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

arXiv 2609.40170 ↗First posted 30 Sep 2026cs.LGcited by 0

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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.

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References · 26

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  4. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 413
  5. 2023A Survey on Oversmoothing in Graph Neural Networkscited by 247
  6. 2019Optimal Wireless Resource Allocation with Random Edge Graph Neural Networkscited by 90
  7. 2022How Powerful are K-hop Message Passing Graph Neural Networkscited by 90
  8. 2020Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysiscited by 81
  9. 2019GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulationcited by 75
  10. 2022Model-Based Deep Learning: On the Intersection of Deep Learning and Optimizationcited by 50
  11. 2022Understanding AdamW through Proximal Methods and Scale-Freenesscited by 46
  12. 2021Link Scheduling using Graph Neural Networkscited by 26
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  2. 2022Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Aircited by 7
  3. 2025Deep Unfolding: Recent Developments, Theory, and Design Guidelinescited by 7
  4. 2024Modular Hypernetworks for Scalable and Adaptive Deep MIMO Receiverscited by 5
  5. 2022Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networkscited by 4
  6. 2025Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizerscited by 4
  7. 2025Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policiescited by 3
  8. 2023Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networkscited by 2
  9. 2023Learning Resource Allocation Policy: Vertex-GNN or Edge-GNN?cited by 2
  10. 2024Rapid Optimization of Superposition Codes for Multi-Hop NOMA MANETs via Deep Unfoldingcited by 2
  11. 2024Decentralized Covert Routing in Heterogeneous Networks Using Reinforcement Learningcited by 1
  12. 2025Deep Reinforcement Learning Based Routing for Heterogeneous Multi-Hop Wireless Networkscited by 1
  13. 2025Deep Reinforcement Learning for Multi-flow Routing in Heterogeneous Wireless Networkscited by 1
  14. 2026Decentralized Multi-Channel MANET Power Optimization Using Graph Neural Networkscited by 1

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