| Autor: |
Bian, Jichen, Tan, Chong, Tang, Peiyao, Zheng, Min |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Signal Processing Letters, 2024 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/LSP.2024.3397160 |
| Popis: |
Wireless sensing technologies become increasingly prevalent due to the ubiquitous nature of wireless signals and their inherent privacy-friendly characteristics. Device-free personnel identity recognition, a prevalent application in wireless sensing, is susceptibly challenged by imbalanced channel state information (CSI) datasets. This letter proposes a novel method for CSI dataset augmentation that employs Conditional Denoising Diffusion Probabilistic Models (C-DDPMs) to generate additional samples that address class imbalance issues. The augmentation markedly improves classification accuracies on our homemade dataset, elevating all classes to above 94%. |
| Databáze: |
arXiv |
| Externí odkaz: |
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