Ali, Mohammed, Duchesne, Florent, Dahman, Ghassan, Gagnon, François and Naboulsi, Diala.
2025.
« New approaches for network topology optimization using deep reinforcement learning and graph neural network ».
IEEE Access, vol. 13.
pp. 85447-85460.
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Abstract
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.
| Item Type: | Peer reviewed article published in a journal |
|---|---|
| Professor: | Professor Gagnon, François Naboulsi, Diala |
| Affiliation: | Génie électrique, Génie logiciel et des technologies de l'information |
| Date Deposited: | 05 Jun 2025 15:47 |
| Last Modified: | 07 Aug 2025 19:51 |
| URI: | https://espace2.etsmtl.ca/id/eprint/30991 |
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