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，动态和复杂的无线网络的增长，如何在5G网络及以后实现准确和健壮的测量结果成为一个具有挑战性和重要的问题。大多数现有数据驱动的解决方案是黑框方法，可能不健壮且自适应，并且仅适用于低维和离散数据。实际上，无线数据属于功能数据类，可以用曲线或功能表示。可以通过功能数据分析（FDA）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。拟议的研究属于以下四个交织的推力。 （i）稀疏无线测量值的功能数据回归：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）用于动态无线测量的基于FDA的转移学习：在测试数据和培训数据之间的分布变化下，研究转移学习，以进行功能数据回归和分类，以在动态环境中进行有效的无线测量。 （iii）基于分位数的基于FDA的学习，用于可靠的无线测量和控制：开发一种基于深度学习的方法来解决基于分位回归方法的基本瓶颈。 （iv）无线测量应用程序进行集成和验证。如果成功，这项研究将大大提高对无线测量和相关领域功能数据的实践和理解。教育和宣传组成部分包括：（i）通过学习理论和FDA来增强课程，以及共同开发有关基于FDA的学习研究生课程，以进行无线测量。 （ii）与动手项目一起吸引本科生。现有的外展计划将被利用，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）宣传活动以提高公众意识，包括期刊出版物，会议演讲，研讨会，IEEE杰出讲座，期刊特殊问题和讲习班以及在大型会议上的特殊会议。该项目制作的代码将在公共存储库Github（https:///github.com/）中传播。一个项目网站将在Auburn University使用url：https：//www.eng.auburn.edu/~szm0001/proj_lmr23.html。该项目网站将经常且定期更新以传播该项目的结果，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，以及对该项目的NSF支持的确认。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）计划共同资助，启发竞争研究的既定计划（EPSCOR），数学科学部（DMS）（DMS）的统计计划以及计算和通信基金会部门。该奖项反映了NSF的法定任务，并通过评估了基金会的crtulial and crowia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia和Broadia and Broadia and Broadia and Broadia and Broadia and broadia and Broadia。