.3178445
• 发表时间: 2022-05
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Bofu Yang;Xuelin Cao;Chongwen Huang;C. Yuen;M. D. Renzo;Yong Liang Guan;D. Niyato;Lijun Qian;M. Debbah

Joint Optimal Placement and Dynamic Resource Allocation for multi-UAV Enhanced Reconfigurable Intelligent Surface Assisted Wireless Network

多无人机增强可重构智能地面辅助无线网络联合优化布局与动态资源分配

• DOI: 10.1109/ccnc51644.2023.10059677
• 发表时间: 2023
• 期刊: The Proceedings of IEEE ROBOCOM 2023
• 作者: Zhang, Yuzhu;Qian, Lijun;Xu, Hao
• 通讯作者: Xu, Hao