IMR: MM-1A: Evolutionary Modeling and Acquisition of Multidimensional 5G Internet Measurements

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Commercial 5G networks are being quickly rolled out in the U.S. The high-throughput, low-latency natures of 5G enable numerous exciting applications, such as cloud/edge assisted machine learning, networked virtual/augmented reality, connected and autonomous vehicles, low-latency IoT applications, and digital agriculture. However, despite 5G’s potential, the research community still lacks a thorough understanding of 5G performance in the wild in the following aspects: (1) unlike its predecessors, 5G encompasses more diverse technologies; (2) the underlying data patterns are often time-varying at different scales; and (3) Scientists often have limited resources to model and acquire multidimensional 5G measurements. An overarching goal of this project is to develop novel statistical methods for modeling complex internet measurements and designing data collection under practical constraints. The proposed research is expected to have a broader impact on the practice and education across statistics, machine learning, signal processing, internet data analysis, and data privacy. The project will integrate the materials developed by this project into courses in statistics and computer science. In addition, the project will actively outreach to local high schools and colleges to organize workshops or summer camps for underrepresented minorities in STEM and engage them in hands-on learning projects.In this project, the cross-disciplinary team aims to significantly advance the fundamental understanding of the modeling and sampling of 5G measurements. The PI and Co-PIs will leverage their expertise to develop learning frameworks, advanced algorithms, and analysis techniques for internet measurements in two interconnected research thrusts. First, evolutionary space-time modeling, an innovative and principled framework for statistical modeling of the 5G internet measurements across space and time will be developed. This modeling paradigm can flexibly incorporate modern nonparametric supervised learning techniques and perform online computation/updating of the model. Second, an influence-based approach to data acquisition, in which system designers can address various constraints, such as variable sparsity and data privacy, will be developed. The research outcomes will offer valuable and practically powerful tools for scientists to understand the 5G internet from streaming data across a large span of space and time.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.