# ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks

arXiv: https://arxiv.org/abs/2301.00757 · HTML: https://mlsanity.com/paper/2301.00757/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation](https://mlsanity.com/paper/2009.10812/) (2020)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Towards Optimal Power Control via Ensembling Deep Neural Networks](https://mlsanity.com/paper/1807.10025/) (2018)
- [Spatial Deep Learning for Wireless Scheduling](https://mlsanity.com/paper/1808.01486/) (2018)
- [Graph Embedding based Wireless Link Scheduling with Few Training Samples](https://mlsanity.com/paper/1906.02871/) (2019)
- [Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation](https://mlsanity.com/paper/2009.14404/) (2020)
- [Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks](https://mlsanity.com/paper/2104.05463/) (2021)
- [Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks](https://mlsanity.com/paper/2108.13178/) (2021)
- [Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection](https://mlsanity.com/paper/2112.10086/) (2021)
- [A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization](https://mlsanity.com/paper/2207.05364/) (2022)
- [Learning to Beamform in Heterogeneous Massive MIMO Networks](https://mlsanity.com/paper/2011.03971/) (2020)
- [AI Empowered Resource Management for Future Wireless Networks](https://mlsanity.com/paper/2106.06178/) (2021)
- [Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation](https://mlsanity.com/paper/2108.01529/) (2021)
- [GBLinks: GNN-Based Beam Selection and Link Activation for Ultra-dense D2D mmWave Networks](https://mlsanity.com/paper/2107.02412/) (2021)
- [Cell-Free Massive MIMO for 6G Wireless Communication Networks](https://mlsanity.com/paper/2110.07309/) (2021)

## Built on by (most cited first, 5 of 5)

- [Generative Diffusion Models of Stochastic Graph Signals](https://mlsanity.com/paper/2607.06833/) (2026)
- [Learning Wireless Interference Patterns: Decoupled GNN for Throughput Prediction in Heterogeneous Multi-Hop p-CSMA Networks](https://mlsanity.com/paper/2510.14137/) (2025)
- [LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning](https://mlsanity.com/paper/2604.16910/) (2026)
- [iSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Delivery](https://mlsanity.com/paper/2603.18539/) (2026)
- [A General Deep Learning Framework for Wireless Resource Allocation under Discrete Constraints](https://mlsanity.com/paper/2603.19322/) (2026)

## Abstract (arXiv)

In order to achieve high data rate and ubiquitous connectivity in future wireless networks, a key task is to efficiently manage the radio resource by judicious beamforming and power allocation. Unfortunately, the iterative nature of the commonly applied optimization-based algorithms cannot meet the low latency requirements due to the high computational complexity. For real-time implementations, deep learning-based approaches, especially the graph neural networks (GNNs), have been demonstrated with good scalability and generalization performance due to the permutation equivariance (PE) property. However, the current architectures are only equipped with the node-update mechanism, which prohibits the applications to a more general setup, where the unknown variables are also defined on the graph edges. To fill this gap, we propose an edge-update mechanism, which enables GNNs to handle both node and edge variables and prove its PE property with respect to both transmitters and receivers. Simulation results on typical radio resource management problems demonstrate that the proposed method achieves higher sum rate but with much shorter computation time than state-of-the-art methods and generalizes well on different numbers of base stations and users, different noise variances, interference levels, and transmit power budgets.