ERI: Empowering Data-Driven Resource Management in Indoor 5G+ Wireless Networks

Tennessee Technological University, Cookeville TN

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Future trends in data traffic require high-quality wireless connections of multi-gigabits per second rates and less than ten milliseconds delay. However, the conventional radio spectrum is quite congested, and hence, it cannot satisfy such high demands. Consequently, unused high-frequency bands will be adopted in the fifth generation and beyond (5G+) wireless networks. Yet, wireless connectivity in high bands is challenged by frequent outages induced by user mobility. Recent studies show that advanced network management techniques based on artificial intelligence can maintain a reliable high-quality link with user mobility. However, to develop such advanced techniques, comprehensive highly-accurate datasets of wireless channel quality are required. Unfortunately, these datasets are not accessible to the research community. The first goal of this project is to develop a realistic and highly-accurate simulator that generates rich datasets of 5G+ wireless channels in the frequency range 400 – 800 Terahertz. This simulator will be made publicly available to empower research efforts in data-driven 5G+ network management solutions. The generated datasets will be validated through a state-of-the-art testbed. Moreover, the generated datasets will be characterized to learn the impact of user mobility patterns on the wireless channel quality. In addition, novel methods will be developed to predict the channel quality due to user mobility, which will further help in developing effective 5G+ network management tools. By empowering future research in data-driven network management solutions, this project enables a broad integration of high-frequency bands in 5G+ wireless networks. As a result, this project supports high-rate low-delay 5G+ technologies, much needed in the era of smart and connected communities and the internet of everything. Thereby, this project broadly impacts myriad aspects of the evolving digital society, particularly, for indoor mobile applications. Furthermore, this project provides workforce training in a highly desirable multi-disciplinary skillset while ensuring the participation of a broad range of students. The 5G+ wireless networks will operate in the unused high-frequency bands, e.g., the visible light (VL) frequency band (400 – 800 Terahertz). While they can support ultra-high throughput and ultra-low latency traffic demands, the wireless channels at such bands suffer from limited diffraction capabilities. This results in frequent outages in communication links with user mobility due to blockage from static and/or mobile objects. Preliminary studies demonstrated that it is not practical to describe these link outages using a general probability distribution model, as such outages are tied to the environment-confined user mobility details. As a result, classical optimization tools will be ineffective for 5G+ network management. On the other hand, data-driven strategies can be used to design intelligent network management strategies that learn from the environment and adaptively allocate resources to the mobile users. However, adopting data-driven network management strategies is challenged by: 1) the absence of high-quality datasets of indoor mobile VL channel gains and 2) the sparsity of the VL channel gain data, which impedes the adoption of conventional data-driven tools. To address these limitations, the proposed project pursues the following research thrusts while considering office room layouts: T1) Development of efficient 5G+ mobile channel simulator that reflects realistic spatio-temporal features in the VL band and captures the impact of link unavailability due to dynamic blockages with the objects and users' bodies. The simulator will be publicly available to empower further research in 5G+ data-driven network management; T2) Development of an efficient 5G+ channel predictor that provides useful VL channel state information for future time frames despite the high sparsity in the channel dataset. The predictor will empower various proactive data-driven 5G+ network management strategies. The developed methods and tools in this project will be validated through a testbed that mimics a VL-based indoor networking setup. 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.