论文标题
临时无线网络中的异步资源分配的无监督学习
Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networks
论文作者
论文摘要
我们考虑在异步无线网络设置下的最佳资源分配问题。没有明确的模型知识，我们设计了一种基于聚合图神经网络（AGG-GNN）的无监督学习方法。根据每个网络节点上的局部汇总信息结构，该方法可以在本地实施时在全球和异步中学习。我们通过将激活模式建模为每个节点的特征并训练基于策略的资源分配方法来捕获异步。我们还提出了一个置换不变属性，该属性表明训练有素的Agg-GNN的可传递性。与基线方法相比，我们最终通过数值模拟来验证我们的策略。
We consider optimal resource allocation problems under asynchronous wireless network setting. Without explicit model knowledge, we design an unsupervised learning method based on Aggregation Graph Neural Networks (Agg-GNNs). Depending on the localized aggregated information structure on each network node, the method can be learned globally and asynchronously while implemented locally. We capture the asynchrony by modeling the activation pattern as a characteristic of each node and train a policy-based resource allocation method. We also propose a permutation invariance property which indicates the transferability of the trained Agg-GNN. We finally verify our strategy by numerical simulations compared with baseline methods.
