用图神经网络实现无线网络智能路由，提升信息传输效率。
Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies
- 基于图神经网络构建状态增强的分布式路由策略。
- 在真实网络拓扑中表现优于基线算法，支持多跳协同转发。
- 无需标签数据，适用于动态无线自组网，适合网络优化研究者。
本文针对大规模无线通信网络中的基于分组的信息路由问题，将其建模为一个受限的统计学习任务，其中每个网络节点仅依赖本地信息运行。机会式路由利用无线通信的广播特性，动态选择最优中继节点，使信息可通过多个中继节点同时传输至目的地。为此，我们提出一种基于状态增强（SA）的分布式优化方法，旨在最大化源节点处理的总信息量。该方法采用图神经网络（GNN）进行基于拓扑连接的图卷积操作，通过无监督学习从GNN结构中提取路由策略，使源节点能在不同流量场景下做出最优决策。数值实验表明，训练基于GNN参数化的模型时，该方法性能显著优于基线算法；进一步在真实网络拓扑和无线自组网测试平台上的应用验证了其有效性，凸显了GNN在鲁棒性与可迁移性方面的优势。
原文摘要 · Abstract (English)
This paper addresses the challenge of packet-based information routing in large-scale wireless communication networks. The problem is framed as a constrained statistical learning task, where each network node operates using only local information. Opportunistic routing exploits the broadcast nature of wireless communication to dynamically select optimal forwarding nodes, enabling the information to reach the destination through multiple relay nodes simultaneously. To solve this, we propose a State-Augmentation (SA) based distributed optimization approach aimed at maximizing the total information handled by the source nodes in the network. The problem formulation leverages Graph Neural Networks (GNNs), which perform graph convolutions based on the topological connections between network nodes. Using an unsupervised learning paradigm, we extract routing policies from the GNN architecture, enabling optimal decisions for source nodes across various flows. Numerical experiments demonstrate that the proposed method achieves superior performance when training a GNN-parameterized model, particularly when compared to baseline algorithms. Additionally, applying the method to real-world network topologies and wireless ad-hoc network test beds validates its effectiveness, highlighting the robustness and transferability of GNNs.
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