A High-Efficient Wi-Fi-Based Cross-Domain Recognition Framework Using Multisource Domain Adaptation for Single-Transceiver Scenarios

With the advancement of deep learning, Wi-Fi-based action recognition methods using channel state information (CSI) rely generally on domain-specific training, and results in performance degradation in unseen domains, which remains a significant challenge. To address this cross-domain recognition, some complexity models are proposed. However, these works mostly rely on multiple Wi-Fi transceivers which is not common in our daily life. To improve the recognition efficiency and reduce the transceiver requirement, we propose a novel framework for the single transceiver scenario which integrates a recursive plots-based CSI sample enhancement strategy with a multisource domain adaptation approach. The CSI sample is first enhanced by using recursive plots. Then, a lightweight convolutional neural network with integrated spatial attention is used to extract initial domain-invariant features. Subsequently, the fine-grained feature is extracted through using dedicated subnetworks. This process aligns the target domain with each source domain and regularizes the target domain outputs across multiple classifiers, thereby enhancing the network’s feature extraction. The proposed model is evaluated on the publicly available Widar3.0 dataset. The results indicate that the proposed method can achieve accuracy rates of 92.6% and 90.2% for cross-location and cross-orientation recognition in single-link scenarios, respectively, and effectively reduce the complexity.