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

## TL;DR

This paper introduces a novel deep reinforcement learning approach utilizing graph convolutional networks for efficient channel allocation in densely deployed WLANs, aiming to improve control and performance over existing methods.

## Contribution

It proposes a GCN-based deep reinforcement learning scheme combined with game theory for faster training in WLAN channel allocation, addressing high topology complexity.

## Key findings

- Effective channel control in dense WLANs
- Faster learning with game theory-based data collection
- Outperforms existing methods in simulations

## Abstract

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.