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A Capacity-Enhanced Local Search for the 5G Cell Switch-off Problem

  • Francisco LunaEmail author
  • Pablo H. Zapata-Cano
  • Ángel Palomares-Caballero
  • Juan F. Valenzuela-Valdés
Conference paper
  • 72 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1173)

Abstract

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.

Keywords

Problem specific operator Hybridization Multi-objective optimization Cell switch-off problem 5G networks 

Notes

Acknowledgements

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.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Francisco Luna
    • 1
    Email author
  • Pablo H. Zapata-Cano
    • 1
  • Ángel Palomares-Caballero
    • 2
  • Juan F. Valenzuela-Valdés
    • 2
  1. 1.Department of Computer Science and Programming LanguagesUniversity of MálagaMálagaSpain
  2. 2.Department of Signal Theory, Telematics and Communications – CITICUniversity of GranadaGranadaSpain

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