通过融合多普勒特征与混合模型，提升低带宽下Wi-Fi活动识别准确率。
Hybrid Deep Learning Framework for CSI-Based Activity Recognition in Bandwidth-Constrained Wi-Fi Sensing
- 先提取多普勒特征增强运动信号，再用Inception+BiLSTM融合空间与时间特征
- 在20/40/80MHz下准确率达89.27%/94.13%/95.30%，低带宽下优势显著
- 适合资源受限的无线传感场景，如智能家居、医疗监护
本文提出一种新型混合深度学习框架，用于提升带宽受限环境下基于信道状态信息（CSI）的人体活动识别（HAR）鲁棒性。核心方法包括：首先通过多普勒轨迹提取阶段增强运动相关信号特征；随后利用集成Inception网络（负责分层空间特征提取）与双向长短期记忆网络（BiLSTM，捕捉时序依赖）的混合神经架构进行处理；最后采用支持向量机（SVM）作为分类层优化决策边界。该框架在公开数据集上于20、40和80 MHz三种带宽配置下进行了系统验证，分别获得89.27%、94.13%和95.30%的准确率。结果表明，在最严苛的低带宽场景中，其性能显著优于单一深度学习基线模型，证实了结合多普勒特征工程与混合学习架构在带宽受限无线感知应用中的有效性。
原文摘要 · Abstract (English)
This paper presents a novel hybrid deep learning framework designed to enhance the robustness of CSI-based Human Activity Recognition (HAR) within bandwidth-constrained Wi-Fi sensing environments. The core of our proposed methodology is a preliminary Doppler trace extraction stage, implemented to amplify salient motion-related signal features before classification. Subsequently, these enhanced inputs are processed by a hybrid neural architecture, which integrates Inception networks responsible for hierarchical spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks that capture temporal dependencies. A Support Vector Machine (SVM) is then utilized as the final classification layer to optimize decision boundaries. The framework's efficacy was systematically validated using a public dataset across 20, 40, and 80 MHz bandwidth configurations. The model yielded accuracies of 89.27% (20 MHz), 94.13% (40 MHz), and 95.30% (80 MHz), respectively. These results confirm a marked superiority over standalone deep learning baselines, especially in the most constrained low-bandwidth scenarios. This study underscores the utility of combining Doppler-based feature engineering with a hybrid learning architecture for reliable HAR in bandwidth-limited wireless sensing applications.
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