# GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks

arXiv: https://arxiv.org/abs/2408.03957 · HTML: https://mlsanity.com/paper/2408.03957/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 28 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [Towards Optimal Power Control via Ensembling Deep Neural Networks](https://mlsanity.com/paper/1807.10025/) (2018)
- [Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks](https://mlsanity.com/paper/1905.07144/) (2019)
- [Decentralized Channel Management in WLANs with Graph Neural Networks](https://mlsanity.com/paper/2210.16949/) (2022)
- [Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes](https://mlsanity.com/paper/2303.16113/) (2023)
- [Accelerating Graph Neural Networks via Edge Pruning for Power Allocation in Wireless Networks](https://mlsanity.com/paper/2305.12639/) (2023)

## Built on by (most cited first, 3 of 3)

- [Low-Latency Relay Selection in NR-V2X Vehicular Communications via Graph Isomorphism Networks with Edge Features](https://mlsanity.com/paper/2607.14176/) (2026)
- [Graph Neural Networks for Resource Allocation in Multi-Channel Wireless Networks](https://mlsanity.com/paper/2506.03813/) (2025)
- [Graph Neural Networks for Resource Allocation in Interference-limited Multi-Channel Wireless Networks with QoS Constraints](https://mlsanity.com/paper/2509.06395/) (2025)

## Abstract (arXiv)

The optimal allocation of channels and power resources plays a crucial role in ensuring minimal interference, maximal data rates, and efficient energy utilisation. As a successful approach for tackling resource management problems in wireless networks, Graph Neural Networks (GNNs) have attracted a lot of attention. This article proposes a GNN-based algorithm to address the joint resource allocation problem in heterogeneous wireless networks. Concretely, we model the heterogeneous wireless network as a heterogeneous graph and then propose a graph neural network structure intending to allocate the available channels and transmit power to maximise the network throughput. Our proposed joint channel and power allocation graph neural network (JCPGNN) comprises a shared message computation layer and two task-specific layers, with a dedicated focus on channel and power allocation tasks, respectively. Comprehensive experiments demonstrate that the proposed algorithm achieves satisfactory performance but with higher computational efficiency compared to traditional optimisation algorithms.