Neuro-Wideband WiFi Sensing via Self-Conditioned CSI Extrapolation

Abstract

WiFi sensing has suffered from the limited bandwidths designated for its original communication purpose, leading to fundamental limits in multipath resolution and thus multi-user sensing. Unfortunately, it is practically prohibitive to obtain large bandwidths on commercial WiFi, considering the conflict between the limited spectrum and the crowded networks. In this paper, we present Neuro-Wideband (NWB), a completely different paradigm that enables wideband WiFi sensing without specialized hardware or extra channel measurements. Our key insight is that any physical measurement of channel state information (CSI) inherently encapsulates multipath parameters, which, while unsolvable in isolation, can be transformed into an expanded form of CSI (eCSI) approximating measurements over a broader bandwidth. To ground this insight, we propose WUKONG to address NWB as a unique self-conditioned learning problem that can be trained by using any existing CSI data as self-labeled samples. WUKONG introduces a novel deep learning framework by integrating Transformer and Diffusion models, which captures sample-specific multipath parameters and transfers this sample-level knowledge to the outcome eCSI. We conduct real-world experiments to evaluate WUKONG on diverse WiFi signals across protocols and bandwidths. The results show the promising effectiveness of NWB, which is further demonstrated through case studies on localization and multi-person breathing monitoring using eCSI. Overall, the proposed NWB promises a practical pathway toward realizing wideband WiFi sensing on commodity hardware, expanding the design space of wireless sensing systems.