Abstract: The popularity of IEEE 802.11 based Wireless Local Area Network (WLAN) increased significantly in recent years and resulted in the dense deployment of WLANs. While densification can contribute to increasing coverage, it could also lead to increasing interference and cannot insure high spatial reuse due to the current physical carrier sensing of IEEE 802.11. To tackle these challenges, the dynamic sensitivity control (DSC) is considered in IEEE 802.11ax, which dynamically selects the appropriate carrier sensing threshold (CST) to improve spectrum efficiency and enhance spatial reuse in densely deployed network. A dynamic Q-learning based CST selection method is proposed to enable a network to select the optimal CST according to the channel condition. Simulation results show that the propsoed scheme provides 40% aggregate throughput gain of a dense network when compared with legacy IEEE 802.11.

Key words:

IEEE 802.11ax, reinforcement learning, Dynamic Sensitivity Control, dense network