GNN-based optimization algorithm for joint user scheduling and beamforming
The coordinated multi-point (CoMP) transmission technology has the characteristics of reducing co-channel interference and improving spectral efficiency.For the CoMP technology, user scheduling (US) and beamforming (BF) design are two fundamental research problems located in the media access control...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022133/ |
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| _version_ | 1841539960796086272 |
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| author | Shiwen HE Jun YUAN Zhenyu AN Min ZHANG Yongming HUANG Yaoxue ZHANG |
| author_facet | Shiwen HE Jun YUAN Zhenyu AN Min ZHANG Yongming HUANG Yaoxue ZHANG |
| author_sort | Shiwen HE |
| collection | DOAJ |
| description | The coordinated multi-point (CoMP) transmission technology has the characteristics of reducing co-channel interference and improving spectral efficiency.For the CoMP technology, user scheduling (US) and beamforming (BF) design are two fundamental research problems located in the media access control layer and the physical layer, respectively.Under the consideration of user service quality requirements, the joint user US-BF optimization problem was investigated with the goal of maximizing network throughput.To overcome the problem that the traditional optimization algorithm had high computational cost and couldn’t effectively utilize the network historical data information, a joint US and power allocation (M-JEEPON) model based on graph neural network was proposed to realize joint US-BF optimization by combining the beam vector analytical solution.The simulation results show that the proposed algorithm can achieve the performance matching or even better than traditional optimization algorithms with lower computational overhead. |
| format | Article |
| id | doaj-art-2b98682442154502a84d77ce4ebd8926 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-2b98682442154502a84d77ce4ebd89262025-01-14T06:29:40ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-07-0143738459394906GNN-based optimization algorithm for joint user scheduling and beamformingShiwen HEJun YUANZhenyu ANMin ZHANGYongming HUANGYaoxue ZHANGThe coordinated multi-point (CoMP) transmission technology has the characteristics of reducing co-channel interference and improving spectral efficiency.For the CoMP technology, user scheduling (US) and beamforming (BF) design are two fundamental research problems located in the media access control layer and the physical layer, respectively.Under the consideration of user service quality requirements, the joint user US-BF optimization problem was investigated with the goal of maximizing network throughput.To overcome the problem that the traditional optimization algorithm had high computational cost and couldn’t effectively utilize the network historical data information, a joint US and power allocation (M-JEEPON) model based on graph neural network was proposed to realize joint US-BF optimization by combining the beam vector analytical solution.The simulation results show that the proposed algorithm can achieve the performance matching or even better than traditional optimization algorithms with lower computational overhead.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022133/cross-layer optimizationgraph neural networkcoordinated multi-pointuser schedulingbeamforming |
| spellingShingle | Shiwen HE Jun YUAN Zhenyu AN Min ZHANG Yongming HUANG Yaoxue ZHANG GNN-based optimization algorithm for joint user scheduling and beamforming Tongxin xuebao cross-layer optimization graph neural network coordinated multi-point user scheduling beamforming |
| title | GNN-based optimization algorithm for joint user scheduling and beamforming |
| title_full | GNN-based optimization algorithm for joint user scheduling and beamforming |
| title_fullStr | GNN-based optimization algorithm for joint user scheduling and beamforming |
| title_full_unstemmed | GNN-based optimization algorithm for joint user scheduling and beamforming |
| title_short | GNN-based optimization algorithm for joint user scheduling and beamforming |
| title_sort | gnn based optimization algorithm for joint user scheduling and beamforming |
| topic | cross-layer optimization graph neural network coordinated multi-point user scheduling beamforming |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022133/ |
| work_keys_str_mv | AT shiwenhe gnnbasedoptimizationalgorithmforjointuserschedulingandbeamforming AT junyuan gnnbasedoptimizationalgorithmforjointuserschedulingandbeamforming AT zhenyuan gnnbasedoptimizationalgorithmforjointuserschedulingandbeamforming AT minzhang gnnbasedoptimizationalgorithmforjointuserschedulingandbeamforming AT yongminghuang gnnbasedoptimizationalgorithmforjointuserschedulingandbeamforming AT yaoxuezhang gnnbasedoptimizationalgorithmforjointuserschedulingandbeamforming |