Energy optimization for optimal location in 5G networks using improved Barnacles Mating Optimizer

Abstract

In recent years, wireless sensor networks have been studied in many applications. One of the important issues studied in these networks is the optimal placement of cells in order to achieve optimal energy consumption. Therefore, intelligent algorithms have been used in most research to achieve this goal. In order to properly roll out 5G, the network must be able to handle the increase in data traffic. The essential aspects of resource allocation systems, such as scalability and throughput, are under too much stress due to the rising amount of traffic. In the present work, network planning is characterized by the optimal selection of the base station location and transmission power in 5G networks. Here, an Amended design of the Barnacles Mating Optimizer (ABMO) Algorithm is proposed and is employed to get higher efficiency in both terms of total connected users and transmit power saving for 5G networks. Therefore, the novelty of this study is in the use of an amended design of the Barnacles Mating Optimizer (ABMO) Algorithm. This amended design improves the accuracy and performance of the ABMO and makes it more effective in finding the best locations for base stations and changing the power level for 5G networks. The amended design also offers several advantages over some other state-of-the-art algorithms and makes the ABMO useful for improving 5G networks Simulations of the proposed ABMO algorithm are then compared with some other state-of-the-art algorithms and the results showed the right location and changing the range of the power level for the proposed method. The outcomes show that the suggested Amended Barnacles Mating Optimizer Algorithm statistically outperforms the original BMO, and some other methods based on DE, and RGA. The findings show that the t-value for BMO and the modified BMO is 4.339e−35 which is so better than that of DE and ABMOO with 1.926e−22, and for the RGA and ABMOO with 4.826e−32.