Literature Database Entry
haertel2024performance
Vinzenz Härtel, "Performance evaluations of various cooperative spectrum sensing algorithms," Bachelor Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), November 2024. (Advisor: Rathinamala Vijay; Referees: Falko Dressler and Thomas Sikora)
Abstract
This thesis investigates the performance of various cooperative spectrum sensing algorithms applied in Low-Earth-Orbit satellite communication systems. With the exceptional growth of satellite launches, efficient spectrum utilization has become crucial to mitigating communication bottlenecks. Through simulation-based evaluation, key spectrum sensing algorithms - including energy-detection, absolute-value-cumulation, robust-sensing, MinMax-sensing and Z-score-sensing - were analyzed across laplacian and gaussian noise models, signal types, and cooperation scenarios. Additionally, the thesis reviews a proposed channel model to simulate satellite-to-ground communication path loss. However, challenges in parameter estimation and reproducibility prevented its successful implementation and experimental application in Berlin. Results reveal that Robust-sensing and Z-score-sensing consistently achieve superior detection probabilities under varying signal-to-noise ratios, especially when complemented by cooperative methods like AND and Majority voting. The thesis provides a foundation for developing robust, scalable algorithms for dynamic, noise-prone environments, contributing to the efficient management of the radio frequency spectrum in satellite communication.
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Vinzenz Härtel
BibTeX reference
@phdthesis{haertel2024performance,
author = {H{\"{a}}rtel, Vinzenz},
title = {{Performance evaluations of various cooperative spectrum sensing algorithms}},
advisor = {Vijay, Rathinamala},
institution = {School of Electrical Engineering and Computer Science (EECS)},
location = {Berlin, Germany},
month = {11},
referee = {Dressler, Falko and Sikora, Thomas},
school = {TU Berlin (TUB)},
type = {Bachelor Thesis},
year = {2024},
}
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