Source Separation using Sparse Bayesian Learning

Wireless communication in recent decades has allowed for a substantial increase in both the speed and capacity of information which may be transmitted over large distances. However, given the expanding societal needs coupled with a finite available spectrum, the question arises of how to increase the efficiency by which information may be transmitted. One natural answer to this question lies in spectrum sharing—that is, in allowing multiple noncooperative agents to inhabit the same spectrum bands. In order to achieve this, we must be able to reliably separate the desired signals from those of other agents in the background. However, since our agents are noncooperative, we must develop a model-agnostic approach at tackling this problem. For this work, we will consider cohabitation between radar signals and communication signals, with the former being the desired signal and the latter being the noncooperative agent. In order to approach such problems involving highly underdetermined linear systems, we propose utilizing Sparse Bayesian Learning and present our results on selected problems.

Grantor dc:publisher

University of Kansas

Year dc:date.issued

2025

Author dc:creator

• El-Katri, Faris Ahmed

Advisor dc:contributor.advisor

• McCormick, Patrick M.

• Electrical engineering
• Deconvolution
• Radar Signal Processing
• Sparse Bayesian Learning

Statement dc:rights

• This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.

Language dc:language.iso

en

Dc Identifier Other

https://www.proquest.com/LegacyDocView/DISSNUM/31939996

OAI identifier oai:identifier

oai:kuscholarworks.ku.edu:1808/37660