NSF Org:	CNS Division Of Computer and Network Systems
Recipient:	REGENTS OF THE UNIVERSITY OF MICHIGAN
Initial Amendment Date:	July 24, 2023
Latest Amendment Date:	August 23, 2024
Award Number:	2323174
Award Instrument:	Continuing Grant
Program Manager:	Deepankar Medhi dmedhi@nsf.gov  (703)292-2935 CNS  Division Of Computer and Network Systems CSE  Directorate for Computer and Information Science and Engineering
Start Date:	November 1, 2023
End Date:	October 31, 2025 (Estimated)
Total Intended Award Amount:	$550,000.00
Total Awarded Amount to Date:	$550,000.00
Funds Obligated to Date:	FY 2023 = $279,913.00 FY 2024 = $270,087.00
History of Investigator:	Zhuoqing Mao (Principal Investigator) zmao@umich.edu Feng Qian (Co-Principal Investigator) Eman Ramadan (Co-Principal Investigator)
Recipient Sponsored Research Office:	Regents of the University of Michigan - Ann Arbor 1109 GEDDES AVE STE 3300 ANN ARBOR MI  US  48109-1015 (734)763-6438
Sponsor Congressional District:	06
Primary Place of Performance:	Regents of the University of Michigan - Ann Arbor 503 THOMPSON STREET ANN ARBOR MI  US  48109-1340
Primary Place of Performance Congressional District:	06
Unique Entity Identifier (UEI):	GNJ7BBP73WE9
Parent UEI:
NSF Program(s):	Networking Technology and Syst
Primary Program Source:	01002324DB NSF RESEARCH & RELATED ACTIVIT 01002425DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	115Z, 7363
Program Element Code(s):	736300
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Commercial 5G networks are becoming more widely available. Their key advantage is much higher speeds, enabling emerging applications such as multimedia streaming, VR/AR (virtual reality/augmented reality), and autonomous vehicles, to name a few. Measuring the performance of these networks and emerging applications becomes important to understand how they function and to identify areas of improvement especially for designing the next generation of such technologies. This collaborative project brings together investigators from the University of Michigan and University of Minnesota to create a platform for measuring the performance of 5G, 6G, (i.e., xG) networks and show the changes over time along with the performance of the emerging applications.

xGTracker is modular, extensible, configurable, cross-technology, and application-centric measurement platform. First, xGTracker has several configurable components and enables researchers to add/replace components. It is capable of selecting available radio bands/technologies to conduct measurements. Second, xGTracker will integrate existing real open-source applications as well as generating different workloads to emulate others for collecting application Quality of Experience metrics. Third, xGTracker allows for dynamic server selection based on several parameters such as (location, carrier, and workload). This helps understand the impact of server location on the collected metrics. Fourth, XGTracker will report energy consumption for different system/device components which enables monitoring the energy consumption for emerging applications. Finally, xGTracker fully considers user privacy allowing users to choose what and how to share their data.

The broader impact of the project has multiple dimensions. First, xGTracker provides tools that measure and characterize the performance of commercial xG networks. This will benefit xG customers, application developers, and xG carriers. XGTracker has the potential to be integrated with industrial collaborators improving the quality of experience for hundreds of millions of xG users in the future. Second, xGTracker presents an opportunity to integrate research and education. It will contribute new content to networking and mobile courses taught and help design various course projects. xGTracker will also be used to show 5G technology to students especially from underrepresented groups and simulate their interest in STEM.

The repository for the xGTracker Platform is at https://github.com/xGTracker-Platform. The project is expected to be open-source under a BSD-style permissive free software license for other researchers to contribute to it and add new features. The data collected through the xGTracker platform will then be made available through different performance maps to show the performance of the different technologies over time as well as the quality of experience for different applications.

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.

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