用图神经网络实现多通道无线网络资源高效分配
Graph Neural Networks for Resource Allocation in Multi-Channel Wireless Networks
- 基于拉格朗日框架与图神经网络联合优化频谱和功率分配
- 相比传统算法，速率提升且推理时间显著降低
- 适合大规模密集网络，支持频谱复用，可拓展性强
随着移动设备数量持续增长，干扰已成为提升无线网络数据速率的主要瓶颈。高效的联合信道与功率分配（JCPA）对管理干扰至关重要。本文首先提出一种改进的加权最小均方误差（eWMMSE）算法解决多通道无线网络中的JCPA问题。为降低迭代优化的计算复杂度，进一步提出JCPGNN-M——一种基于图神经网络的解决方案，可为每个用户同时分配多条信道。我们将问题重新表述为拉格朗日函数形式，系统性地施加总功率约束。该方法结合拉格朗日框架与图神经网络，迭代更新拉格朗日乘子与资源分配方案。不同于现有基于GNN的方法中每个用户仅限单信道的情况，JCPGNN-M支持高效频谱复用，在密集网络场景下具有良好扩展性。仿真结果表明，与eWMMSE相比，JCPGNN-M在数据速率上表现更优；同时，其推理时间远低于eWMMSE，且能良好泛化至更大规模网络。
原文摘要 · Abstract (English)
As the number of mobile devices continues to grow, interference has become a major bottleneck in improving data rates in wireless networks. Efficient joint channel and power allocation (JCPA) is crucial for managing interference. In this paper, we first propose an enhanced WMMSE (eWMMSE) algorithm to solve the JCPA problem in multi-channel wireless networks. To reduce the computational complexity of iterative optimization, we further introduce JCPGNN-M, a graph neural network-based solution that enables simultaneous multi-channel allocation for each user. We reformulate the problem as a Lagrangian function, which allows us to enforce the total power constraints systematically. Our solution involves combining this Lagrangian framework with GNNs and iteratively updating the Lagrange multipliers and resource allocation scheme. Unlike existing GNN-based methods that limit each user to a single channel, JCPGNN-M supports efficient spectrum reuse and scales well in dense network scenarios. Simulation results show that JCPGNN-M achieves better data rate compared to eWMMSE. Meanwhile, the inference time of JCPGNN-M is much lower than eWMMS, and it can generalize well to larger networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。