Open Access

REVIEW

Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey

Binglei Yue, Aili Jiang, Chun Yang, Junwei Lei, Heng Liu, Yin Zhang^*

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

* Corresponding Author: Yin Zhang. Email:

Computers, Materials & Continua 2026, 86(1), 1-28. https://doi.org/10.32604/cmc.2025.071047

Received 30 July 2025; Accepted 29 September 2025; Issue published 10 November 2025

Abstract

With the growing advancement of wireless communication technologies, WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution. Among the available signal types, Channel State Information (CSI) offers fine-grained temporal, frequency, and spatial insights into multipath propagation, making it a crucial data source for human-centric sensing. Recently, the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments. This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI. We first outline mainstream CSI acquisition tools and their hardware specifications, then provide a detailed discussion of preprocessing methods such as denoising, time–frequency transformation, data segmentation, and augmentation. Subsequently, we categorize deep learning approaches according to sensing tasks—namely detection, localization, and recognition—and highlight representative models across application scenarios. Finally, we examine key challenges including domain generalization, multi-user interference, and limited data availability, and we propose future research directions involving lightweight model deployment, multimodal data fusion, and semantic-level sensing.

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