https://orcid.org/0000-0001-8691-8944
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Abstract
The rapid evolution of mobile communications, remarkably the fifth generation (5G) and research-stage sixth (6G), highlights the need for numerous heterogeneous base stations to meet high demands. However, the deployment of many base stations entails a high energy cost, which contradicts the concept of green networks promoted by next-generation networks. The Cell Switch-Off (CSO) problem addresses this by aiming to reduce energy consumption in ultra-dense networks without compromising service quality. This article explores the CSO problem from a multi-objective optimization perspective, focusing on how spatial network demand heterogeneity affects the multi-objective landscape of the problem. In addition to deep landscape understanding, it introduces a local search operator designed to exploit these landscape characteristics, improving the multi-objective efficiency of metaheuristics. The results indicate that increasing heterogeneity simplifies the exploration of the problem space, with the operator achieving closer approximations to the Pareto front, particularly in minimizing network power consumption.
Acknowledgments
The authors thank the Supercomputing and Bioinformatics Centre of the Universidad de Málaga, for providing its services and the Picasso supercomputer facilities to perform the experiments (http://www.scbi.uma.es/).
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.