| 王子威,陶旭,李晖,史振婷,张见,徐钰龙.柔性双工网络功率分配:边剪枝加速的GNN计算[J].电子测量与仪器学报,2024,38(2):160-170 |
| 柔性双工网络功率分配:边剪枝加速的GNN计算 |
| Power allocation in flexible duplex networks: GNN computation accelerated by edge pruning |
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| DOI： |
| 中文关键词: 图神经网络 柔性双工网络 功率分配 动态阈值 时间复杂度 |
| 英文关键词:GNN flexible duplex networks power allocation dynamic threshold time complexity |
| 基金项目:中文基金项目国家自然科学基金项目（61661018）、江苏省基础研究计划青年基金项目（BK20210064）、无锡市科技创新创业资金项目（WX03-02B0137-022200-34） |
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| Author | Institution |
| Wang Ziwei | College of Electronics and Information Engineering, Nanjing University of Information Science and Technology,
Nanjing 210044, China |
| Tao Xu | Jiangsu Integrated Circuit Reliability Technology and Testing System Engineering Research
Center, Wuxi University, Wuxi 214105, China |
| Li Hui | 1.College of Electronics and Information Engineering, Nanjing University of Information Science and Technology,
Nanjing 210044, China;2.Jiangsu Integrated Circuit Reliability Technology and Testing System Engineering Research
Center, Wuxi University, Wuxi 214105, China; 3.Graduate School of Chinese
Aeronautical Establishment, Yangzhou 225006, China |
| Shi Zhenting | College of Electronics and Information Engineering, Nanjing University of Information Science and Technology,
Nanjing 210044, China |
| Zhang Jian | Jiangsu Integrated Circuit Reliability Technology and Testing System Engineering Research
Center, Wuxi University, Wuxi 214105, China |
| Xu Yulong | College of Electronics and Information Engineering, Nanjing University of Information Science and Technology,
Nanjing 210044, China |
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| 中文摘要: |
| 由于用户间干扰的存在，无线通信网络中的功率分配问题往往是非凸的、计算量巨大。当前图神经网络(graph neural network, GNN)成为一种有效的计算方法被用来解决该问题。为了最大限度地提高网络传输速率的同时降低计算复杂度，提出一种将设备属性和通信连接属性纳入GNN的柔性双工网络图表示方法，并构建了相应的柔性双工图神经网络(flexible duplex GNN, FD-GNN)模型，首次将节点对之间的距离、信道增益和邻居作为动态阈值引入到FD-GNN中，以适应动态环境。排除GNN中邻居的信道状态信息，通过修剪FD-GNN中的边来减少计算时间降低网络时间复杂度。仿真表明，所提出的基于信道增益邻居的阈值设定方法，性能最优且达到加权最小均方误差（weighted minimum mean square error，WMMSE）的97%，相较于Full-GNN所需的训练时间下降24%。提出的基于阈值的边剪枝有效降低了GNN运算的时间复杂度，提高了算法有效性。 |
| 英文摘要: |
| Due to the presence of interference between users, power allocation problems in wireless communication networks are often non convex and require a huge amount of computation. The current graph neural network (GNN) has become an effective computational method used to solve this problem. In order to maximize network transmission speed while reducing computational complexity, a flexible duplex network graph representation method that incorporates device and communication connection attributes into GNN is proposed, and a corresponding flexible duplex graph neural network (FD-GNN) model is constructed. For the first time, the distance between node pairs, channel gain, and neighbors are introduced as dynamic thresholds into FD-GNN to adapt to dynamic environments. Excluding channel state information of neighbors in GNN, pruning edges in FD-GNN reduces computation time and network complexity. Simulation results show that the proposed threshold setting method based on channel gain neighbors has the best performance and reaches 97% of the weighted minimum mean square error (WMMSE), reducing the training time required by 24% compared to Full GANN. The proposed threshold based effectively reduces the time complexity of GNN operations and improves the effectiveness of the algorithm. |
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