Collaborative Research: IMR: MM-1A: Functional Data Analysis-aided Learning Methods for Robust Wireless Measurements

合作研究：IMR：MM-1A：用于稳健无线测量的功能数据分析辅助学习方法

• 批准号: 2319342
• 负责人: Shiwen Mao
• 金额: $ 40万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319342&HistoricalAwards=false
• 关键词: Collaborative; Research; IMR; MM; 1A

With the growth of large-scale, heterogeneous, dynamic, and complex wireless networks, how to achieve accurate and robust measurements in 5G networks and beyond becomes a challenging and important problem. Most existing data-driven solutions are black-box approaches, which may not be robust and adaptive, and work only for low-dimensional and discrete data. In fact, wireless data belong to the class of functional data, which can be represented by curves or functions. High-dimensional wireless datasets can be better handled by functional data analysis (FDA). Recognizing the significance of the aforementioned problems, this project aims to bridge the gap between FDA-based learning and wireless measurement. The proposed research falls into the following four interwoven thrusts. (i) Functional Data Regression for Sparse Wireless Measurements: to develop a deep learning-based approach to address fundamental regression problems of functional data. (ii) FDA-based Transfer Learning for Dynamic Wireless Measurements: to study transfer learning for functional data regression and classification under the distribution shift between test data and training data for effective wireless measurements in dynamic environments. (iii) Quantile FDA-based Learning for Robust Wireless Measurements and Control: to develop a deep learning-based approach to address the fundamental bottleneck of quantile regression-based methods. (iv) Wireless Measurement Applications for Integration and Validation. If successful, this research will greatly advance the practice and understanding of functional data for wireless measurement and related fields. The educational and outreach components include: (i) Curriculum enhancement with learning theory and FDA, and joint developing a graduate course on FDA-based learning for wireless measurements. (ii) Engaging undergrads with hands-on projects. The existing outreach programs will be leveraged to offer research opportunities and seminars to undergrads, with emphasis on engaging underrepresented students. (iii) Outreach activities to increase public awareness, include journal publications, conference presentations, seminars, IEEE distinguished lectures, journal special issues, and workshops and special sessions at major conferences.The code produced from this project will be disseminated at the public repository GitHub (https://github.com/). A project website will be maintained at Auburn University with URL: https://www.eng.auburn.edu/~szm0001/proj_lMR23.html. This project website will be frequently and regularly updated for dissemination of the outcomes from this project, including a description of the project, project team, major outcomes such as publications, codes and datasets, as well as an acknowledgement of NSF support to this project. This website will be managed/updated by the PI for the three-year project period. This project is jointly funded by the Networking Technology and Systems (NeTS) program, the Established Program to Stimulate Competitive Research (EPSCoR), the Statistics program in the Division of Mathematical Sciences (DMS), and the Computing and Communication Foundations Division.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.

随着大规模、异构、动态和复杂无线网络的发展，如何在 5G 网络及以后的网络中实现准确、鲁棒的测量成为一个具有挑战性的重要问题。大多数现有的数据驱动解决方案都是黑盒方法，可能不具有鲁棒性和适应性，并且仅适用于低维和离散数据。事实上，无线数据属于函数数据类，可以用曲线或函数来表示。高维无线数据集可以通过功能数据分析（FDA）更好地处理。认识到上述问题的重要性，该项目旨在弥合基于 FDA 的学习和无线测量之间的差距。拟议的研究分为以下四个相互交织的主旨。 (i) 稀疏无线测量的功能数据回归：开发一种基于深度学习的方法来解决功能数据的基本回归问题。 (ii) 基于 FDA 的动态无线测量迁移学习：研究在测试数据和训练数据之间的分布变化下功能数据回归和分类的迁移学习，以便在动态环境中进行有效的无线测量。 (iii) 基于分位数 FDA 的鲁棒无线测量和控制学习：开发基于深度学习的方法来解决基于分位数回归方法的基本瓶颈。 (iv) 用于集成和验证的无线测量应用。如果成功，这项研究将极大地促进无线测量及相关领域功能数据的实践和理解。教育和推广部分包括： (i) 通过学习理论和 FDA 加强课程，并联合开发基于 FDA 的无线测量学习研究生课程。 (ii) 让本科生参与实践项目。将利用现有的外展计划为本科生提供研究机会和研讨会，重点是吸引代表性不足的学生。 (iii) 提高公众意识的外展活动，包括期刊出版物、会议演讲、研讨会、IEEE 杰出讲座、期刊特刊以及主要会议的研讨会和特别会议。该项目生成的代码将在公共存储库 GitHub 上传播（https://github.com/）。奥本大学将维护一个项目网站，网址为：https://www.eng.auburn.edu/~szm0001/proj_lMR23.html。该项目网站将经常更新，以传播该项目的成果，包括项目的描述、项目团队、出版物、代码和数据集等主要成果，以及对 NSF 对该项目支持的认可。该网站将由 PI 在三年项目期间进行管理/更新。该项目由网络技术和系统（NeTS）计划、刺激竞争研究既定计划（EPSCoR）、数学科学部（DMS）统计计划以及计算和通信基础部共同资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。