# Learning Decentralized Wireless Resource Allocations with Graph Neural Networks

arXiv: https://arxiv.org/abs/2107.01489 · HTML: https://mlsanity.com/paper/2107.01489/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2021.

## Built on by (most cited first, 25 of 25)

- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back](https://mlsanity.com/paper/2303.11323/) (2023)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Learning Resilient Radio Resource Management Policies with Graph Neural Networks](https://mlsanity.com/paper/2203.11012/) (2022)
- [A Manifold Perspective on the Statistical Generalization of Graph Neural Networks](https://mlsanity.com/paper/2406.05225/) (2024)
- [State-Augmented Learnable Algorithms for Resource Management in Wireless Networks](https://mlsanity.com/paper/2207.02242/) (2022)
- [Convolutional Neural Networks on Manifolds: From Graphs and Back](https://mlsanity.com/paper/2210.00376/) (2022)
- [Tangent Bundle Filters and Neural Networks: from Manifolds to Cellular Sheaves and Back](https://mlsanity.com/paper/2210.15058/) (2022)
- [Stability of Aggregation Graph Neural Networks](https://mlsanity.com/paper/2207.03678/) (2022)
- [Generative Diffusion Models for Resource Allocation in Wireless Networks](https://mlsanity.com/paper/2504.20277/) (2025)
- [Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes](https://mlsanity.com/paper/2303.16113/) (2023)
- [Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks](https://mlsanity.com/paper/2302.08447/) (2023)
- [Accelerating Graph Neural Networks via Edge Pruning for Power Allocation in Wireless Networks](https://mlsanity.com/paper/2305.12639/) (2023)
- [Graph Neural Networks-Based User Pairing in Wireless Communication Systems](https://mlsanity.com/paper/2306.00717/) (2023)
- [Task-Oriented Edge Networks: Decentralized Learning Over Wireless Fronthaul](https://mlsanity.com/paper/2312.01288/) (2023)
- [Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach](https://mlsanity.com/paper/2505.12902/) (2025)
- [Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning](https://mlsanity.com/paper/2212.12329/) (2022)
- [Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey](https://mlsanity.com/paper/2302.13876/) (2023)
- [A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Demands](https://mlsanity.com/paper/2311.04600/) (2023)
- [Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks](https://mlsanity.com/paper/2311.04604/) (2023)
- [Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network](https://mlsanity.com/paper/2405.17436/) (2024)
- [Heterogeneous Resource Allocation with Multi-task Learning for Wireless Networks](https://mlsanity.com/paper/2502.10027/) (2025)
- [Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version)](https://mlsanity.com/paper/2509.05447/) (2025)
- [Limit Analysis of Graph Neural Networks with Wireless Conflict Graphs](https://mlsanity.com/paper/2606.03794/) (2026)
- [Queue-Aware Graph Reinforcement Learning for UAV-ISAC-Assisted Maritime Data Collection](https://mlsanity.com/paper/2607.00324/) (2026)

## Abstract (arXiv)

We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure. We develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process a sequence of delayed and potentially asynchronous graph aggregated state information obtained locally at each transmitter from multi-hop neighbors. We further utilize model-free primal-dual learning methods to optimize performance subject to constraints in the presence of delay and asynchrony inherent to decentralized networks. We demonstrate a permutation equivariance property of the resulting resource allocation policy that can be shown to facilitate transference to dynamic network configurations. The proposed framework is validated with numerical simulations that exhibit superior performance to baseline strategies.