Graduation Year

2020

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Electrical Engineering

Major Professor

Ismail Uysal, Ph.D.

Co-Major Professor

Kwang-Cheng Chen, Ph.D.

Committee Member

Gisele Bennett, Ph.D.

Committee Member

Giovanni Luca Ciampaglia, Ph.D.

Committee Member

Robert Hooker, Ph.D.

Committee Member

Ravi Sankar, Ph.D.

Keywords

Aerial Wireless Networks, Inter-agent Communication, Mobile Wireless Networks Deployment, Mobility Networks, Reinforcement Learning

Abstract

Service demand patterns for wireless networks are evolving with the technological developments in areas such as personal computing, unmanned vehicles, and internet-of-things, where increasing mobile service demand is one of the significant challenges introduced. In addition to these new intrinsic dynamics, natural disasters and societal upheaval are also disrupting the conventional patterns of network demand. Situations like damaged infrastructure due to a natural disaster or large numbers of displaced people caused by political strife and social upheaval demand flexible, rapidly deployable network architectures. The increasing demands of next-generation communication services are straining the capabilities of the traditional approach of the permanent deployments of fixed infrastructure, presenting a need for novel approaches.

The availability of previously unprecedented data sources also brings data-driven approaches to the spotlight. Having access to large-scale longitudinal datasets of network user behavior makes it possible to study and model user behavior which can then be used to improve services provided to users.

This dissertation has its primary contributions in socially aware analysis of cellular network usage data and developing methods for forming flexible aerial wireless network infrastructures in applications challenging for traditional infrastructure deployments. Specifically, this dissertation first presents an investigation of the wireless network user data to generate actionable intelligence that can guide policymakers and point out the changing demands of wireless networks discovering user modalities outside the common user patterns and then explores the use of aerial platforms for rapid deployment of flexible wireless networks to satisfy the mobility and reliability demands of the future wireless communications.

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