An application of reinforcement learning for efficient spectrum usage in next-generation mobile cellular networks
This paper proposes reinforcement learning as a foundational stone of a framework for efficient spectrum usage in the context of nextgeneration mobile cellular networks. The objective of the framework is to efficiently use the spectrum in a cellular orthogonal frequency-division multiple access netw...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2010 |
| 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:2117/8321 |
| Acceso en línea: | https://hdl.handle.net/2117/8321 https://dx.doi.org/10.1109/TSMCC.2010.2041230 |
| Access Level: | acceso abierto |
| Palabra clave: | Cell phones Signal theory (Telecommunication) Telefonia mòbil Senyal, Teoria del (Telecomunicació) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| Sumario: | This paper proposes reinforcement learning as a foundational stone of a framework for efficient spectrum usage in the context of nextgeneration mobile cellular networks. The objective of the framework is to efficiently use the spectrum in a cellular orthogonal frequency-division multiple access network while unnecessary spectrum is released for secondary spectrum usage within a private commons spectrum accessmodel. Numerical results show that the proposed framework obtains the best performance compared with other approaches for spectrum assignment. Moreover, the framework is relatively simple to implement in terms of computational requirements and signaling overhead. |
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