A Graph Neural Network-Based Dual Attention Fusion Network for CSI-based Activity Recognition

Over the past decade, Channel State Information (CSI)-based human activity recognition (HAR) has attracted wide attention. Despite significant advancements, existing CSI-based HAR methods primarily face two critical challenges: 1) how to exploit intrinsic hierarchical spatial correlations spanning adjacent sub-carriers while maintaining global awareness of entire CSI series; 2) how to establish a cross-dimensional (e.g., spatial, temporal) optimization framework that enables effective information fusion across distinct feature domains to achieve robust CSI series prediction. To address these challenges, we propose Wi-DualAtt, a novel graph neural network(GNN)-based CSI feature extraction network, specifically designed for the effective fusion of spatial and temporal dimensions. The proposed Wi-DualAtt is composed of three key components: a graph attention network (GAT)-based hierarchical correlation attention network (GHCAN), a temporal feature attention network (TFAN), and a prediction fusion module (PFM). Specifically, GHCAN employs spatial attention to capture the hierarchical correlation among all sub-carriers. Meanwhile, TFAN utilizes an attention layer to extract significant temporal features from CSI samples. Finally, PFM integrates the recognition results from the aforementioned two components, utilizing a knowledge distillation mechanism to form the final recognition result, thereby enhancing the recognition capability for CSI-based HAR systems. Extensive experimental results demonstrate that Wi-DualAtt outperforms several state-of-the-art models, achieving recognition accuracy exceeding 99% across various CSI-based activity recognition scenarios.