Jumping knowledge graph attention network for resource allocation in wireless cellular system
Abstract Next-generation wireless networks are characterized by two essential features: ubiquitous connectivity and high-speed data transmission. The realization of these features hinges on the development of rational resource allocation strategies to optimize the utilization of radio resources. Thi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-00603-4 |
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| _version_ | 1850124796073869312 |
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| author | Qiushi Sun Zhou Fang Yin Li Ovanes Petrosian |
| author_facet | Qiushi Sun Zhou Fang Yin Li Ovanes Petrosian |
| author_sort | Qiushi Sun |
| collection | DOAJ |
| description | Abstract Next-generation wireless networks are characterized by two essential features: ubiquitous connectivity and high-speed data transmission. The realization of these features hinges on the development of rational resource allocation strategies to optimize the utilization of radio resources. This study addresses the beamforming design problem for downlink transmission in multi-cell cellular networks, with a focus on maximizing user data rates while adhering to stringent power constraints. To tackle this challenge, we propose a novel graph learning-based optimization framework that learns the mapping from channel states to beamforming vectors in an unsupervised manner. At the core of this framework is an attention-based graph neural network (GNN), which efficiently captures complex inter-node relationships by dynamically computing the importance of neighboring nodes. Furthermore, a jumping knowledge network is integrated to enhance structural representation learning, enabling the model to adaptively capture diverse neighborhood ranges for each node and mitigate the issue of over-smoothing. Extensive simulations demonstrate that the proposed algorithm significantly outperforms existing benchmark methods, exhibiting robust performance and strong generalization capabilities across a wide range of system parameter configurations. |
| format | Article |
| id | doaj-art-dd4cb4d7f575489180e1c0ceb803ef44 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dd4cb4d7f575489180e1c0ceb803ef442025-08-20T02:34:14ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-00603-4Jumping knowledge graph attention network for resource allocation in wireless cellular systemQiushi Sun0Zhou Fang1Yin Li2Ovanes Petrosian3School of Management, Harbin Institute of TechnologyFaculty of Applied Mathematics and Control Processes, St.Petersburg UniversitySchool of Mathematics, Harbin Institute of TechnologySchool of Mathematics, Harbin Institute of TechnologyAbstract Next-generation wireless networks are characterized by two essential features: ubiquitous connectivity and high-speed data transmission. The realization of these features hinges on the development of rational resource allocation strategies to optimize the utilization of radio resources. This study addresses the beamforming design problem for downlink transmission in multi-cell cellular networks, with a focus on maximizing user data rates while adhering to stringent power constraints. To tackle this challenge, we propose a novel graph learning-based optimization framework that learns the mapping from channel states to beamforming vectors in an unsupervised manner. At the core of this framework is an attention-based graph neural network (GNN), which efficiently captures complex inter-node relationships by dynamically computing the importance of neighboring nodes. Furthermore, a jumping knowledge network is integrated to enhance structural representation learning, enabling the model to adaptively capture diverse neighborhood ranges for each node and mitigate the issue of over-smoothing. Extensive simulations demonstrate that the proposed algorithm significantly outperforms existing benchmark methods, exhibiting robust performance and strong generalization capabilities across a wide range of system parameter configurations.https://doi.org/10.1038/s41598-025-00603-4 |
| spellingShingle | Qiushi Sun Zhou Fang Yin Li Ovanes Petrosian Jumping knowledge graph attention network for resource allocation in wireless cellular system Scientific Reports |
| title | Jumping knowledge graph attention network for resource allocation in wireless cellular system |
| title_full | Jumping knowledge graph attention network for resource allocation in wireless cellular system |
| title_fullStr | Jumping knowledge graph attention network for resource allocation in wireless cellular system |
| title_full_unstemmed | Jumping knowledge graph attention network for resource allocation in wireless cellular system |
| title_short | Jumping knowledge graph attention network for resource allocation in wireless cellular system |
| title_sort | jumping knowledge graph attention network for resource allocation in wireless cellular system |
| url | https://doi.org/10.1038/s41598-025-00603-4 |
| work_keys_str_mv | AT qiushisun jumpingknowledgegraphattentionnetworkforresourceallocationinwirelesscellularsystem AT zhoufang jumpingknowledgegraphattentionnetworkforresourceallocationinwirelesscellularsystem AT yinli jumpingknowledgegraphattentionnetworkforresourceallocationinwirelesscellularsystem AT ovanespetrosian jumpingknowledgegraphattentionnetworkforresourceallocationinwirelesscellularsystem |