Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing

• URL: http://arxiv.org/abs/2403.12400v1
• Date: Tue, 19 Mar 2024 03:16:52 GMT
• Title: Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing
• Authors: Zijian Zhao, Tingwei Chen, Fanyi Meng, Hang Li, Xiaoyang Li, Guangxu Zhu,
• Abstract summary: We propose a deep learning model based on Bidirectional Representations from Transformers (BERT) for CSI recovery. CSI-BERT can be trained in an self-supervised manner on the target dataset without the need for additional data. Experimental results demonstrate that CSI-BERT achieves lower error rates and faster speed compared to traditional methods.
• Score: 14.973433993744708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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