# Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies

arXiv: https://arxiv.org/abs/2503.03736 · HTML: https://mlsanity.com/paper/2503.03736/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [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)
- [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)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [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)
- [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)
- [Mobile Wireless Network Infrastructure on Demand](https://mlsanity.com/paper/2002.03026/) (2020)
- [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)
- [DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09637/) (2023)
- [Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach](https://mlsanity.com/paper/2405.05748/) (2024)
- [Learning State-Augmented Policies for Information Routing in Communication Networks](https://mlsanity.com/paper/2310.00248/) (2023)

## Built on by (most cited first, 3 of 3)

- [Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression](https://mlsanity.com/paper/2506.18748/) (2025)
- [Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks](https://mlsanity.com/paper/2510.11109/) (2025)
- [MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETs](https://mlsanity.com/paper/2609.40170/) (2026)

## Abstract (arXiv)

This paper addresses the challenge of packet-based information routing in large-scale wireless communication networks. The problem is framed as a constrained statistical learning task, where each network node operates using only local information. Opportunistic routing exploits the broadcast nature of wireless communication to dynamically select optimal forwarding nodes, enabling the information to reach the destination through multiple relay nodes simultaneously. To solve this, we propose a State-Augmentation (SA) based distributed optimization approach aimed at maximizing the total information handled by the source nodes in the network. The problem formulation leverages Graph Neural Networks (GNNs), which perform graph convolutions based on the topological connections between network nodes. Using an unsupervised learning paradigm, we extract routing policies from the GNN architecture, enabling optimal decisions for source nodes across various flows. Numerical experiments demonstrate that the proposed method achieves superior performance when training a GNN-parameterized model, particularly when compared to baseline algorithms. Additionally, applying the method to real-world network topologies and wireless ad-hoc network test beds validates its effectiveness, highlighting the robustness and transferability of GNNs.