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 this https URL.
Abstract (translated)
尽管已经开发了许多用于Wi-Fi感知的深度学习方法，但包损失通常会导致对信道状态信息（CSI）的非连续估计，这会对学习模型的性能产生负面影响。为了克服这一挑战，我们提出了一个基于双向编码器表示的Transformer（BERT）的CSI恢复深度学习模型，名为CSI-BERT。CSI-BERT可以在无需额外数据的情况下在目标数据集上进行自监督训练。此外，与传统的插值方法不同，CSI-BERT捕捉了不同子载波之间的序列关系。实验结果表明，CSI-BERT在即使面临高损失率的情况下，也实现了与传统插值方法不同的较低误率和较快的速度。此外，通过利用CSI-BERT恢复的CSI，像Residual Network和Recurrent Neural Network这样的深度学习模型可以在Wi-Fi感测任务中实现约15%的准确度平均增加。我们收集的数据集WiGesture及其代码现在可以在这个链接上公开获取：https://github.com/yourgist/CSI-BERT
URL
https://arxiv.org/abs/2403.12400
