Learning State-Augmented Policies for Information Routing in Communication Networks
This addresses routing efficiency in communication networks, but appears incremental as it builds on existing GNN methods with a novel augmentation strategy.
The paper tackles the problem of information routing in large-scale communication networks by formulating it as a constrained statistical learning problem with local information, and proposes a State Augmentation strategy using graph neural networks to maximize aggregate information at source nodes, achieving improved performance in numerical simulations compared to baseline algorithms.
This paper examines the problem of information routing in a large-scale communication network, which can be formulated as a constrained statistical learning problem having access to only local information. We delineate a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures, by deploying graph convolutions over the topological links of the communication network. The proposed technique leverages only the local information available at each node and efficiently routes desired information to the destination nodes. We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies. In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms. Numerical simulations depict the improved performance of the proposed method in training a GNN parameterization as compared to baseline algorithms.