  Power control in decentralized wireless networks poses a complex stochastic
optimization problem when formulated as the maximization of the average sum
rate for arbitrary interference graphs. Recent work has introduced data-driven
design methods that leverage graph neural network (GNN) to efficiently
parametrize the power control policy mapping channel state information (CSI) to
the power vector. The specific GNN architecture, known as random edge GNN
(REGNN), defines a non-linear graph convolutional architecture whose spatial
weights are tied to the channel coefficients, enabling a direct adaption to
channel conditions. This paper studies the higher-level problem of enabling
fast adaption of the power control policy to time-varying topologies. To this
end, we apply first-order meta-learning on data from multiple topologies with
the aim of optimizing for a few-shot adaptation to new network configurations.
