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

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.