Coverage optimization and power reduction in SFN using simulated annealing
An approach that predicts the propagation, models the terrestrial receivers and optimizes the performance of single frequency networks (SFN) for digital video broadcasting in terms of the final coverage achieved over any geographical region, enhancing the most populated areas, is proposed in this pa...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/9973 |
| Acceso en línea: | http://hdl.handle.net/10902/9973 |
| Access Level: | acceso abierto |
| Palabra clave: | DVB systems Propagation prediction Simulated annealing Single frequency network optimization |
| Sumario: | An approach that predicts the propagation, models the terrestrial receivers and optimizes the performance of single frequency networks (SFN) for digital video broadcasting in terms of the final coverage achieved over any geographical region, enhancing the most populated areas, is proposed in this paper. The effective coverage improvement and thus, the self-interference reduction in the SFN is accomplished by optimizing the internal static delays, sector antenna gain, and both azimuth and elevation orientation for every transmitter within the network using the heuristic simulated annealing (SA) algorithm. Decimation and elevation filtering techniques have been considered and applied to reduce the computational cost of the SA-based approach, including results that demonstrate the improvements achieved. Further representative results for two SFN in different scenarios considering the effect on the final coverage of optimizing any of the transmitter parameters previously outlined or a combination of some of them are reported and discussed in order to show both, the performance of the method and how increasing gradually the complexity of the model for the transmitters leads to more realistic and accurate results. |
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