# Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1905.07144 · HTML: https://mlsanity.com/paper/1905.07144/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 12 of 12)

- [Reinforcement learning on graphs: A survey](https://mlsanity.com/paper/2204.06127/) (2022)
- [Decentralized Channel Management in WLANs with Graph Neural Networks](https://mlsanity.com/paper/2210.16949/) (2022)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks](https://mlsanity.com/paper/2408.03957/) (2024)
- [Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure](https://mlsanity.com/paper/2201.00668/) (2022)
- [Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs](https://mlsanity.com/paper/2311.15858/) (2023)
- [Graph Neural Networks Gone Hogwild](https://mlsanity.com/paper/2407.00494/) (2024)
- [A Network Science perspective of Graph Convolutional Networks: A survey](https://mlsanity.com/paper/2301.04824/) (2023)
- [Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks](https://mlsanity.com/paper/2401.00950/) (2023)
- [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)

Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods.