# MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETs

arXiv: https://arxiv.org/abs/2609.40170 · HTML: https://mlsanity.com/paper/2609.40170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 71 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 26 of 26)

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- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [How Powerful are K-hop Message Passing Graph Neural Networks](https://mlsanity.com/paper/2205.13328/) (2022)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation](https://mlsanity.com/paper/1906.12192/) (2019)
- [Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization](https://mlsanity.com/paper/2205.02640/) (2022)
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- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [Deep Distributed Optimization for Large-Scale Quadratic Programming](https://mlsanity.com/paper/2412.12156/) (2024)
- [Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air](https://mlsanity.com/paper/2207.08498/) (2022)
- [Deep Unfolding: Recent Developments, Theory, and Design Guidelines](https://mlsanity.com/paper/2512.03768/) (2025)
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- [Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks](https://mlsanity.com/paper/2203.14339/) (2022)
- [Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers](https://mlsanity.com/paper/2504.19000/) (2025)
- [Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies](https://mlsanity.com/paper/2503.03736/) (2025)
- [Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks](https://mlsanity.com/paper/2302.08447/) (2023)
- [Learning Resource Allocation Policy: Vertex-GNN or Edge-GNN?](https://mlsanity.com/paper/2307.12480/) (2023)
- [Rapid Optimization of Superposition Codes for Multi-Hop NOMA MANETs via Deep Unfolding](https://mlsanity.com/paper/2406.05747/) (2024)
- [Decentralized Covert Routing in Heterogeneous Networks Using Reinforcement Learning](https://mlsanity.com/paper/2402.10087/) (2024)
- [Deep Reinforcement Learning Based Routing for Heterogeneous Multi-Hop Wireless Networks](https://mlsanity.com/paper/2508.14884/) (2025)
- [Deep Reinforcement Learning for Multi-flow Routing in Heterogeneous Wireless Networks](https://mlsanity.com/paper/2511.02030/) (2025)
- [Decentralized Multi-Channel MANET Power Optimization Using Graph Neural Networks](https://mlsanity.com/paper/2605.12612/) (2026)

## Abstract (arXiv)

MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple frequency channels and support heterogeneous traffic patterns, decentralized transmit-power allocation becomes increasingly challenging. We develop a unified learned optimization framework for decentralized power allocation in dynamic multi-hop, multi-channel MANETs. We formulate a constrained end-to-end throughput maximization problem covering unicast, multicast, multicommodity, convergecast, and many-to-many communication. Although centralized and non-convex, this problem serves as an unsupervised training objective for MANET-GNN, a message-passing GNN that operates as a distributed learned optimizer. MANET-GNN uses only local, possibly noisy, CSI and a prescribed number of neighbor message exchanges, enabling low-latency decentralized inference while generalizing across topologies and network sizes. Numerical results show that MANET-GNN achieves centralized-competitive performance across communication frameworks, remains robust to channel uncertainty, and scales effectively across MANET configurations.