Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks
This paper presents a hybrid machine-learning framework for optimizing 3-Dimensional (3D) Unmanned Aerial Vehicles (UAV) node localization and resource distribution in UAV-assisted THz 6G networks to ensure efficient coverage in dynamic, high-density environments. The proposed model efficiently mana...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-01-01
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| Series: | Cognitive Robotics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667241325000035 |
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| author | Workeneh Geleta Negassa Demissie J. Gelmecha Ram Sewak Singh Davinder Singh Rathee |
| author_facet | Workeneh Geleta Negassa Demissie J. Gelmecha Ram Sewak Singh Davinder Singh Rathee |
| author_sort | Workeneh Geleta Negassa |
| collection | DOAJ |
| description | This paper presents a hybrid machine-learning framework for optimizing 3-Dimensional (3D) Unmanned Aerial Vehicles (UAV) node localization and resource distribution in UAV-assisted THz 6G networks to ensure efficient coverage in dynamic, high-density environments. The proposed model efficiently managed interference, adapted to UAV mobility, and ensured optimal throughput by dynamically optimizing UAV trajectories. The hybrid framework combined the strengths of Graph Neural Networks (GNN) for feature aggregation, Deep Neural Networks (DNN) for efficient resource allocation, and Double Deep Q-Networks (DDQN) for distributed decision-making. Simulation results demonstrated that the proposed model outperformed traditional machine learning models, significantly improving energy efficiency, latency, and throughput. The hybrid model achieved an optimized energy efficiency of 90 Tbps/J, reduced latency to 0.0105 ms, and delivered a network throughput of approximately 96 Tbps. The model adapts to varying link densities, maintaining stable performance even in high-density scenarios. These findings underscore the framework's potential to address key challenges in UAV-assisted 6G networks, paving the way for scalable and efficient communication in next-generation wireless systems. |
| format | Article |
| id | doaj-art-0acb765312974d3aa32f96231bafe192 |
| institution | Kabale University |
| issn | 2667-2413 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Cognitive Robotics |
| spelling | doaj-art-0acb765312974d3aa32f96231bafe1922025-01-30T05:15:11ZengKeAi Communications Co. Ltd.Cognitive Robotics2667-24132025-01-0156176Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networksWorkeneh Geleta Negassa0Demissie J. Gelmecha1Ram Sewak Singh2Davinder Singh Rathee3Electronics and Communication Engineering, Adama Science and Technology University Adama, EthiopiaCorresponding author.; Electronics and Communication Engineering, Adama Science and Technology University Adama, EthiopiaElectronics and Communication Engineering, Adama Science and Technology University Adama, EthiopiaElectronics and Communication Engineering, Adama Science and Technology University Adama, EthiopiaThis paper presents a hybrid machine-learning framework for optimizing 3-Dimensional (3D) Unmanned Aerial Vehicles (UAV) node localization and resource distribution in UAV-assisted THz 6G networks to ensure efficient coverage in dynamic, high-density environments. The proposed model efficiently managed interference, adapted to UAV mobility, and ensured optimal throughput by dynamically optimizing UAV trajectories. The hybrid framework combined the strengths of Graph Neural Networks (GNN) for feature aggregation, Deep Neural Networks (DNN) for efficient resource allocation, and Double Deep Q-Networks (DDQN) for distributed decision-making. Simulation results demonstrated that the proposed model outperformed traditional machine learning models, significantly improving energy efficiency, latency, and throughput. The hybrid model achieved an optimized energy efficiency of 90 Tbps/J, reduced latency to 0.0105 ms, and delivered a network throughput of approximately 96 Tbps. The model adapts to varying link densities, maintaining stable performance even in high-density scenarios. These findings underscore the framework's potential to address key challenges in UAV-assisted 6G networks, paving the way for scalable and efficient communication in next-generation wireless systems.http://www.sciencedirect.com/science/article/pii/S2667241325000035Hybrid machine learningNode localizationResource distributionTHz-6GUAV-assisted networks |
| spellingShingle | Workeneh Geleta Negassa Demissie J. Gelmecha Ram Sewak Singh Davinder Singh Rathee Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks Cognitive Robotics Hybrid machine learning Node localization Resource distribution THz-6G UAV-assisted networks |
| title | Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks |
| title_full | Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks |
| title_fullStr | Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks |
| title_full_unstemmed | Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks |
| title_short | Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks |
| title_sort | hybrid machine learning based 3 dimensional uav node localization for uav assisted wireless networks |
| topic | Hybrid machine learning Node localization Resource distribution THz-6G UAV-assisted networks |
| url | http://www.sciencedirect.com/science/article/pii/S2667241325000035 |
| work_keys_str_mv | AT workenehgeletanegassa hybridmachinelearningbased3dimensionaluavnodelocalizationforuavassistedwirelessnetworks AT demissiejgelmecha hybridmachinelearningbased3dimensionaluavnodelocalizationforuavassistedwirelessnetworks AT ramsewaksingh hybridmachinelearningbased3dimensionaluavnodelocalizationforuavassistedwirelessnetworks AT davindersinghrathee hybridmachinelearningbased3dimensionaluavnodelocalizationforuavassistedwirelessnetworks |