基于随机边图神经网络的模块化元学习功率控制
Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks
摘要：
在本文中，我们考虑的问题，功率控制的无线网络具有任意时变的拓扑结构，包括可能增加或删除的节点。采用了利用图神经网络（GNN）的数据驱动设计方法，以便有效地参数化将信道状态信息（CSI）映射到发射功率的功率控制策略。特定的GNN架构，称为随机边缘GNN（REGNN），定义了一个非线性图卷积滤波器，其空间权重与信道系数相关。虽然先前的工作假设了一个联合训练的方法，其中基于REGNN的政策是共享所有的拓扑结构，本文的目标适应的功率控制策略的基础上，关于当前的拓扑结构的有限的CSI数据。为此，我们提出了一种新的模块化元学习技术，使模块分配的有效优化。黑盒元学习通过（随机）梯度下降优化通用自适应过程，而模块化元学习找到一组可重用模块，这些模块可以形成任何新网络拓扑的解决方案的组件。数值结果验证了联合训练方案的功率控制问题的元学习的好处，并展示了模块化元学习的优势时，数据的可用性是非常有限的。
英文摘要：
In this paper, we consider the problem of power control for a wireless network with an arbitrarily time-varying topology, including the possible addition or removal of nodes. A data-driven design methodology that leverages graph neural networks (GNNs) is adopted in order to efficiently parametrize the power control policy mapping the channel state information (CSI) to transmit powers. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional filter whose spatial weights are tied to the channel coefficients. While prior work assumed a joint training approach whereby the REGNN-based policy is shared across all topologies, this paper targets adaptation of the power control policy based on limited CSI data regarding the current topology. To this end, we propose a novel modular meta-learning technique that enables the efficient optimization of module assignment. While black-box meta-learning optimizes a general-purpose adaptation procedure via (stochastic) gradient descent, modular meta-learning finds a set of reusable modules that can form components of a solution for any new network topology. Numerical results validate the benefits of meta-learning for power control problems over joint training schemes, and demonstrate the advantages of modular meta-learning when data availability is extremely limited.
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