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Abstract <p>The deployment of an extremely high number of base stations (BSs) is a widely recognized technology for meeting the expected performance of next-generation wireless networks (5G/B5G/6G), leading to what is known as ultradense networks (UDNs). However, UDNs have also become a major challenge regarding sustainability due to the energy consumption of such a high number of BSs. Among the different research lines proposed in the literature to address this issue, the selective deactivation of the cells installed in the BSs in periods of low traffic demand is considered in this work. It is an NP-complete nonlinear optimization problem for which we are proposing two novel linear and quadratic approximations aiming at making them affordable for mathematical programming solvers. Computational experiments are conducted by comparing the approximated models and the original nonlinear problem and using different solvers to evaluate their performance. The results suggest that the approximated models can obtain feasible solutions in reasonable computing times and are a promising research line. Finally, a prototype of a multiobjective metaheuristic that uses these approximated models as a local search operator is developed, and the preliminary results show a consistent enhancement over the canonical algorithm with an average improvement in the hypervolume quality indicator of 27.77%.</p>

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Efficient Linear and Quadratic Approximation Models for the Cell Switch-Off Problem in Ultradense Networks

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Diego Rossit,
  • Francisco Luna Valero,
  • Jesús Galeano-Brajones,
  • Javier Carmona-Murillo

摘要

Abstract

The deployment of an extremely high number of base stations (BSs) is a widely recognized technology for meeting the expected performance of next-generation wireless networks (5G/B5G/6G), leading to what is known as ultradense networks (UDNs). However, UDNs have also become a major challenge regarding sustainability due to the energy consumption of such a high number of BSs. Among the different research lines proposed in the literature to address this issue, the selective deactivation of the cells installed in the BSs in periods of low traffic demand is considered in this work. It is an NP-complete nonlinear optimization problem for which we are proposing two novel linear and quadratic approximations aiming at making them affordable for mathematical programming solvers. Computational experiments are conducted by comparing the approximated models and the original nonlinear problem and using different solvers to evaluate their performance. The results suggest that the approximated models can obtain feasible solutions in reasonable computing times and are a promising research line. Finally, a prototype of a multiobjective metaheuristic that uses these approximated models as a local search operator is developed, and the preliminary results show a consistent enhancement over the canonical algorithm with an average improvement in the hypervolume quality indicator of 27.77%.