Practical adaptive user association policies for wireless systems with dynamic interference
We study the impact of user association policies on flow-level performance in interference-limited wireless networks. Most research in this area has used static interference models(neighboring base stations are always active) and resorted to intuitive objectives such as load balancing. In this paper...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2011 |
| País: | España |
| Institución: | IMDEA Networks Institute |
| Repositorio: | IMDEA Networks Institute Digital Repository |
| Idioma: | inglés |
| OAI Identifier: | oai:dspace.networks.imdea.org:20.500.12761/1201 |
| Acceso en línea: | http://hdl.handle.net/20.500.12761/1201 |
| Access Level: | acceso abierto |
| Palabra clave: | Q Science::Q Science (General) Q Science::QA Mathematics::QA75 Electronic computers. Computer science T Technology::T Technology (General) T Technology::TA Engineering (General). Civil engineering (General) T Technology::TK Electrical engineering. Electronics Nuclear engineering |
| Sumario: | We study the impact of user association policies on flow-level performance in interference-limited wireless networks. Most research in this area has used static interference models(neighboring base stations are always active) and resorted to intuitive objectives such as load balancing. In this paper, we show that this can be counterproductive in the presence of dynamic interference which couples the transmission rates to users at various base stations. We propose a methodology to optimize the performance of a class of coupled systems, and apply it to study the user association problem. We show that by properly inducing load asymmetries, substantial performance gains can be achieved relative to a load balancing policy (e.g., 15 times reduction in mean delay). We present a practical, measurement-based, interference-aware association policy that infers the degree of interference-induced coupling and adapts to it. Systematic simulations establish that both our optimized static and adaptive association policies substantially outperform various dynamic policies which can, in extreme cases even be susceptible to Braess’s paradox like phenomena, i.e., an increase in the number of base stations can lead to worse performance under greedy association policies. Further, these results are robust to changes in file size distributions, large-scale propagation parameters, and spatial load distributions. |
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