A Physics-Guided Receiver for WiFi that Learns Hardware Nonlinearities without Training Data

Modern WiFi systems operating under IEEE 802.11ax (WiFi 6) and 802.11be (WiFi 7) suffer from hardware nonlinearities — including power amplifier saturation, ADC quantisation, and OFDM clipping — that conventional linear receivers treat as white Gaussian noise, leaving structured, learnable residuals uncorrected. We propose the K-R receiver, a physics-guided two-step framework that combines model-based MMSE equalisation with a closed-form nonlinear residual corrector trained per packet using existing preamble pilots, requiring no offline dataset and no backpropagation. The 12-feature residual corrector includes a cubic amplitude term physically grounded in the standard Rapp PA model, enabling the framework to exploit model-consistent nonlinear structure rather than learning arbitrary corrections. Under IEEE 802.11ax-compliant simulations across 12 independent scenarios, the proposed receiver achieves consistent spectral efficiency gains of approximately + 0.40–0.43 bps/Hz over MMSE, remains stable under hardware mismatch conditions where deep-learning baselines degrade, and achieves over 60× higher energy efficiency than offline-trained neural receivers. An analytical MSE decomposition reveals that the channel-error correction gain dominates the quantisation gain at low SNR — precisely at the coverage edge, where improvements matter most. These results suggest that physics-guided online learning is a practical and principled alternative to offline-trained neural receivers for real-world WiFi deployment.