Gibbs Sampling Aided Throughput Improvement for Next-Generation Wi-Fi

Wireless communications, and in particular wireless local area network (WLAN) technology, has undergone a tremendous evolution in the past decades. After the release of the WLAN standard IEEE 802.11a/b in 1999, Wi-Fi technology soon became pervasive, thanks mainly to its deployment on the unlicensed ISM band. However, high traffic, especially in hotspots and areas with dense deployment of Wi-Fi access points (APs) (e.g., stations, airports, etc.) has caused major issues and a severe degradation of communications quality. The latest WLAN standards (e.g., 802.11ac, 802.11ax) have largely succeeded in improving the link quality and data rate by adopting state-of-the-art PHY layer technologies, e.g., OFDMA, MU-MIMO. However, improvement of the MAC layer in these standards is not noticeable due to restrictions such as hardware limitation and backward compatibility issues for legacy APs. As an effort to improve the MAC layer for the next-generation WLAN standard, in this paper we propose a simple algorithm with low computational complexity for channel selection in Wi-Fi networks. The main idea is to take advantage of the potential of the IEEE 802.11ax MAC to avoid major standard modifications. For this purpose, we employ the channel utilization ratio (CUR), which is measured periodically by each AP based on its channel sensing. Time-averaged CUR values are weighted based on a Gibbs sampling approach and a probability associated to each channel is updated. Finally, a channel is selected based on the aforementioned probabilities in predefined time slots. Simulation results show that the proposed approach can improve the system throughput by up to 5% and transmission delay by up to 20%.