Base Station Switching Problem for Green Cellular Networks with Social Spider Algorithm

TL;DR

This paper introduces a binary Social Spider Algorithm to optimize base station switching in cellular networks, significantly reducing energy consumption and environmental impact during off-peak hours.

Contribution

It presents a novel binary Social Spider Algorithm that effectively handles complex constraints for energy-efficient base station management in cellular networks.

Findings

Algorithm achieves superior energy savings in simulations.

Effective handling of constraints via penalty function.

Potential for reducing carbon footprint of mobile networks.

Abstract

With the recent explosion in mobile data, the energy consumption and carbon footprint of the mobile communications industry is rapidly increasing. It is critical to develop more energy-efficient systems in order to reduce the potential harmful effects to the environment. One potential strategy is to switch off some of the under-utilized base stations during off-peak hours. In this paper, we propose a binary Social Spider Algorithm to give guidelines for selecting base stations to switch off. In our implementation, we use a penalty function to formulate the problem and manage to bypass the large number of constraints in the original optimization problem. We adopt several randomly generated cellular networks for simulation and the results indicate that our algorithm can generate superior performance.