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

To enhance the network throughput of densely deployed wireless local area networks (WLANs), coordinated control methods of access points (APs) have recently been discussed, especially within the IEEE 802.11 Extremely High Throughput Study Group. This paper presents a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs), as a coordinated control method of APs. In densely deployed WLANs, the number of observable 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 training data, independent of the neural network model. The simulation results indicate that the proposed method can allocate channels with higher throughput when compared to existing methods.