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Smart Grid and Innovative Frontiers in Telecommunications. 8th EAI International Conference, EAI SmartGIFT 2024a, Santa Clara, United States, March 23-24, 2024, Proceedings

Research Article

Machine Learning for Ambient Backscatter Channel Estimation and Signal Detection: Opportunities and Challenges

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-78806-2_1,
        author={Diancheng Cheng and Fan Wu and Cong Zhang and Jinjin Sun and Yuan’an Liu},
        title={Machine Learning for Ambient Backscatter Channel Estimation and Signal Detection: Opportunities and Challenges},
        proceedings={Smart Grid and Innovative Frontiers in Telecommunications. 8th EAI International Conference, EAI SmartGIFT 2024a, Santa Clara, United States, March 23-24, 2024, Proceedings},
        proceedings_a={SMARTGIFT},
        year={2025},
        month={1},
        keywords={Ambient Backscatter Communication Machine Learning Channel Estimation Signal Detection},
        doi={10.1007/978-3-031-78806-2_1}
    }
    
  • Diancheng Cheng
    Fan Wu
    Cong Zhang
    Jinjin Sun
    Yuan’an Liu
    Year: 2025
    Machine Learning for Ambient Backscatter Channel Estimation and Signal Detection: Opportunities and Challenges
    SMARTGIFT
    Springer
    DOI: 10.1007/978-3-031-78806-2_1
Diancheng Cheng1, Fan Wu1,*, Cong Zhang1, Jinjin Sun1, Yuan’an Liu1
  • 1: Beijing University of Posts and Telecommunications
*Contact email: wufanwww@bupt.edu.cn

Abstract

As a promising low-power connection paradigm in the ubiquitous Internet of Things (IoT), ambient backscatter communication (AmBC) collects energy from ambient radio frequency (RF) signals while using them as carrier signals, which brings ultra-low power consumption and deployment cost. However, it has not been widely applied in practice because of its difficulties in weak signal detection. To overcome these difficulties, machine learning (ML)-based methods have been highlighted recently. ML methods can achieve accurate signal processing under a low receive signal-to-interference-plus-noise ratio (SINR) in unpredictable interference communication scenarios, benefiting from their outstanding inference and classification tools. In this survey, a brief review of AmBC is first introduced and the four-fold signal-receiving challenges of AmBC are discussed. After that, two key signal processing technologies, i.e., AmBC channel estimation and AmBC signal detection, are emphasized. The representative ML-based methods of AmBC channel estimation and AmBC signal detection are summarized, following their advantages and disadvantages. Finally, some valuable research directions on this topic are introduced to guide future research.

Keywords
Ambient Backscatter Communication Machine Learning Channel Estimation Signal Detection
Published
2025-01-09
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-78806-2_1
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