Fast and efficient energy-oriented cell assignment in heterogeneous networks
| Autor: | Ramón Agüero, Juan Luis-Gorricho, Joan Serrat, Luis Diez, Javier Rubio-Loyola, Christian Aguilar-Fuster |
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| Přispěvatelé: | Universidad de Cantabria, Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica, Universitat Politècnica de Catalunya. MAPS - Management, Pricing and Services in Next Generation Networks |
| Rok vydání: | 2019 |
| Předmět: |
Dense networks
Mathematical optimization Optimization problem Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors [Àrees temàtiques de la UPC] Computer Networks and Communications Computer science Heuristic (computer science) Metaheuristic 02 engineering and technology Local optimum 0203 mechanical engineering Cell assignment 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Resource allocation Heuristic Cellular networks 020302 automobile design & engineering 020206 networking & telecommunications Wireless communication systems Comunicació sense fil Sistemes de Energy efficiency Heterogeneous networks Algorithm design Heuristics Assignment problem Heterogeneous network Information Systems |
| Zdroj: | Wireless Networks, 2020, 26(5), 3119-3137 UCrea Repositorio Abierto de la Universidad de Cantabria instname Wireless Networks UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Recercat. Dipósit de la Recerca de Catalunya |
| ISSN: | 1572-8196 1022-0038 3119-3137 |
| Popis: | The cell assignment problem is combinatorial, with increased complexity when it is tackled considering resource allocation. This paper models joint cell assignment and resource allocation for cellular heterogeneous networks, and formalizes cell assignment as an optimization problem. Exact algorithms can find optimal solutions to the cell assignment problem, but their execution time increases drastically with realistic network deployments. In turn, heuristics are able to find solutions in reasonable execution times, but they get usually stuck in local optima, thus failing to find optimal solutions. Metaheuristic approaches have been successful in finding solutions closer to the optimum one to combinatorial problems for large instances. In this paper we propose a fast and efficient heuristic that yields very competitive cell assignment solutions compared to those obtained with three of the most widely-used metaheuristics, which are known to find solutions close to the optimum due to the nature of their search space exploration. Our heuristic approach adds energy expenditure reduction in its algorithmic design. Through simulation and formal statistical analysis, the proposed scheme has been proved to produce efficient assignments in terms of the number of served users, resource allocation and energy savings, while being an order of magnitude faster than metaheuritsic-based approaches. This paper has been supported by the National Council of Research and Technology (CONACYT) through Grant FONCICYT/272278 and the ERANetLAC (Network of the European Union, Latin America, and the Caribbean Countries) Project ELAC2015/T100761. This paper is partially supported also by the ADVICE Project, TEC2015-71329 (MINECO/FEDER) and the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 777067 (NECOS Project). |
| Databáze: | OpenAIRE |
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