Narayanan, Arvind2022-06-082022-06-082021-09https://hdl.handle.net/11299/227910University of Minnesota Ph.D. dissertation. 2021. Major: Computer Science. Advisors: Zhi-Li Zhang, Feng Qian. 1 computer file (PDF); 246 pages.2019 marks the year when 5G services were rolled out commercially to consumers. 5G is expected to support sub-millisecond latency as well as ultra-high throughput of 20~Gbps that is a 100x improvement compared to its predecessor 4G/LTE. However, there exists a vacuum in understanding how 5G as a technology performs in-the-wild and whether it can fulfill its promises. Even after two years of being deployed commercially, the impact of 5G services on network performance or power consumption is not well understood. It is also unclear how applications and services today practically benefit from 5G technology. In an attempt to fill these voids, this dissertation is dedicated to examine the commercial 5G landscape ``from'' and ``across'' several key dimensions -- network performance, radio power characteristics, and quality-of-experience implications for mobile applications -- to provide key insights for interested stakeholders, and propose intelligent mechanisms for application developers to better leverage 5G technology that can aid in building future 5G-aware apps and services. To that end, using tools such as 5G Tracker developed in-house, we conducted a detailed measurement study to provide a first impression of the network performance characteristics of 5G services (including that of the much anticipated mmWave) and helped establish an important baseline for studying the evolution of 5G's performance. Subsequently, we expanded its scope and conducted the first comprehensive measurement study to investigate power consumption of 5G radio on commodity smartphones. Overall, our findings reveal key characteristics of commercial 5G services in terms of throughput, latency, performance bottlenecks, coverage, radio state transitions and radio power consumption under diverse scenarios, application performance (web-browsing, file-download, video streaming) with detailed comparisons to 4G/LTE networks. Furthermore, we also quantitatively reveal critical trade-offs (e.g. ultra-high vs. stable network performance, performance vs. energy) that get amplified with 5G and how challenges lie for upper layers (transport and up) to effectively utilize 5G. Leveraging the insights obtained from our measurement studies, we statistically reveal that, unlike in 4G/LTE, location alone is not sufficient for mapping 5G (especially mmWave) performance. We further proposed Lumos5G, a data-driven machine learning based framework for mmWave 5G throughput prediction that utilizes and fuses a variety of contextual features that can be collected on the smartphone, including its location, mobility information, geometric relationship with the 5G base station, and cellular connectivity information for predicting 5G throughput. We discuss how approaches like these provide opportunities as well as challenges in building future 5G-aware apps. In summary, this thesis contributes to better understanding the emerging commercial 5G landscape in the U.S. by systematically designing measurement methodologies and timely conducting experiments and analysis. With the rate at which 5G advancements and adoption are taking place, clearly 5G is on its evolutionary path for the next few years. The insights and challenges highlighted by our research will hold a mirror up to the implications of this new emerging technology. Artifacts of our research have been made available in the form of: datasets (5Gophers v1.0, Lumos5G v1.0, SIGCOMM21-5G v1.0), a smartphone-based measurement tool (5G Tracker) and a 5G mapping platform (5Gophers.umn.edu) for visualizing 5G coverage.en5G5G Tracker5GophersLumos5GMeasurementsThesis or Dissertation