Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing
<|reference_start|>Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing: Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models. To overcome this challenge, we propose a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) for CSI recovery, named CSI-BERT. CSI-BERT can be trained in an self-supervised manner on the target dataset without the need for additional data. Furthermore, unlike traditional interpolation methods that focus on one subcarrier at a time, CSI-BERT captures the sequential relationships across different subcarriers. Experimental results demonstrate that CSI-BERT achieves lower error rates and faster speed compared to traditional interpolation methods, even when facing with high loss rates. Moreover, by harnessing the recovered CSI obtained from CSI-BERT, other deep learning models like Residual Network and Recurrent Neural Network can achieve an average increase in accuracy of approximately 15\% in Wi-Fi sensing tasks. The collected dataset WiGesture and code for our model are publicly available at https://github.com/RS2002/CSI-BERT.<|reference_end|>
@article{zhao2024finding,
    title={Finding the Missing Data: A BERT-inspired Approach Against Package Loss
  in Wireless Sensing},
    author={Zijian Zhao, Tingwei Chen, Fanyi Meng, Hang Li, Xiaoyang Li, and
  Guangxu Zhu},
    journal={arXiv preprint arXiv:2403.12400},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12400},
    primaryClass={cs.LG cs.AI eess.SP}
}