A belief-based decision-making framework for spectrum selection in cognitive radio networks
This paper presents a comprehensive cognitive management framework for spectrum selection in cognitive radio networks. The framework uses a belief vector concept as a means to predict the interference affecting the different spectrum blocks and relies on a smart analysis of the scenario dynamicity t...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/89370 |
| Acceso en línea: | https://hdl.handle.net/2117/89370 https://dx.doi.org/10.1109/TVT.2015.2508646 |
| Access Level: | acceso abierto |
| Palabra clave: | Cognitive radio networks Belief vector Cognitive radio Spectrum selection Testbed Ràdio cognitiva Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica |
| Sumario: | This paper presents a comprehensive cognitive management framework for spectrum selection in cognitive radio networks. The framework uses a belief vector concept as a means to predict the interference affecting the different spectrum blocks and relies on a smart analysis of the scenario dynamicity to properly determine an adequate observation strategy to balance the trade-off between achievable performance and measurement requirements. In this respect, the paper shows that the interference dynamics in a given spectrum block can be properly characterized through the second highest eigenvalue of the interference state transition matrix. Therefore, this indicator is retained in the proposed framework as a relevant parameter to drive the selection of both the observation strategy and spectrum selection decision-making criterion. The paper evaluates the proposed framework to illustrate the capability to properly choose among a set of possible observation strategies under different scenario conditions. Furthermore, a comparison against other state-of-the-art solutions is presented. |
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