Finding the Missing Data: A BERT-inspired Approach Against Package Loss
  in Wireless Sensing
Zijian Zhao Tingwei Chen Fanyi Meng Hang Li Xiaoyang Li Guangxu Zhu
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.