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Radio Frequency AI/ML (RF AI/ML) for Wireless Spectrum Sensing and Awareness

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 1 September 2025 | Viewed by 503

Special Issue Editors


E-Mail Website
Guest Editor
Virginia Tech National Security Institute, Spectrum Dominance Division, Blacksburg, VA, USA
Interests: digital signal processing; wireless spectrum sensing; radio frequency machine learning; adversarial machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Virginia Tech National Security Institute, Spectrum Dominance Division, Blacksburg, VA, USA
Interests: machine learning

Special Issue Information

Dear Colleagues,

In recent years, the rapid growth in AI/ML technologies in other modalities (images, text, audio, video, etc.) has led to an increased interest in these technologies for use in the radio frequency domain. While this field of research is becoming ever more popular, primarily due to its promise in solving commercial spectrum scarcity issues and its inherent importance in military communications, a vast majority of the research to date has been focused on the transfer of these technologies/solutions directly to RF applications. 

In this Special Issue, we will collate original research on recent advances, technologies, solutions, applications, and challenges in the field of radio frequency spectrum sensing and awareness that go beyond transfer learning and investigate novel research from an RF-domain-centric perspective. 

Potential topics include, but are not limited to, the following:

  • RF AI/ML Applications
    • Spectrum Scanning and Prioritization;
    • Signal Detection;
    • Signal Classification/Identification;
    • Specific Emitter Identification;
    • Co-Channel Signal Separation;
    • Multi-Antenna Sensing;
    • Collaborative Sensing.
  • RF AI/ML Datasets
    • Modality Investigations (Image-based, IQ-based, feature-based, etc.);
    • “Cyborg” Dataset Generation (intelligent use of synthetic and real data);
    • Datasets/Tools (contribution is the dataset/tool with numerical experiments as baselines).
  • RF AI/ML Security
    • Adversarial Attacks;
    • Adversarial Hardening.
  • RF AI/ML Trust
    • Uncertainty Quantification;
    • Explainability.

Dr. William Headley
Dr. Stephen Adams
Guest Editors

Manuscript Submission Information

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Keywords

  • radio frequency
  • spectrum sensing
  • RF AI/ML

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Published Papers (1 paper)

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Research

17 pages, 2421 KiB  
Article
Cross-Receiver Radio Frequency Fingerprint Identification: A Source-Free Adaptation Approach
by Jian Yang, Shaoxian Zhu, Zhongyi Wen and Qiang Li
Sensors 2025, 25(14), 4451; https://doi.org/10.3390/s25144451 - 17 Jul 2025
Viewed by 225
Abstract
Radio frequency fingerprint identification (RFFI) leverages the unique characteristics of radio signals resulting from inherent hardware imperfections for identification, making it essential for applications in telecommunications, cybersecurity, and surveillance. Despite the advancements brought by deep learning in enhancing RFFI accuracy, challenges persist in [...] Read more.
Radio frequency fingerprint identification (RFFI) leverages the unique characteristics of radio signals resulting from inherent hardware imperfections for identification, making it essential for applications in telecommunications, cybersecurity, and surveillance. Despite the advancements brought by deep learning in enhancing RFFI accuracy, challenges persist in model deployment, particularly when transferring RFFI models across different receivers. Variations in receiver hardware can lead to significant performance declines due to shifts in data distribution. This paper introduces the source-free cross-receiver RFFI (SCRFFI) problem, which centers on adapting pre-trained RF fingerprinting models to new receivers without needing access to original training data from other devices, addressing concerns of data privacy and transmission limitations. We propose a novel approach called contrastive source-free cross-receiver network (CSCNet), which employs contrastive learning to facilitate model adaptation using only unlabeled data from the deployed receiver. By incorporating a three-pronged loss function strategy—minimizing information entropy loss, implementing pseudo-label self-supervised loss, and leveraging contrastive learning loss—CSCNet effectively captures the relationships between signal samples, enhancing recognition accuracy and robustness, thereby directly mitigating the impact of receiver variations and the absence of source data. Our theoretical analysis provides a solid foundation for the generalization performance of SCRFFI, which is corroborated by extensive experiments on real-world datasets, where under realistic noise and channel conditions, that CSCNet significantly improves recognition accuracy and robustness, achieving an average improvement of at least 13% over existing methods and, notably, a 47% increase in specific challenging cross-receiver adaptation tasks. Full article
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