Smart 5G Small Cell Mobile Networking with Sleep Strategy Using Novel Swarm Optimization

Abstract

Due to its major contribution to the overall energy consumption in Information and Communication Technologies (ICT), lowering the energy consumption of mobile communication networks has attracted a lot of attention. This study presents the implementation of a 5G network model based on heterogeneity, employing macro cells and small cells, in conjunction with diverse users and control techniques. In this work, the analysis is performed between the consumption of energy and the performance of users in the cellular networks. The objective is to address the identified problem which is related to energy conservation and proposed a solution utilizing the Modified Particle Swarm Optimization (MPSO) algorithm, which is then compared with the existing Iterative Optimization Algorithm (IOA). The sleep strategy involved in this work helps to save energy by overlapping the cells. The evaluation of the implemented solution focuses on various parameters, including contract rate, power, earnings, and energy efficiency.Comparing the outcomes achieved through the two optimization algorithms, the MPSO-based evaluation demonstrates superior performance over the iterative optimization algorithm in terms of energy efficiency and power consumption. Before applying sleep strategy, the energy efficiency is 2.6%, after applying sleep strategy with IOA having 4% and with suggested approach efficiency is obtained is 6%. The evaluation is conducted using the Matlab software tool.