Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies

2025-03-05

AI Summary

To address the low efficiency and reliance on global topology information in multi-hop opportunistic routing for large-scale wireless networks, this paper proposes a distributed Graph Neural Network (GNN) optimization framework based on State Augmentation (SA). The method leverages wireless broadcast characteristics and local neighborhood information to formulate routing decisions as an unsupervised graph policy learning task—achieving, for the first time, end-to-end trainable, topology-adaptive, GNN-driven opportunistic routing policy extraction. By employing graph convolution to aggregate dynamic network state, it eliminates the need for explicit channel estimation and centralized control. Evaluated on both simulation environments and real-world wireless ad hoc testbeds, the proposed approach significantly outperforms conventional baseline algorithms in terms of throughput, delay, and packet delivery ratio. It demonstrates strong robustness to dynamic link fluctuations, generalization across diverse network topologies, and practical feasibility for real-world deployment.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• Optimizing packet routing in large-scale wireless networks.
• Using Graph Neural Networks for dynamic node selection.
• Enhancing network performance via unsupervised learning policies.

Innovation

• State-Augmentation based distributed optimization approach
• Graph Neural Networks for routing decisions
• Unsupervised learning for optimal routing policies