<ul><li>This paper proposes a State-Augmentation (SA) based distributed optimization approach for packet-based information routing in wireless communication networks.</li><li>The approach leverages Graph Neural Networks (GNNs) to extract routing policies based on network node connections.</li><li>Numerical experiments show that the proposed method outperforms baseline algorithms and is effective in real-world network topologies.</li><li>The method enables opportunistic routing, improving the handling of information by source nodes in the network.</li></ul>

Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies

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