Meta-Learning Empowered Graph Neural Networks for Radio Resource Management

TL;DR

This paper introduces a meta-learning framework using graph neural networks for efficient radio resource management in dynamic wireless networks, enabling fast adaptation and improved throughput.

Contribution

It proposes a novel meta-learning approach with GNNs for scalable, fast-adapting power optimization in wireless networks, addressing dynamic configuration challenges.

Findings

Improves network throughput and fairness.

Enables rapid adaptation to new network scenarios.

Reduces training data requirements for new configurations.

Abstract

In this paper, we consider a radio resource management (RRM) problem in the dynamic wireless networks, comprising multiple communication links that share the same spectrum resource. To achieve high network throughput while ensuring fairness across all links, we formulate a resilient power optimization problem with per-user minimum-rate constraints. We obtain the corresponding Lagrangian dual problem and parameterize all variables with neural networks, which can be trained in an unsupervised manner due to the provably acceptable duality gap. We develop a meta-learning approach with graph neural networks (GNNs) as parameterization that exhibits fast adaptation and scalability to varying network configurations. We formulate the objective of meta-learning by amalgamating the Lagrangian functions of different network configurations and utilize a first-order meta-learning algorithm, called Reptile, to obtain the meta-parameters. Numerical results verify that our method can efficiently improve the overall throughput and ensure the minimum rate performance. We further demonstrate that using the meta-parameters as initialization, our method can achieve fast adaptation to new wireless network configurations and reduce the number of required training data samples.