ResDiff: Hardware-Aware Physical-Layer Covert Communication via Diffusion-Based Residual Perturbation

Physical-layer covert communication is increasingly challenged by powerful detectors that exploit the fine-grained statistical structure of received signals. In realistic Radio Frequency (RF) front ends, signal-dependent impairments such as power amplifier (PA) nonlinearity and In-phase and Quadrature (I/Q) imbalance induce transmitter-specific, non-Gaussian emission statistics under which conventional Gaussian embedding rules cause detectable distribution drift. We propose ResDiff, a two-stage learn-then-embed framework that first trains a symbol-conditional diffusion prior to capture a hardware-consistent emission manifold, then embeds covert information through bounded, variance-adaptive residuals spread over a K-symbol block with coherent block decoding at the legitimate receiver. Simulations under a severe impairment profile in an Additive White Gaussian Noise (AWGN) channel show that ResDiff improves stealthiness while maintaining reliable covert recovery and that increasing K reduces detectability by lowering the per symbol embedding pressure. Overall, the results indicate that hardware-aware generative priors, combined with rate-controlled block embedding, provide a practical path to covert-in-cover-traffic communication under modern detection capabilities.