Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network
In this paper, we address the frequent problem associated with user association and resource allocation along with optimal deployment of base station (BS) in multiple radio access technology (Multi-RAT)-assisted heterogeneous network (Het-Net). Considering real time user scenarios, optimal resource...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.1998300 |
| _version_ | 1797684863812239360 |
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| author | Sanjoy Debnath Anand Jee Debarati Sen Srimanta Baishya Wasim Arif |
| author_facet | Sanjoy Debnath Anand Jee Debarati Sen Srimanta Baishya Wasim Arif |
| author_sort | Sanjoy Debnath |
| collection | DOAJ |
| description | In this paper, we address the frequent problem associated with user association and resource allocation along with optimal deployment of base station (BS) in multiple radio access technology (Multi-RAT)-assisted heterogeneous network (Het-Net). Considering real time user scenarios, optimal resource allocation in Het-Net while ensuring each user’s minimum required data rate is a challenging task to be performed. Here, we propose a novel algorithm with a well-known and efficient meta-heuristic optimization technique to resolve the aforementioned problem. We use hybrid memory-based dragonfly algorithm with differential evolution (DADE) for its excellent convergence characteristics. Extensive simulations are performed to determine the optimal network utility under the consideration of nonuniform user distribution and fine-tuning their respective service class and contract of association parameters. Simulation results depict that the proposed algorithm improves the overall network utility in terms of radio resource utilization and energy consumption while satisfying the user demands. Comparative analysis of the proposed technique with the other state-of-the-art algorithm depicts the superiority of the proposed algorithm in terms of accuracy and consistency. We also perform optimal multi-RAT cell planning under the above constraints including a network blackout scenario. The algorithm ensures each user coverage by optimally allocating the available resources. |
| first_indexed | 2024-03-12T00:35:56Z |
| format | Article |
| id | doaj.art-2c1e95e6f234413192b23d089766b385 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:35:56Z |
| publishDate | 2021-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-2c1e95e6f234413192b23d089766b3852023-09-15T09:33:59ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452021-12-0135152246227510.1080/08839514.2021.19983001998300Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous NetworkSanjoy Debnath0Anand Jee1Debarati Sen2Srimanta Baishya3Wasim Arif4National Institute of Technology SilcharIIT DelhiIIT KharagpurNational Institute of Technology SilcharNational Institute of Technology SilcharIn this paper, we address the frequent problem associated with user association and resource allocation along with optimal deployment of base station (BS) in multiple radio access technology (Multi-RAT)-assisted heterogeneous network (Het-Net). Considering real time user scenarios, optimal resource allocation in Het-Net while ensuring each user’s minimum required data rate is a challenging task to be performed. Here, we propose a novel algorithm with a well-known and efficient meta-heuristic optimization technique to resolve the aforementioned problem. We use hybrid memory-based dragonfly algorithm with differential evolution (DADE) for its excellent convergence characteristics. Extensive simulations are performed to determine the optimal network utility under the consideration of nonuniform user distribution and fine-tuning their respective service class and contract of association parameters. Simulation results depict that the proposed algorithm improves the overall network utility in terms of radio resource utilization and energy consumption while satisfying the user demands. Comparative analysis of the proposed technique with the other state-of-the-art algorithm depicts the superiority of the proposed algorithm in terms of accuracy and consistency. We also perform optimal multi-RAT cell planning under the above constraints including a network blackout scenario. The algorithm ensures each user coverage by optimally allocating the available resources.http://dx.doi.org/10.1080/08839514.2021.1998300 |
| spellingShingle | Sanjoy Debnath Anand Jee Debarati Sen Srimanta Baishya Wasim Arif Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network Applied Artificial Intelligence |
| title | Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network |
| title_full | Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network |
| title_fullStr | Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network |
| title_full_unstemmed | Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network |
| title_short | Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network |
| title_sort | energy efficient optimal resource allocation in multi rat heterogeneous network |
| url | http://dx.doi.org/10.1080/08839514.2021.1998300 |
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