Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks
This work addresses the challenge of adapting power control in wireless networks for improved efficiency, but it is incremental as it builds on existing graph neural network methods.
The paper tackled the problem of enabling fast adaptation of power control policies to time-varying topologies in decentralized wireless networks, and the result was a method using first-order meta-learning on data from multiple topologies to optimize for few-shot adaptation to new network configurations.
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.