Cross-Layer Channel Sounding Optimization Towards Next-Gen Wi-Fi: From Model Driven to Data Driven

Extremely High Throughput (EHT) and Ultra-high reliability (UHR) are new objectives in Next-Gen Wi-Fi, i.e., Wi-Fi 7 and beyond; however, the data rate within a periodic channel sounding round is expected to significantly deteriorate under time-varying channels with Doppler effect in Downlink Multi-User Multiple-Input Multiple-Output. Therefore, Next-Gen channel sounding must carefully balance the MAC-layer CSI overhead reduction and the PHY-layer channel capacity degradation caused by the Doppler effect for data rate maximization. Despite its critical importance, the cross-layer (PHY + MAC) Wi-Fi channel sounding optimization in time-varying channels remains under-explored. This paper addresses this research gap by proposing a cross-layer optimization problem to find the optimal EHT sounding period that maximizes the average data rate by considering both MAC-layer CSI overhead and PHY-layer channel capacity degradation. This problem is then converted into an equivalent optimization problem that can be solved efficiently using our proposed model driven optimal search algorithm with proven convexity. Afterwards, we introduce a data driven Transformer-based partial CSI prediction framework to alleviate CSI staleness without introducing extra CSI overhead, which further enhances the average data rate. Through simulations, we evaluate the baseline EHT sounding protocol that always uses outdated partial CSI, and then benchmark the baseline against our proposed hybrid data and model driven approach. The numerical results demonstrate that integrating Transformer-based partial CSI prediction with the optimal channel sounding period significantly reduces CSI overhead by up to 25.2%, while increasing the average throughput by up to 30.9%.