Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization
The challenge of dynamic traffic demand in mobile networks is tackled by moving cells based on unmanned aerial vehicles. Considering the tremendous potential of unmanned aerial vehicles in the future, we propose a new heuristic algorithm for coverage optimization. The proposed algorithm is implement...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477221075544 |
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| _version_ | 1849309390318862336 |
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| author | Marek Ružička Marcel Vološin Juraj Gazda Taras Maksymyuk Longzhe Han MisCha Dohler |
| author_facet | Marek Ružička Marcel Vološin Juraj Gazda Taras Maksymyuk Longzhe Han MisCha Dohler |
| author_sort | Marek Ružička |
| collection | DOAJ |
| description | The challenge of dynamic traffic demand in mobile networks is tackled by moving cells based on unmanned aerial vehicles. Considering the tremendous potential of unmanned aerial vehicles in the future, we propose a new heuristic algorithm for coverage optimization. The proposed algorithm is implemented based on a conditional generative adversarial neural network, with a unique multilayer sum-pooling loss function. To assess the performance of the proposed approach, we compare it with the optimal core-set algorithm and quasi-optimal spiral algorithm. Simulation results show that the proposed approach converges to the quasi-optimal solution with a negligible difference from the global optimum while maintaining a quadratic complexity regardless of the number of users. |
| format | Article |
| id | doaj-art-118f943eac4042e78c0b00a69e5923bb |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-118f943eac4042e78c0b00a69e5923bb2025-08-20T03:54:11ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-03-011810.1177/15501477221075544Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimizationMarek Ružička0Marcel Vološin1Juraj Gazda2Taras Maksymyuk3Longzhe Han4MisCha Dohler5Department of Computers and Informatics, Technical University of Košice, Košice, SlovakiaDepartment of Computers and Informatics, Technical University of Košice, Košice, SlovakiaDepartment of Computers and Informatics, Technical University of Košice, Košice, SlovakiaDepartment of Telecommunications, Lviv Polytechnic National University, Lviv, UkraineSchool of Information Engineering, Nanchang Institute of Technology, Nanchang, ChinaCentre of Telecommunications Research, King’s College London, London, UKThe challenge of dynamic traffic demand in mobile networks is tackled by moving cells based on unmanned aerial vehicles. Considering the tremendous potential of unmanned aerial vehicles in the future, we propose a new heuristic algorithm for coverage optimization. The proposed algorithm is implemented based on a conditional generative adversarial neural network, with a unique multilayer sum-pooling loss function. To assess the performance of the proposed approach, we compare it with the optimal core-set algorithm and quasi-optimal spiral algorithm. Simulation results show that the proposed approach converges to the quasi-optimal solution with a negligible difference from the global optimum while maintaining a quadratic complexity regardless of the number of users.https://doi.org/10.1177/15501477221075544 |
| spellingShingle | Marek Ružička Marcel Vološin Juraj Gazda Taras Maksymyuk Longzhe Han MisCha Dohler Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization International Journal of Distributed Sensor Networks |
| title | Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization |
| title_full | Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization |
| title_fullStr | Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization |
| title_full_unstemmed | Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization |
| title_short | Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization |
| title_sort | fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle based network coverage optimization |
| url | https://doi.org/10.1177/15501477221075544 |
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