Date of Award

1-1-2023

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Electrical and Computer Engineering

Content Description

1 online resource (xviii, 359 pages) : illustrations (some color)

Dissertation/Thesis Chair

Aveek Dutta

Committee Members

Dola Saha, Hany Elgala, Ngwe Thawdar

Keywords

Adaptive Signal Processing, Artificial Intelligence, Blockchain, Coexistence, Multimodal, Next-Generation Networks, Wireless LANs

Subject Categories

Electrical and Electronics

Abstract

Next Generation (xG) wireless networks are poised to revolutionize the way people, devices, data and processes sense, communicate, interact, and collectively enable a wide range of emerging applications, ranging from smart cities, connected healthcare, and advanced vehicular communication to extended reality. To facilitate this seamless interoperability, these networks need to evolve to accommodate and integrate multiple modalities in communication/ sensing technologies and spectrum, heterogeneous networks, trends in signal-processing (statistical, AI-driven, and distributed systems), centralized and distributed architectures, and device/ network hardware resources. However, to cater to the high-target metrics and wide-range of applications, these multi-modal networks must efficiently address multi-faceted challenges in coexistence to cope with spectrum scarcity and growing density of wireless entities, adaptability to dynamic and highly-dimensional wireless environments, and security in de-centralized networks. The thesis explore novel techniques, architectures, and validation methodologies to advance the design, implementation, and evaluation of multimodal xG networks, and is structured around three primary research threads: 1) Cross-Layer signal processing for spectrum/ network coexistence, 2) Adaptive signal processing that generalizes to a variety of wireless environments, and 3) Design of pioneering infrastructure and testbeds that enable practical research and validation of multi-modal xG networks.

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