Bayesian Learning based Rate Adaptation in IEEE 802.11ax WLANs with a Target PER

The optimal modulation and coding scheme (MCS) selection in wireless transmission depends on the dynamically evolving channel state. Hence, Rate adaptation in a wireless channel relies on periodically reported channel quality indicator (CQI) values to select the optimal MCS. The latest 802.11ax, with a HE-sounding protocol, supports an explicit feedback mechanism where the client sends back a transformed estimate of the channel state information (CSI) in the HE CQI Report field. When generated more frequently, these reports can be expensive as they introduce unnecessary computational and protocol overhead. Also, the CSI feedback information is quantized, delayed, and noisy. To reduce the frequent CSI feedback (receiver to the transmitter) overhead, in our work, we obtain CSI statistically at the transmitter through Bayesian Learning (BL). Further, we propose a Bayesian Learning based Rate Adaptation (BLbRA) scheme at the transmitter. BLbRA throughput performance is consistent even with reduced feedback overhead. BLbRA can be implemented without any change in the standard frame format, and therefore, it is suitable for practical deployment.