Research Article A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks Ruchi Sachan, Tae Jong Choi, and Chang Wook Ahn Department of Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of Korea Correspondence should be addressed to Chang Wook Ahn; cwan@skku.edu Received 11 December 2015; Revised 18 April 2016; Accepted 11 May 2016 Academic Editor: Ahmed Kattan Copyright © 2016 Ruchi Sachan et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te exponential growth in data trafc due to the modernization of smart devices has resulted in the need for a high-capacity wireless network in the future. To successfully deploy 5G network, it must be capable of handling the growth in the data trafc. Te increasing amount of trafc volume puts excessive stress on the important factors of the resource allocation methods such as scalability and throughput. In this paper, we defne a network planning as an optimization problem with the decision variables such as transmission power and transmitter (BS) location in 5G networks. Te decision variables lent themselves to interesting implementation using several heuristic approaches, such as diferential evolution (DE) algorithm and Real-coded Genetic Algorithm (RGA). Te key contribution of this paper is that we modifed RGA-based method to fnd the optimal confguration of BSs not only by just ofering an optimal coverage of underutilized BSs but also by optimizing the amounts of power consumption. A comparison is also carried out to evaluate the performance of the conventional approach of DE and standard RGA with our modifed RGA approach. Te experimental results showed that our modifed RGA can fnd the optimal confguration of 5G/LTE network planning problems, which is better performed than DE and standard RGA. 1. Introduction Te green domain is a new stage which aims to protect Earth and contribute to reducing the global warming by ef- ciently optimizing the energy consumption. Tus, the need for energy efcient wireless networks has drawn signifcant attention and focuses on the need to cut operating expenses and power usage of the telecommunications infrastructure, where radio networks represent about 80% of energy con- sumption. Furthermore, it is widely known that base stations (BSs) consume a signifcant amount of the energy (above 50%) in a cellular network [1, 2], which requires optimization of the transmission power and location of a BS regarding green aspects as shown in Figure 1. Te current 3G and 4G communication technologies were introduced to fulfll the massive demand for enhancing the speed of data trafc. Although the current communica- tions technology has progressed impressively, it is still facing the increasing demands due to the development of smart devices. For this reason, various intensive studies towards 5G networks are being developed beyond the current 4G/IMT- Advanced standards and are moving towards the next phase of mobile communication. Te most important requirement for the development of 5G network is the enhanced data trafc; that is, it has to support robustly an exponentially increasing number of devices [3]. Moreover, Long-Term Evo- lution (LTE) which is expected to be used with 5G networks has to deal with the reduced cell size of a BS [4], which leads to an increase in the number of BSs and raises a concern about increasing energy consumption of BSs. Fortunately, 5G networks would beneft from the position information and fttingly guide the wireless network designs and optimization. Tere are many ways to fnd precise location information in wireless networks along with related distances, velocities, angles, delays, and predictable user behavior [5] in 5G networks. Te information obtained from location-aware technology can be used to address numerous issues by implementing sharing and coexistence approaches to the challenges in 5G networks based on the user’s position. By getting more accurate information of the users, power Hindawi Publishing Corporation Discrete Dynamics in Nature and Society Volume 2016, Article ID 5348203, 9 pages http://dx.doi.org/10.1155/2016/5348203