Location area planning and cell-to-switch assignment in cellular networks
Location area (LA) planning plays an important role in cellular networks because of the tradeoff caused by paging and registration signalling. The upper boundary for the size of an LA is the service area of a mobile services switching center (MSC). In that extreme case, the cost of paging is at its...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2004 |
| 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:2117/176775 |
| Acceso en línea: | https://hdl.handle.net/2117/176775 https://dx.doi.org/10.1109/TWC.2004.827767 |
| Access Level: | acceso abierto |
| Palabra clave: | Wireless communication systems Intelligent networks Land mobile radio cellular systems Costs Paging strategies Bandwidth Switches Simulated annealing Quality of service Base stations Databases Comunicació sense fil, Sistemes de Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| Sumario: | Location area (LA) planning plays an important role in cellular networks because of the tradeoff caused by paging and registration signalling. The upper boundary for the size of an LA is the service area of a mobile services switching center (MSC). In that extreme case, the cost of paging is at its maximum but no registration is needed. On the other hand, if each cell is an LA, the paging cost is minimal but the cost of registration is the largest. Between these extremes lie one or more partitions of the MSC service area that minimize the total cost of paging and registration. In this paper, we seek to determine the location areas in an optimum fashion. Cell to switch assignments are also determined to achieve the minimization of the network cost. For that purpose, we use the available network information to formulate a realistic optimization problem, and propose an algorithm based on simulated annealing (SA) for its solution. Then, we investigate the quality of the SA-based technique by comparing it to greedy search, random generation methods, and a heuristic algorithm. |
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