A Capacity-Enhanced Local Search for the 5G Cell Switch-off Problem
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Network densification with deployments of many small base stations (SBSs) is a key enabler technology for the fifth generation (5G) cellular networks, and it is also clearly in conflict with one of the target design requirements of 5G systems: a 90% reduction of the power consumption. In order to address this issue, switching off a number of SBSs in periods of low traffic demand has been standardized as an recognized strategy to save energy. But this poses a challenging NP-complete optimization problem to the system designers, which do also have to provide the users with maxima capacity. This is a multi-objective optimization problem that has been tackled with multi-objective evolutionary algorithms (MOEAs). In particular, a problem-specific search operator with problem-domain information has been devised so as to engineer hybrid MOEAs. It is based on promoting solutions that activate SBSs which may serve users with higher data rates, while also deactivating those not serving any user at all. That is, it tries to improve the two problem objectives simultaneously. The resulting hybrid algorithms have shown to reach better approximations to the Pareto fronts than the canonical algorithms over a set of nine scenarios with increasing diversity in SBSs and users.
KeywordsProblem specific operator Hybridization Multi-objective optimization Cell switch-off problem 5G networks
This work has been supported by the the Spanish and Andalusian goverments, and FEDER, under contrats TIN2016-75097-P, RTI2018-102002-A-I00 and B-TIC-402-UGR18. Francisco Luna also acknowledges support from Universidad de Málaga. The authors thankfully acknowledges the support provided by the Supercomputing and Bioinformatics center of the University of Málaga.
- 1.3GPP: Small Cell Enhancements for E-UTRA and E-UTRAN–Physical Layer Aspects. Technical report, 3rd Generation Partnership Project (3GPP) (2014). http://www.3gpp.org/ ftp/Specs/html-info/36872.htm
- 4.Cisco: Global mobile data traffic forecast update, 2017–2022 white paper (2019). https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-738429.html. Accessed 8 June 2019
- 7.Ericsson: Ericsson mobility report (2018). https://www.ericsson.com/en/mobility-report/reports/q4-update-2018. Accessed 8 June 2019
- 11.González, D., Mutafungwa, E., Haile, B., Hämäläinen, J., Poveda, H.: A planning and optimization framework for ultra dense cellular deployments. Mob. Inf. Syst. 2017, 1–17 (2017)Google Scholar
- 13.Knowles, J.: A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers. In: 5th International Conference on Intelligent Systems Design and Applications, pp. 552–557 (2005)Google Scholar
- 15.Luna, F., Luque-Baena, R., Martínez, J., Valenzuela-Valdés, J., Padilla, P.: Addressing the 5G cell switch-off problem with a multi-objective cellular genetic algorithm. In: IEEE 5G World Forum, 5GWF 2018, pp. 422–426 (2018)Google Scholar
- 20.Zapata-Cano, P., Luna, F., Valenzuela-Valdés, J., Mora, A.M., Padilla, P.: Metaheurísticas híbridas para el problema del apagado de celdas en redes 5G (in Spanish). In: MAEB 2018, pp. 665–670 (2018)Google Scholar