基于图注意力网络的无线信道功率资源优化分配OACSTPCD
Optimized Wireless Channel Power Resource Allocation Based on Graph Attention Network
为更好地优化无线自组织网络中的节点发射功率,进一步提升网络总体吞吐量,文章提出一种基于图神经网络理论的发射功率分配算法.该算法以"展开加权最小均方误差"迭代算法为总体框架,在迭代结构中引入"图注意力网络"模型,通过无监督学习机制对特定参数进行训练,在保持良好优化性能的同时加快算法收敛.仿真结果表明,文章提出的功率优化分配算法可在达到优于同类算法性能的前提下,显著降低计算复杂度.
In order to better optimize the transmission power of nodes in ad hoc networks and further improve the overall network throughput,this paper proposes a transmission power allocation algorithm based on graph neural network(GNN)theory.This algorithm takes the"unfolded weighted minimum mean square error"(UWMMSE)iterative algorithm as the overall framework,introduces the"graph attention network"model in the iterative structure,and trains specific parameters through unsupervised learning mechanism,while maintaining good optimization performance and accelerating algorithm convergence.The simulation results show that the power optimization allocation algorithm proposed in the paper can significantly reduce computational complexity while achieving better performance than similar algorithms.
周想凌;胡晨;罗弦;吕苏;罗先南
国网湖北省电力有限公司,湖北省 武汉市 430074国网电力科学研究院有限公司,江苏省 南京市 210003
电子信息工程
图注意力网络展开加权最小均方误差功率分配图神经网络5G
graph attention networkunfolded weighted minimum mean square errorpower allocationgraph neural network5G
《电力信息与通信技术》 2024 (005)
63-69 / 7
国家电网有限公司总部科技项目资助"融合5G短切片、4G短复用的电力无线核心骨干专网关键技术研究与应用"(5108-202218280A-2-415-XG).
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