A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks
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...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2016/5348203 |
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| author | Ruchi Sachan Tae Jong Choi Chang Wook Ahn |
| author_facet | Ruchi Sachan Tae Jong Choi Chang Wook Ahn |
| author_sort | Ruchi Sachan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fc35dedcc4234b25bc171f6cd7347f4d |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-fc35dedcc4234b25bc171f6cd7347f4d2025-02-03T06:00:43ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2016-01-01201610.1155/2016/53482035348203A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless NetworksRuchi Sachan0Tae Jong Choi1Chang Wook Ahn2Department of Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of KoreaDepartment of Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of KoreaDepartment of Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of KoreaThe 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.http://dx.doi.org/10.1155/2016/5348203 |
| spellingShingle | Ruchi Sachan Tae Jong Choi Chang Wook Ahn A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks Discrete Dynamics in Nature and Society |
| title | A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks |
| title_full | A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks |
| title_fullStr | A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks |
| title_full_unstemmed | A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks |
| title_short | A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks |
| title_sort | genetic algorithm with location intelligence method for energy optimization in 5g wireless networks |
| url | http://dx.doi.org/10.1155/2016/5348203 |
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