State-Augmented Opportunistic Routing in Wireless Communication Systems with Graph Neural Networks
In this study, we address the challenge of packet based information routing in large-scale wireless communication networks. We approach this scenario by framing the problem as a statistical learning problem, where each node in the network relies only on the local data. Our exploration focuses on the idea of opportunistic routing, which exploits the broadcast nature of wireless communication to select the optimal relay node and transmit information packets to the destination node via multiple relay nodes. We present a distributed optimization method based on state augmentation (SA) that aims to maximize the total information on different source nodes of the network. Our formulation of the problem deploys graph neural networks (GNNs) to perform graph convolution on the topological connections between the network nodes. Using unsupervised learning, we derive optimal routing policies for the source nodes across multiple flows from the GNN output. Numerical results show the superiority of our proposed method by comparing a GNN model trained against standard algorithms.
