Open Access

ARTICLE

Multidomain Correlation-Based Multidimensional CSI Tensor Generation for Device-Free Wi-Fi Sensing

Liufeng Du^1,*, Shaoru Shang¹, Linghua Zhang², Chong Li¹, Jianing Yang³, Xiyan Tian¹

1 School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang, 453003, China
2 School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
3 School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China

* Corresponding Author: Liufeng Du. Email:

(This article belongs to the Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)

Computer Modeling in Engineering & Sciences 2024, 138(2), 1749-1767. https://doi.org/10.32604/cmes.2023.030144

Received 24 March 2023; Accepted 15 May 2023; Issue published 17 November 2023

Abstract

Due to the fine-grained communication scenarios characterization and stability, Wi-Fi channel state information (CSI) has been increasingly applied to indoor sensing tasks recently. Although spatial variations are explicitly reflected in CSI measurements, the representation differences caused by small contextual changes are easily submerged in the fluctuations of multipath effects, especially in device-free Wi-Fi sensing. Most existing data solutions cannot fully exploit the temporal, spatial, and frequency information carried by CSI, which results in insufficient sensing resolution for indoor scenario changes. As a result, the well-liked machine learning (ML)-based CSI sensing models still struggling with stable performance. This paper formulates a time-frequency matrix on the premise of demonstrating that the CSI has low-rank potential and then proposes a distributed factorization algorithm to effectively separate the stable structured information and context fluctuations in the CSI matrix. Finally, a multidimensional tensor is generated by combining the time-frequency gradients of CSI, which contains rich and fine-grained real-time contextual information. Extensive evaluations and case studies highlight the superiority of the proposal.

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Keywords

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