KronDPBL: Self-Supervised Pattern–Noise Disentanglement Towards Transferable WiFi CSI Representation Learning

WiFi Channel State Information (CSI) has become a promising sensing modality for device-free human activity recognition. However, raw CSI measurements are contaminated by hardware impairments and environmental noise, leading deep neural networks to learn spurious, noise-dependent features that generalize poorly across domains. We propose KronDPBL, a self-supervised representation learning framework that explicitly disentangles recurring signal patterns from segment-specific noise to learn robust and transferable CSI representations. KronDPBL introduces a distribution-guided cross-attention mechanism that separates persistent signal motifs from transient perturbations, together with an asymmetric attention design that retains noise information in the key branch for contrastive similarity estimation while suppressing it in the value branch to synthesize noise-free pattern representations. To exploit the intrinsic structure of CSI measurements, KronDPBL further incorporates Kronecker-sum spatial encoding and a low-rank reconstruction objective, a parameter-efficient two-axis spatial prior into the learned representations. The model is trained with dual contrastive objectives that jointly enforce pattern invariance and noise disentanglement, applied to each corpus under a strict fold-wise protocol, with encoders frozen after pretraining, and producing representations that transfer effectively across sensing tasks. Extensive experiments on six public CSI benchmark datasets demonstrate that a frozen KronDPBL encoder without end-to-end fine-tuning achieves state-of-the-art performance on 11 of 13 downstream tasks, improving accuracy by up to 14.6% over prior methods. Furthermore, KronDPBL consistently exhibits superior robustness under severe signal perturbations, outperforming existing approaches by 5.6%–23.0%.