Uncertainty-Aware Machine Learning for Delay-Spread Estimation and Surplus Guard Interval Utilization in IEEE 802.11be Environments

Herein, an uncertainty quantification-based framework is proposed for estimating the root mean square (RMS) delay spread σ as a probability distribution in IEEE 802.11be environments. The method selects a guard interval (GI) using a safety margin derived from the 90th-percentile estimate σ^q90 and reuses the surplus GI for secondary transmission. The conventional least squares-based estimator exhibits a large ~150 ns RMS error, yielding over 90% underestimation and inefficient GI utilization. To address this, a lightweight one-dimensional convolutional neural network with 28,259 parameters is trained using pinball loss and a softplus monotonicity constraint to produce the q10, q50, and q90 quantiles of the σ distribution. A safety-margin-based policy, GImin = α·σ^q90, is applied, and the surplus GI carries short fast Fourier transform sub-orthogonal frequency division multiplexing secondary transmission. Over IEEE TGn B, D, and E channels across 810,000 trials at α = 3.5, the method reduces RMS error from 150 to 28 ns, lowers the underestimation rate from 92% to 23% regardless of signal-to-noise ratio, and improves goodput by 14% (9.74 to 11.12 Mbps), comparable to the σ-known baseline. The standard-compliant, backward-compatible scheme provides an extensible architecture for next-generation wireless local area networks.