Performance Measurement of Small Cell Power Management Mechanism in 5G Cellular Networks using Firefly Algorithm

Abstract

In cellular networks, with the increase in demand, designing a base station (BS) with less energy consumption remains a challenge for researchers. Also, in a heterogeneous network that is dense in nature, the distribution of numerous small BS has become a challenging issue in terms of expanding the cost of energy. In this paper, we investigate an optimized nature-based cluster sleep technique for reducing the power consumption in the BS as well as the interference in the network. The small BS are grouped along with the interference, which is assumed to be the cluster, which is quite large, where the fire fly (FF) algorithm is applied to frame the sleep technique for the small BS. These FF algorithms, which are based on fire fly attractiveness behavior, improve connectivity among the base stations in an energy-efficient way. The outcomes reveal that the projected sleep technique with the FF algorithm reduces the power consumed by the BS and also gives satisfactory performance for mobile users. The results were compared with the other techniques, such as BS conventional sleep mode and BS sleep mode with LEACH. The proposed method outperformed the other techniques.