# Learning State-Augmented Policies for Information Routing in Communication Networks

arXiv: https://arxiv.org/abs/2310.00248 · HTML: https://mlsanity.com/paper/2310.00248/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Deep Convolutional Networks on Graph-Structured Data](https://mlsanity.com/paper/1506.05163/) (2015)
- [Convolutional Neural Network Architectures for Signals Supported on Graphs](https://mlsanity.com/paper/1805.00165/) (2018)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Experience-driven Networking: A Deep Reinforcement Learning based Approach](https://mlsanity.com/paper/1801.05757/) (2018)
- [Learning Optimal Resource Allocations in Wireless Systems](https://mlsanity.com/paper/1807.08088/) (2018)
- [State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards](https://mlsanity.com/paper/2102.11941/) (2021)
- [Spatial Deep Learning for Wireless Scheduling](https://mlsanity.com/paper/1808.01486/) (2018)
- [Graph Embedding based Wireless Link Scheduling with Few Training Samples](https://mlsanity.com/paper/1906.02871/) (2019)
- [A Graph Neural Network Approach for Scalable Wireless Power Control](https://mlsanity.com/paper/1907.08487/) (2019)
- [Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks](https://mlsanity.com/paper/1907.03289/) (2019)
- [Learning Resilient Radio Resource Management Policies with Graph Neural Networks](https://mlsanity.com/paper/2203.11012/) (2022)
- [State-Augmented Learnable Algorithms for Resource Management in Wireless Networks](https://mlsanity.com/paper/2207.02242/) (2022)
- [MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering](https://mlsanity.com/paper/2303.18157/) (2023)
- [Energy-Efficient Subchannel and Power Allocation for HetNets Based on Convolutional Neural Network](https://mlsanity.com/paper/1903.00165/) (2019)
- [Accelerated Backpressure Algorithm](https://mlsanity.com/paper/1302.1475/) (2013)

## Built on by (most cited first, 1 of 1)

- [Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies](https://mlsanity.com/paper/2503.03736/) (2025)

## Abstract (arXiv)

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.