Abstract
The modernization of smart devices has emerged in exponential growth in data traffic for a high-capacity wireless network. 5G networks must be capable of handling the excessive stress associated with resource allocation methods for its successful deployment. We also need to take care of the problem of causing energy consumption during the dense deployment process. The dense deployment results in severe power consumption because of fulfilling the demands of the increasing traffic load accommodated by base stations. This paper proposes an improved Artificial Bee Colony (ABC) algorithm which uses the set of variables such as the transmission power and location of each base station (BS) to improve the accuracy of localization of a user equipment (UE) for the efficient energy consumption at BSes. To estimate the optimal configuration of BSes and reduce the power requirement of connected UEs, we enhanced the ABC algorithm, which is named a Modified ABC (MABC) algorithm, and compared it with the latest work on Real-Coded Genetic Algorithm (RCGA) and Differential Evolution (DE) algorithm. The proposed algorithm not only determines the optimal coverage of underutilized BSes but also optimizes the power utilization considering the green networks. The performance comparisons of the modified algorithms were conducted to show that the proposed approach has better effectiveness than the legacy algorithms, ABC, RCGA, and DE.
Highlights
An increasing number of mobile devices with data intensive applications are generating an enormous amount of data
We represent some numerical results obtained from the application of Evolutionary Algorithms such as Differential Evolution (DE), RGA, modified RCGA (MRGA), Artificial Bee Colony (ABC), and Modified ABC (MABC)
We have taken some of the constant variables such as carrier frequency (Cf), FDD frame structure, receiver antenna gain (Ag), bandwidth, Mast Head Amplifier (MHA) gain (Mg), cable loss (Cl), noise figure (Nf), and body loss (Lb)
Summary
An increasing number of mobile devices with data intensive applications are generating an enormous amount of data. Considering the growth of mobile devices in day-to-day life, the future networks must be capable of dealing with the ever-increasing mobile data traffic. Most of the devices today support the majority of services like 3G and 4G-LTE recently, and for the future, it must be capable of handling the rise of the critical factors such as excessive data traffic stress along with 5G networks. Of the power in cellular networks in recent communication technology due to irregular planning [2]. It is noted that base stations (BSes) consume a significant amount of the energy (above 50%) in cellular networks [3, 4]. To estimate the locations of BSes to optimize the transmission power concerning the green aspects is required
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