FELoc: a feature equalization method based on channel state information for Wi-Fi indoor localization✩

Ken Long; Jincheng Yang; Guoxu Xia

doi:10.1016/j.dcan.2026.02.002

›› 2026, Vol. 12 ›› Issue (5) :813 -824. DOI: 10.1016/j.dcan.2026.02.002

Research Article

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FELoc: a feature equalization method based on channel state information for Wi-Fi indoor localization^✩

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School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications , Chongqing, 400065, China

^* E-mail addresses: longken@cqupt.edu.cn (K. Long),

S230131131@stu.cqupt.edu.cn (J. Yang),

S220101166@stu.cqupt.edu.cn (G. Xia).

Ken Long received the MS and PhD degrees in signal and information processing from the Beijing University of Posts and Telecommunications, Beijing, China, in 2004 and 2009, respectively. Since 2009, he has been the Major Investigator with the Mobile Communications Key Laboratory, Chongqing University of Posts and Telecommunications (CQUPT). He has also been a Research Supervisor with CQUPT. He has published some articles on these topics and holds one patent on wireless technology. His main professional interests are within the areas of wireless communications, next generation mobile networks, and signal processing.

Jincheng Yang received the BS degree from Ningbo University of Technology, China, in 2023. He is currently working toward the MS degree in electronic information technology with the Chongqing University of Posts and Telecommunications, Chongqing, China. His research interests include indoor localization, deep learning, and its applications.

Guoxu Xia received the MS degree in Communications and Information Engineering from Chongqing University of Posts and Telecommunications, China, in 2025. His research interests include RIS, deep learning, MIMO, and cell-free networks.

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Abstract

With the rapid development of 5G-A technology, Wi-Fi-based indoor ﬁngerprint localization has gained prominence for its high-precision indoor localization. However, conventional methods often suﬀer from noise interference and indistinct ﬁngerprints in complex environments, reducing the accuracy of localization. To address these issues, the Feature Equalization Localization Method (FELoc) based on Channel State Information (CSI) is proposed in this paper. A uniﬁed preprocessing strategy named WaveICA is designed to enhance ﬁngerprint quality by combining Hierarchical Thresholding Wavelet Denoising (HTWD) for noise suppression and Independent Component Analysis (ICA) for extracting independent features, which eﬀectively improves the discriminability of CSI amplitude and phase data from multiple antennas. Additionally, Feature Equalization Localization Network (FELN) is designed for feature extraction and fusion, which employs dual-branch convolution with a Multiscale Channel-spatial Attention Module (MCAM) to enhance salient features and a novel Feature Equalization Module (FEM) to adaptively fuse phase and amplitude representations. Extensive experiments conducted in diverse indoor scenarios demonstrate that FELoc achieves superior localization performance, providing both higher accuracy and stronger robustness compared to other methods.

Keywords

Wi-Fi / Indoor localization / Channel state information / Hierarchical thresholding wavelet denoising / Independent component analysis / Feature equalization localization network

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Ken Long, Jincheng Yang, Guoxu Xia. FELoc: a feature equalization method based on channel state information for Wi-Fi indoor localization^✩. , 2026, 12 (5) : 813-824 DOI:10.1016/j.dcan.2026.02.002

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CRediT authorship contribution statement

Ken Long: Resources, Investigation; Jincheng Yang: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Data curation; Guoxu Xia: Investigation.

Declaration of competing interest

The authors declare that they have no known competing ﬁnancial interests or personal relationships that could have appeared to inﬂuence the work reported in this paper.

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