Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks

TL;DR

This paper introduces a modular meta-learning approach for power control in wireless networks with dynamic topologies, leveraging graph neural networks to adapt efficiently to new network configurations with limited data.

Contribution

It proposes a novel modular meta-learning method for GNN-based power control, enabling rapid adaptation to changing network topologies with minimal CSI data.

Findings

Meta-learning outperforms joint training in power control tasks.

Modular meta-learning significantly improves adaptation with limited data.

Numerical results confirm the effectiveness of the proposed approach.

Abstract

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.