A Novel Bio-Inspired Energy Optimization for Two-Tier Wireless Communication Networks: A Grasshopper Optimization Algorithm (GOA)-Based Approach
Energy consumption has become one of the most challenging problems in future wireless communication networks. One of the promising methods in fifth generation (5G) cellular networks to meet the ever-increasing demand for high data traffic is wireless heterogeneous networks (HetNets). Adding more bas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/5/1216 |
| _version_ | 1797615492074045440 |
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| author | Ashraf Sherif Huseyin Haci |
| author_facet | Ashraf Sherif Huseyin Haci |
| author_sort | Ashraf Sherif |
| collection | DOAJ |
| description | Energy consumption has become one of the most challenging problems in future wireless communication networks. One of the promising methods in fifth generation (5G) cellular networks to meet the ever-increasing demand for high data traffic is wireless heterogeneous networks (HetNets). Adding more base stations may improve network coverage, but leads to the consumption of a significant amount of power. The scheme of two-tier networks contains small cell base stations (SCBs) that cooperate with macro cell base stations (MCBs) to provide wider coverage. Some small cell base station SCBs are experiencing light traffic loads due to the movement of user equipment (UEs), but these SCBs still consume a considerable amount of energy. Therefore, to reduce SCBs’ power consumption and maximize the overall energy efficiency (EE) of a two-tier network, some SCBs need to be switched off. In this paper, we extend the operation modes for BSs and present a novel mechanism to select an appropriate operation mode for each SCB that is based on bio-inspired behavior. We employ a bias function to manage the power consumption of each operation mode. Each SCB has four power mode selections: On, Standby, Sleep, and Off. We formulate the EE maximization problem under a set of constraints and present a Grasshopper Optimization Algorithm-based Variant Power Mode Selection (GOA-VPMS) to solve it. The proposed algorithm scheme outperforms previous work and provides a higher EE, according to the simulation results. |
| first_indexed | 2024-03-11T07:26:06Z |
| format | Article |
| id | doaj.art-01f389a38c7e438289b9df623db41b30 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T07:26:06Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-01f389a38c7e438289b9df623db41b302023-11-17T07:33:15ZengMDPI AGElectronics2079-92922023-03-01125121610.3390/electronics12051216A Novel Bio-Inspired Energy Optimization for Two-Tier Wireless Communication Networks: A Grasshopper Optimization Algorithm (GOA)-Based ApproachAshraf Sherif0Huseyin Haci1Department of Electrical and Electronic Engineering, Near East University, Near East Boulevard, Nicosia 99138, TurkeyDepartment of Electrical and Electronic Engineering, Near East University, Near East Boulevard, Nicosia 99138, TurkeyEnergy consumption has become one of the most challenging problems in future wireless communication networks. One of the promising methods in fifth generation (5G) cellular networks to meet the ever-increasing demand for high data traffic is wireless heterogeneous networks (HetNets). Adding more base stations may improve network coverage, but leads to the consumption of a significant amount of power. The scheme of two-tier networks contains small cell base stations (SCBs) that cooperate with macro cell base stations (MCBs) to provide wider coverage. Some small cell base station SCBs are experiencing light traffic loads due to the movement of user equipment (UEs), but these SCBs still consume a considerable amount of energy. Therefore, to reduce SCBs’ power consumption and maximize the overall energy efficiency (EE) of a two-tier network, some SCBs need to be switched off. In this paper, we extend the operation modes for BSs and present a novel mechanism to select an appropriate operation mode for each SCB that is based on bio-inspired behavior. We employ a bias function to manage the power consumption of each operation mode. Each SCB has four power mode selections: On, Standby, Sleep, and Off. We formulate the EE maximization problem under a set of constraints and present a Grasshopper Optimization Algorithm-based Variant Power Mode Selection (GOA-VPMS) to solve it. The proposed algorithm scheme outperforms previous work and provides a higher EE, according to the simulation results.https://www.mdpi.com/2079-9292/12/5/1216two-tier networkenergy efficiency (EE)bias functionGrasshopper Optimization Algorithm (GOA) |
| spellingShingle | Ashraf Sherif Huseyin Haci A Novel Bio-Inspired Energy Optimization for Two-Tier Wireless Communication Networks: A Grasshopper Optimization Algorithm (GOA)-Based Approach Electronics two-tier network energy efficiency (EE) bias function Grasshopper Optimization Algorithm (GOA) |
| title | A Novel Bio-Inspired Energy Optimization for Two-Tier Wireless Communication Networks: A Grasshopper Optimization Algorithm (GOA)-Based Approach |
| title_full | A Novel Bio-Inspired Energy Optimization for Two-Tier Wireless Communication Networks: A Grasshopper Optimization Algorithm (GOA)-Based Approach |
| title_fullStr | A Novel Bio-Inspired Energy Optimization for Two-Tier Wireless Communication Networks: A Grasshopper Optimization Algorithm (GOA)-Based Approach |
| title_full_unstemmed | A Novel Bio-Inspired Energy Optimization for Two-Tier Wireless Communication Networks: A Grasshopper Optimization Algorithm (GOA)-Based Approach |
| title_short | A Novel Bio-Inspired Energy Optimization for Two-Tier Wireless Communication Networks: A Grasshopper Optimization Algorithm (GOA)-Based Approach |
| title_sort | novel bio inspired energy optimization for two tier wireless communication networks a grasshopper optimization algorithm goa based approach |
| topic | two-tier network energy efficiency (EE) bias function Grasshopper Optimization Algorithm (GOA) |
| url | https://www.mdpi.com/2079-9292/12/5/1216 |
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