A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks

Abstract

The exponential growth in data traffic 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 traffic. The increasing amount of traffic volume puts excessive stress on the important factors of the resource allocation methods such as scalability and throughput. In this paper, we define a network planning as an optimization problem with the decision variables such as transmission power and transmitter (BS) location in 5G networks. The decision variables lent themselves to interesting implementation using several heuristic approaches, such as differential evolution (DE) algorithm and Real-coded Genetic Algorithm (RGA). The key contribution of this paper is that we modified RGA-based method to find the optimal configuration of BSs not only by just offering 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 modified RGA approach. The experimental results showed that our modified RGA can find the optimal configuration of 5G/LTE network planning problems, which is better performed than DE and standard RGA. Hindawi Publishing Corporation

Planning Optimization of the Distributed Antenna System in High-Speed Railway Communication Network Based on Improved Cuckoo Search

“…where μ and ν obey the normal distributions shown in (20); the standard deviations σ μ and σ ν of the normal distribution corresponding to (20) are shown in (21). β is a parameter of Lévy distribution, and it is usually 1.5 in CS algorithm.…”

“…Liang et al [20] build a new BS planning optimization problem of the 4G heterogeneous networks and use the multiobjective optimization algorithm to solve the problem. Sachan et al [21] also use a GA-based approach to solve the network planning optimization problem in 5G. Moreover, Lin et al [22] propose a hybrid algorithm of GA and differential evolution (DE) to solve the electromagnetic pollution and power consumption joint optimization problem in green 5G networks.…”

MABC: Power-Based Location Planning with a Modified ABC Algorithm for 5G Networks

“…In [4], the authors modified the traditional RGA to make novel GAs for the future generation of cellular networks. As they mentioned that because of shuffling all the chromosome in crossover operation, the performance of standard RGA makes worst solution over the generation.…”

“…Radio access networks almost consume 80% of the power in cellular networks in recent communication technology due to irregular planning [2]. Moreover, 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.…”

“…In this paper, we have modified the Artificial Bee Colony (ABC) algorithm [8] to optimize the location estimation with the minimum power required for the UEs with less activated BSes. Through the comparison of Modified ABC (MABC) with the other mentioned algorithms such as Real-Coded Genetic Algorithm (RCGA) and Differential Evolution (DE) algorithm, we found that even the modified RCGA (MRGA) with the shuffling of their chromosome could not precisely find an optimum solution [4]. However, the standard ABC has performed well, compared to the other implemented techniques, such as RCGA, MRGA, and DE, because of its different methods of playing employed bees and scout bees to optimize the solution efficiently for our targeted problem.…”