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

随着大规模、异质、动态和复杂的无线网络的发展，如何在5G网络及更远的地方实现准确和健壮的测量成为一个具有挑战性的重要问题。现有的大多数数据驱动的解决方案都是黑盒方法，这种方法可能不健壮和自适应，并且仅适用于低维和离散数据。事实上，无线数据属于功能类数据，可以用曲线或函数来表示。功能数据分析(FDA)可以更好地处理高维无线数据集。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推动力。(I)用于稀疏无线测量的函数数据回归：开发一种基于深度学习的方法来解决函数数据的基本回归问题。(Ii)基于FDA的动态无线测量的转移学习：研究动态环境下测试数据和训练数据分布漂移下的函数数据回归和分类的转移学习。(Iii)基于分位数FDA的稳健无线测量和控制学习：开发一种基于深度学习的方法，以解决基于分位数回归的方法的根本瓶颈。(4)用于集成和验证的无线测量应用程序。如果研究成功，将极大地促进功能数据在无线测量及相关领域的实践和理解。教育和外展部分包括：(I)与学习理论和FDA一起加强课程，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。现有的外展计划将被用来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。(3)为提高公众认识而开展的外联活动，包括期刊出版物、会议介绍、研讨会、电气和电子工程师协会杰出讲座、期刊特刊、讲习班和主要会议的特别会议。该项目编制的代码将在公共资料库GitHub(https://github.com/).)分发将在奥本大学维护一个项目网站，网址为：https://www.eng.auburn.edu/~szm0001/proj_lMR23.html.该项目网站将经常和定期更新，以传播该项目的成果，包括对项目、项目团队、出版物、代码和数据集等主要成果的描述，以及对国家科学基金会对该项目的支持的确认。在为期三年的项目期内，该网站将由PI管理/更新。该项目由网络技术与系统(NETS)计划、已建立的促进竞争研究计划(EPSCoR)、数学科学部(DMS)的统计计划以及计算和通信基础分部联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.