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Title
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Sharing is caring : a machine-learning based management framework for efficient spectrum collaboration
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Author
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Abstract
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| Wireless communication technologies became a part of our modern society. Every year the number of wireless devices and wireless technologies increases. Cisco expects that around 25.4 and 42.6 billion wireless devices will be connected to the Internet in 2022. This growth introduces some major challenges. One of these challenges is to use the wireless spectrum, used by all of these wireless devices, more efficient, especially within the radio bands itself. To meet the demand of more wireless devices and higher throughput, new techniques are necessary to optimise the use of the wireless spectrum. Based on literature, it was expected that collaboration between neighboring wireless networks (from all kind of technologies) can improve the efficiency of the use of the wireless spectrum. Increasing spectrum efficiency can be accomplished in two ways: (i) the improvement of physical transmission, (ii) the use Artificial Intelligence (AI) to improve the decisions made by the wireless nodes. The improvement of the physical transmissions has a direct effect on the efficiency of the use of the spectrum. It is clear that the more efficient data can be transmitted, to improve the bits per Hertz, the less spectrum will be used for the same data. AI, on the other hand, gives us the opportunity make smarter decisions based on the physical limitations of the wireless system and behavior of the environment. The use of AI can also enable the possibility to start and maintain collaboration with other neighboring technologies to improve the efficiency of the wireless spectrum. This dissertation focuses on the contributions made for the AI decision engine for wireless network technologies. Within the context of this dissertation we focus on the use of AI to improve the decisions made by the wireless nodes. This dissertation provides multiple improvements to enable collaboration for wireless networks, which will lead to a more efficient use of the wireless spectrum. First, we describe a decision-making framework designed to enable AI-enabled algorithms within wireless radio stacks. All other algorithms described in this dissertation are implemented within the framework. Secondly, we present a spectrum prediction algorithm. This prediction algorithm is able to predict the behavior of neighboring wireless networks, even if insufficient information is available. This ability provides us to select better transmission moments. Finally, we introduce the policy-based flow selection algorithm. This algorithm is able to collaborate to improve the Quality of Service and optimize the spectrum footprint. |
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Language
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English
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Publication
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Antwerp
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University of Antwerp, Faculty of Science, Department of Computer Science
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2021
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Volume/pages
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173 p.
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Note
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Latré, Steven [Supervisor]
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Full text (open access)
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