WiSR: Wireless Domain Generalization Based on Style Randomization

Current wireless cross-domain solutions are limited to cross-one-factor tasks, requiring target domain data participation for training or position-independent feature extraction using multiple transceivers. Therefore, this paper aims to demonstrate cross-domain wireless sensing in a more challenging domain generalization (DG) setting without multiple transceivers or target domain data. Specifically, we propose a style-randomized cross-domain wireless sensing model called WiSR, which extracts domain-invariant features from multiple source domains. It quantifies Channel State Information (CSI) differences in the subcarrier dimensions as subcarrier-domain styles and instructs the feature extractor to gradually bias the gesture signals by randomizing the subcarrier-domain styles at the feature level. Meanwhile, a domain classifier that shares the same feature extractor is instructed to gradually bias the domain signals by randomizing the gesture features. Then, the adversarial training framework enables the domain classifier to reduce the influence of domain signals on the feature extractor. Extensive experiments have been performed on three gesture datasets with varying amounts of subcarriers from devices with different NICs, including cross-one-factor (such as room, user, location, and orientation) and cross-multi-factor sensing tasks. The results demonstrate that our method considerably increases performance on wireless DG tasks. Our code is available at: https://github.com/LiuSjia/WiSR .