Multi-Objective Green Optimization for Energy Cellular Networks Using Particle Swarm Optimization Algorithm

EasyChair Preprint 7175

Abstract

The energy consumption of cellular networks is essential and this consumption increases with the development of generations of networks and the expansion of the database. The volume of data grows to very large volumes, in terms of the number of users covered by the base stations of this network. Some of the important limitations of cellular networks are the excessive production of carbon dioxide by base stations and the cost of installing enough base stations for good coverage. Our goal in this paper is threefold. Indeed, the cost of installing base stations must be minimized, CO2 emissions must be reduced and network coverage must be maximized. So we have a problem with three conflicting goals. We have modeled this problem as a multi-objective optimization problem. To resolve it, we propose a method based on Particle Swarm Optimization (PSO) algorithms. To evaluate the effectiveness of the proposed algorithm, experiments are performed on a data set. The results showed that our approach improves the coverage of cellular networks, reduces carbon dioxide production, and reduces the cost of base stations installed, simultaneously.

Keyphrases: Energy cellular network, Green multiobjective problem, Particle Swarm Optimization

@booklet{EasyChair:7175,
  author    = {Ayoub Chehlafi and Mohammed Gabli and Soufiane Dahmani},
  title     = {Multi-Objective Green Optimization for Energy Cellular Networks Using Particle Swarm Optimization Algorithm},
  howpublished = {EasyChair Preprint 7175},
  year      = {EasyChair, 2021}}