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

2024-03-19 03:16:52
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 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:



3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot