Date of Award

Summer 2024

Language

English

Embargo Period

7-15-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Computer Science

Program

Computer Science

First Advisor

Dr. Mariya Zheleva

Committee Members

Dr. Mariya Zheleva, Dr. Petko Bogdanov, Dr. Jeong-Hyon Hwang, Dr. Mila Gasco

Keywords

Spectrum Sharing, Coexistence, Rural Areas, TVWS, CBRS

Subject Categories

Digital Communications and Networking | Signal Processing | Systems and Communications

Abstract

The Radio Frequency (RF) spectrum is scarce and to make it available for new mobile wireless services, regulators are forced to re-allocate spectrum from existing services or develop mechanisms to share spectrum with new entries. Television White Space (TVWS) and Citizen Broadband Radio Service (CBRS) are two examples of recently commercialized spectrum sharing technologies. TVWS enables sharing among fixed wireless broadband technologies (secondary users) and terrestrial TV broadcast services (primary users). CBRS enables spectrum sharing among 5G/LTE (secondary users) and naval radar (primary users). With both technologies, a central database determines when it is safe for secondary users to operate without interfering with primary licensees. While these are just two early examples, spectrum sharing, and coexistence will be the future of wireless networks, as all stakeholders’ needs and capabilities expand. Current sharing mechanisms are technology specific and incur serious inefficiencies in spatial reuse of the spectrum due to overly conservative resource allocations. Thus, future efficient spectrum sharing will require algorithms to facilitate coexistence through mutual awareness. However, currently we lack sufficient analytic capabilities to support transmitter detection, particularly for short-lived fleeting transmitters, which poses challenges in sharing spectrum with entities such as naval or weather radar. Additionally, analysis outcomes for mutual awareness are as insightful as the quality of the data that supports them, and there are currently no mechanisms to quantify spectrum data fidelity. Finally, even when sharing is granted, we need ways to accelerate data transmission through optimal rate adaptation to ensure that multiple clients can utilize spectrum-sharing networks.

The goal of this dissertation is to develop algorithms for spectrum awareness to facilitate spectrum management and coexistence with an outlook towards equitable access. To address inefficiencies in spectrum sharing, we develop a reinforcement learning rate adaptation algorithm for mobile access in TVWS networks. Efficient rate adaptation will minimize the time to transmit and will ensure more time is available on a given band for other clients. To aid practical mutual awareness, we develop a general unsupervised algorithm for the detection of narrow-band and short-lived transmitters. We demonstrate its applicability in CBRS and show that it can reduce the interference protection zones along the US coast by a factor of three, and dramatically increase the spatial efficiency of spectrum reuse. To ensure accurate spectrum characterization, we develop a framework that attributes sensor properties and configuration to spectrum data fidelity and models the relationship between spectrum analytics performance and data quality. This is the first work that can quantify the fidelity of data from a spectrum trace. Finally, we propose an adaptive closed loop approach to dynamic and data driven policy with a call to action to the spectrum community. Our work has been informed by the needs of rural and infrastructure-challenged areas and our research outcomes have been field-tested in collaboration with rural communities in Upstate New York. We design solutions for challenges associated with information access through mutually-aware spectrum sharing technologies. While the focus of analysis in this dissertation is on TVWS and CBRS technology, our proposed methodologies are more general and can be applied to any future spectrum sharing technologies.

License

This work is licensed under the University at Albany Standard Author Agreement.

Download

Share

COinS