# A Graph Neural Network Approach for Scalable Wireless Power Control

arXiv: https://arxiv.org/abs/1907.08487 · HTML: https://mlsanity.com/paper/1907.08487/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 26 of 26)

- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [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)
- [State-Augmented Learnable Algorithms for Resource Management in Wireless Networks](https://mlsanity.com/paper/2207.02242/) (2022)
- [Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective](https://mlsanity.com/paper/2105.01696/) (2021)
- [Power Allocation for Wireless Federated Learning using Graph Neural Networks](https://mlsanity.com/paper/2111.07480/) (2021)
- [Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee](https://mlsanity.com/paper/2106.10426/) (2021)
- [Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels](https://mlsanity.com/paper/2110.01722/) (2021)
- [Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks](https://mlsanity.com/paper/2301.11166/) (2023)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies](https://mlsanity.com/paper/2503.03736/) (2025)
- [Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression](https://mlsanity.com/paper/2506.18748/) (2025)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Learning Autonomy in Management of Wireless Random Networks](https://mlsanity.com/paper/2106.07984/) (2021)
- [Stability Analysis of Unfolded WMMSE for Power Allocation](https://mlsanity.com/paper/2110.07471/) (2021)
- [Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks](https://mlsanity.com/paper/2302.08447/) (2023)
- [Graph Neural Networks-Based User Pairing in Wireless Communication Systems](https://mlsanity.com/paper/2306.00717/) (2023)
- [Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems](https://mlsanity.com/paper/2306.11865/) (2023)
- [Learning State-Augmented Policies for Information Routing in Communication Networks](https://mlsanity.com/paper/2310.00248/) (2023)
- [To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?](https://mlsanity.com/paper/2112.14011/) (2021)
- [A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networks](https://mlsanity.com/paper/2210.16412/) (2022)
- [Application of deep and reinforcement learning to boundary control problems](https://mlsanity.com/paper/2310.15191/) (2023)
- [Learning Non-myopic Power Allocation in Constrained Scenarios](https://mlsanity.com/paper/2401.10297/) (2024)
- [Science-Informed Design of Deep Learning With Applications to Wireless Systems: A Tutorial](https://mlsanity.com/paper/2407.07742/) (2024)
- [VERA: Verifiable Feasibility Representations with Counterfactual Credit for Constrained Multi-Agent Control](https://mlsanity.com/paper/2609.38889/) (2026)

## Abstract (arXiv)

Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural network structures, e.g., multi-layer perceptron (MLP) and convolutional neural network (CNN), are inherited from deep learning for image processing tasks, and thus are not tailored to problems in wireless networks. In particular, the performance of these methods deteriorates dramatically when the wireless network size becomes large. In this paper, we propose to utilize graph neural networks (GNNs) to develop scalable methods for solving the power control problem in K-user interference channels. Specifically, a K-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph. We then propose an interference graph convolutional neural network (IGCNet) to learn the optimal power control in an unsupervised manner. It is shown that one-layer IGCNet is a universal approximator to continuous set functions, which well matches the permutation invariance property of interference channels and it is robust to imperfect channel state information (CSI). Extensive simulations will show that the proposed IGCNet outperforms existing methods and achieves significant speedup over the classic algorithm for power control, namely, WMMSE. The code is available on https://github.com/yshenaw/Globecom2019.