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

随着大规模、异构、动态和复杂的无线网络的日益增长，如何在5G网络及以后实现准确和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的解决方案是黑盒方法，这可能不是鲁棒性和自适应性，并且仅适用于低维和离散数据。实际上，无线数据属于函数数据类，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，本项目旨在弥合基于FDA的学习和无线测量之间的差距。拟议的研究福尔斯分为以下四个相互交织的推力。(i)稀疏无线测量的函数数据回归：开发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的动态无线测量迁移学习：研究在测试数据和训练数据之间的分布偏移下，用于功能数据回归和分类的迁移学习，以实现动态环境中的有效无线测量。(iii)基于分位数FDA的学习，用于稳健的无线测量和控制：开发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。(iv)用于集成和验证的无线测量应用。如果成功的话，这项研究将大大推进无线测量和相关领域的实践和理解的功能数据。教育和外联部分包括：㈠利用学习理论和FDA加强课程设置，并联合开发一门基于FDA的无线测量学习研究生课程。(ii)让本科生参与动手项目。现有的推广计划将被利用，以提供研究机会和研讨会的本科生，重点是从事代表性不足的学生。(iii)提高公众意识的外联活动包括期刊出版物、会议演讲、研讨会、IEEE杰出讲座、期刊特刊以及研讨会和主要会议的特别会议。该项目产生的代码将在公共存储库GitHub（https：//github.com/）上传播。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。该项目网站将经常定期更新，以传播该项目的成果，包括项目描述、项目团队、主要成果（如出版物、代码和数据集）以及NSF对该项目的支持。该网站将在三年的项目期内由PI管理/更新。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With the increasing 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 DynamicWireless 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 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.