Decentralized Multi-Channel MANET Power Optimization Using Graph Neural Networks

2026-05-12

AI Summary

This work addresses the challenge of decentralized multi-channel transmit power allocation in resource-constrained mobile ad hoc networks (MANETs), where existing approaches struggle to achieve efficient and real-time optimization. The paper proposes MANET-GNN, the first framework to leverage graph neural networks (GNNs) for this setting, modeling network topology through message passing and enabling fast, unsupervised, decentralized power control. Notably, MANET-GNN operates without global channel state information, exhibits robustness to channel noise, and generalizes effectively across diverse network topologies and channel conditions. Experimental results demonstrate that the proposed method significantly improves throughput across various MANET scenarios and scales efficiently to large numbers of nodes and frequency bands, meeting the stringent real-time requirements of practical deployments.

Abstract

The increasing demand for mobile ad hoc networks (MANETs) calls for decentralized mechanisms that can allocate transmit power across nodes and channels under stringent resource constraints. Existing optimization-based approaches, however, do not account for expected settings where each link includes multiple channels (e.g., multi-band signaling). Motivated by recent advances in machine learning for distributed optimization, we propose MANET-GNN, a graph neural network (GNN)-based algorithm for decentralized power allocation in multi-channel MANETs. MANET-GNN explicitly exploits the network topology, scales efficiently with the number of nodes and frequency bands, generalizes across topologies and channel conditions, and enables near-instantaneous inference suitable for real-time deployment. Our design builds on a constrained optimization formulation and employs a dedicated GNN architecture inspired by message passing, trained via an unsupervised procedure that is robust to noisy channel state information. Numerical evaluations demonstrate that MANET-GNN achieves high-throughput multi-channel communication across diverse MANET scenarios.

Problem

• MANET
• multi-channel
• power allocation
• decentralized optimization
• resource constraints

Innovation

• Graph Neural Networks
• Decentralized Optimization
• Multi-Channel MANET
• Power Allocation
• Unsupervised Learning