New Approaches for Network Topology Optimization Using Deep Reinforcement Learning and Graph Neural Network
The exponential growth in Internet-connected devices has escalated the demand for optimized network topologies to ensure high performance. Traditional optimization methods often fall short in scalability and adaptability when it comes to network topology planning. In this paper, we address the chall...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11000124/ |
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| author | Mohammed Ali Florent Duchesne Ghassan Dahman Francois Gagnon Diala Naboulsi |
| author_facet | Mohammed Ali Florent Duchesne Ghassan Dahman Francois Gagnon Diala Naboulsi |
| author_sort | Mohammed Ali |
| collection | DOAJ |
| description | The exponential growth in Internet-connected devices has escalated the demand for optimized network topologies to ensure high performance. Traditional optimization methods often fall short in scalability and adaptability when it comes to network topology planning. In this paper, we address the challenge of transforming mesh topologies into tree topologies for wireless networks, with the objective of maximizing throughput. We propose two new methods: Path Selection with Rejection Strategy (PSRS), which leverages Message-Passing Neural Networks (MPNN), and Dual-Agent Tree Topology Exploration (DATTE), which employs Graph Attention Networks (GAT). These schemes integrate Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNNs) to construct efficient tree topologies with the goal of maximizing the minimum throughput of the wireless network. Experimental results validate the scalability and performance gains of the proposed approaches, highlighting their potential for real-world applications. |
| format | Article |
| id | doaj-art-2e394dc24a0f43bd8b8b3a39db1ae3e9 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2e394dc24a0f43bd8b8b3a39db1ae3e92025-08-20T03:08:20ZengIEEEIEEE Access2169-35362025-01-0113854478546010.1109/ACCESS.2025.356923611000124New Approaches for Network Topology Optimization Using Deep Reinforcement Learning and Graph Neural NetworkMohammed Ali0https://orcid.org/0000-0001-5770-1631Florent Duchesne1Ghassan Dahman2https://orcid.org/0000-0002-9681-5655Francois Gagnon3https://orcid.org/0000-0002-4558-3401Diala Naboulsi4https://orcid.org/0000-0002-5313-9378École de Technologie Supérieure, Montreal, QC, CanadaÉcole de Technologie Supérieure, Montreal, QC, CanadaÉcole de Technologie Supérieure, Montreal, QC, CanadaÉcole de Technologie Supérieure, Montreal, QC, CanadaÉcole de Technologie Supérieure, Montreal, QC, CanadaThe exponential growth in Internet-connected devices has escalated the demand for optimized network topologies to ensure high performance. Traditional optimization methods often fall short in scalability and adaptability when it comes to network topology planning. In this paper, we address the challenge of transforming mesh topologies into tree topologies for wireless networks, with the objective of maximizing throughput. We propose two new methods: Path Selection with Rejection Strategy (PSRS), which leverages Message-Passing Neural Networks (MPNN), and Dual-Agent Tree Topology Exploration (DATTE), which employs Graph Attention Networks (GAT). These schemes integrate Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNNs) to construct efficient tree topologies with the goal of maximizing the minimum throughput of the wireless network. Experimental results validate the scalability and performance gains of the proposed approaches, highlighting their potential for real-world applications.https://ieeexplore.ieee.org/document/11000124/Deep reinforcement learninggraph neural networksproximal policy optimizationtree topologywireless network |
| spellingShingle | Mohammed Ali Florent Duchesne Ghassan Dahman Francois Gagnon Diala Naboulsi New Approaches for Network Topology Optimization Using Deep Reinforcement Learning and Graph Neural Network IEEE Access Deep reinforcement learning graph neural networks proximal policy optimization tree topology wireless network |
| title | New Approaches for Network Topology Optimization Using Deep Reinforcement Learning and Graph Neural Network |
| title_full | New Approaches for Network Topology Optimization Using Deep Reinforcement Learning and Graph Neural Network |
| title_fullStr | New Approaches for Network Topology Optimization Using Deep Reinforcement Learning and Graph Neural Network |
| title_full_unstemmed | New Approaches for Network Topology Optimization Using Deep Reinforcement Learning and Graph Neural Network |
| title_short | New Approaches for Network Topology Optimization Using Deep Reinforcement Learning and Graph Neural Network |
| title_sort | new approaches for network topology optimization using deep reinforcement learning and graph neural network |
| topic | Deep reinforcement learning graph neural networks proximal policy optimization tree topology wireless network |
| url | https://ieeexplore.ieee.org/document/11000124/ |
| work_keys_str_mv | AT mohammedali newapproachesfornetworktopologyoptimizationusingdeepreinforcementlearningandgraphneuralnetwork AT florentduchesne newapproachesfornetworktopologyoptimizationusingdeepreinforcementlearningandgraphneuralnetwork AT ghassandahman newapproachesfornetworktopologyoptimizationusingdeepreinforcementlearningandgraphneuralnetwork AT francoisgagnon newapproachesfornetworktopologyoptimizationusingdeepreinforcementlearningandgraphneuralnetwork AT dialanaboulsi newapproachesfornetworktopologyoptimizationusingdeepreinforcementlearningandgraphneuralnetwork |