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Sensors for Enabling Wireless Spectrum Access
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Sensors for Enabling Wireless Spectrum Access

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

Deadline for manuscript submissions: closed (30 January 2026) | Viewed by 5525

Special Issue Editors

Dr. Joseph Gaeddert

E-Mail Website
Guest Editor
Virginia Tech National Security Institute, Blacksburg, VA 24061, USA
Interests: software-defined radio, digital communications, radio environment maps
Prof. Dr. Alan Michaels

E-Mail Website
Guest Editor
Virginia Tech Hume Center for National Security and Technology, Virginia Tech, Blacksburg, VA 24061, USA
Interests: digital communications; satellite communications; radio frequency machine learning; digital chaos; non-traditional hardware
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global demand for available wireless spectrum for reliable and high-rate data communications has grown at an astounding rate, far outpacing their enabling standards. As a consequence, the need to dynamically sense spectrum occupancy, avoid primary user interference, and opportunistically leverage unused frequency bands has been an ongoing area of research for some time; however, recent advancements in low-cost software-defined radio hardware, signal processing algorithms, and even machine-learning tools have bolstered the need for high-quality spectrum-sensing capabilities. These enabling technologies have brought capabilities in dynamic spectrum access networking to an even wider group of researchers than ever before.

The scope of this Special Issue to address spectrum monitoring needs includes, but is not limited to, the following: phased arrays for spectrum sensing; software-defined radios for spectrum sensing; digital signal processing spectrum sensing algorithms; and machine-learning architectures for spectrum sensing.

Dr. Joseph Gaeddert
Prof. Dr. Alan Michaels
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • radio frequency (RF) wireless communications
  • spectrum sensing
  • dynamic spectrum access
  • software-defined radio
  • 5g/cellular communications
  • satellite communications
  • radar coexistence
  • antennas and phased arrays
  • digital signal processing

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Published Papers (5 papers)

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Research

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21 pages, 3170 KB  
Article
Reliable Communication in Distributed Photovoltaic Sensor Networks: A Large Language Model-Driven Approach
by Wu Dong, Xu Liu, Qing Liu, Guanghui Zhang, Ji Shi, Xun Zhao, Zhongming Lei and Wei Wang
Sensors 2026, 26(3), 838; https://doi.org/10.3390/s26030838 - 27 Jan 2026
Viewed by 248
Abstract
Distributed photovoltaic (DPV) systems present a cost-effective and sustainable industrial energy solution, yet their reliable monitoring faces significant technological constraints. This paper proposes a hierarchical optimization framework that integrates hysteresis-based traffic shaping at the network layer with Large Language Model (LLM)-driven diagnostics at [...] Read more.
Distributed photovoltaic (DPV) systems present a cost-effective and sustainable industrial energy solution, yet their reliable monitoring faces significant technological constraints. This paper proposes a hierarchical optimization framework that integrates hysteresis-based traffic shaping at the network layer with Large Language Model (LLM)-driven diagnostics at the application layer. The proposed dynamic algorithm minimizes latency and downtime by prioritizing critical fault data. Priority-based scheduling ensures this critical data is transmitted preferentially over routine sensor readings. At the application layer, the system utilizes physics-informed prompt engineering to perform zero-shot root cause analysis, circumventing the training data requirements of traditional classifiers. Under a 10 Mbps gateway bandwidth, our method achieves a 46.08% to 49.87% reduction in P50 latency compared to traditional approaches. Moreover, the LLM-powered diagnostic system provides detailed assessments, enabling precise fault diagnosis for DPV systems. Full article
(This article belongs to the Special Issue Sensors for Enabling Wireless Spectrum Access)
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25 pages, 1178 KB  
Article
Leveraging Machine Learning Classifiers in Transfer Learning for Few-Shot Modulation Recognition
by Song Li, Yong Wang, Jun Xiong and Xia Wang
Sensors 2026, 26(2), 674; https://doi.org/10.3390/s26020674 - 20 Jan 2026
Viewed by 153
Abstract
The rapid advancement of communication systems has heightened the demand for efficient and robust modulation recognition. Conventional deep learning-based methods, however, often struggle in practical few-shot scenarios where acquiring sufficient labeled training data is prohibitive. To bridge this gap, this paper proposes a [...] Read more.
The rapid advancement of communication systems has heightened the demand for efficient and robust modulation recognition. Conventional deep learning-based methods, however, often struggle in practical few-shot scenarios where acquiring sufficient labeled training data is prohibitive. To bridge this gap, this paper proposes a hybrid transfer learning (HTL) approach that synergistically combines the representation power of deep feature extraction with the flexibility and stability of traditional machine learning (ML) classifiers. The proposed method capitalizes on knowledge transferred from large-scale auxiliary datasets through pre-training, followed by few-shot adaptation using simple ML classifiers. Multiple classical ML classifiers are incorporated and evaluated within the HTL framework for few-shot modulation recognition (FSMR). Comprehensive experiments demonstrate that HTL consistently outperforms existing baseline methods in such data-scarce settings. Furthermore, a detailed analysis of several key parameters is conducted to assess their impact on performance and to inform deployment in practical environments. Notably, the results indicate that the K-nearest neighbor classifier, owing to its instance-based and non-parametric nature, delivers the most robust and generalizable performance within the HTL paradigm, offering a promising solution for reliable FSMR in real-world applications. Full article
(This article belongs to the Special Issue Sensors for Enabling Wireless Spectrum Access)
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26 pages, 9222 KB  
Article
Evaluation of Confusion Behaviors in SEI Models
by Brennan Olds, Ethan Maas and Alan J. Michaels
Sensors 2025, 25(13), 4006; https://doi.org/10.3390/s25134006 - 27 Jun 2025
Viewed by 742
Abstract
Radio Frequency Machine Learning (RFML) has in recent years become a popular method for performing a variety of classification tasks on received signals. Among these tasks is Specific Emitter Identification (SEI), which seeks to associate a received signal with the physical emitter that [...] Read more.
Radio Frequency Machine Learning (RFML) has in recent years become a popular method for performing a variety of classification tasks on received signals. Among these tasks is Specific Emitter Identification (SEI), which seeks to associate a received signal with the physical emitter that transmitted it. Many different model architectures, including individual classifiers and ensemble methods, have proven their capabilities for producing high accuracy classification results when performing SEI. Though the works studying different model architectures report on successes, there is a notable absence regarding the examination of systemic failures and negative traits associated with learned behaviors. This work studies those failure patterns for a 64-radio SEI classification problem by isolating common patterns in incorrect classification results across multiple model architectures and two distinct control variables: Signal-to-Noise Ratio (SNR) and the quantity of training data utilized. This work finds that many of the RFML-based models devolve to selecting from amongst a small subset of classes (≈10% of classes) as SNRs decrease and that observed errors are reasonably consistent across different SEI models and architectures. Moreover, our results validate the expectation that ensemble models are generally less brittle, particularly at a low SNR, yet they appear not to be the highest-performing option at a high SNR. Full article
(This article belongs to the Special Issue Sensors for Enabling Wireless Spectrum Access)
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15 pages, 4276 KB  
Article
Spectrum Sensing Method Based on STFT-RADN in Cognitive Radio Networks
by Anyi Wang, Tao Zhu and Qifeng Meng
Sensors 2024, 24(17), 5792; https://doi.org/10.3390/s24175792 - 6 Sep 2024
Cited by 7 | Viewed by 2306
Abstract
To address the common issues in traditional convolutional neural network (CNN)-based spectrum sensing algorithms in cognitive radio networks (CRNs), including inadequate signal feature representation, inefficient utilization of feature map information, and limited feature extraction capabilities due to shallow network structures, this paper proposes [...] Read more.
To address the common issues in traditional convolutional neural network (CNN)-based spectrum sensing algorithms in cognitive radio networks (CRNs), including inadequate signal feature representation, inefficient utilization of feature map information, and limited feature extraction capabilities due to shallow network structures, this paper proposes a spectrum sensing algorithm based on a short-time Fourier transform (STFT) and residual attention dense network (RADN). Specifically, the RADN model improves the basic residual block and introduces the convolutional block attention module (CBAM), combining residual connections and dense connections to form a powerful deep feature extraction structure known as residual in dense (RID). This significantly enhances the network’s feature extraction capabilities. By performing STFT on the received signals and normalizing them, the signals are converted into time–frequency spectrograms as network inputs, better capturing signal features. The RADN is trained to extract abstract features from the time–frequency images, and the trained RADN serves as the final classifier for spectrum sensing. Experimental results demonstrate that the STFT-RADN spectrum sensing method significantly improves performance under low signal-to-noise ratio (SNR) conditions compared to traditional deep-learning-based methods. This method not only adapts to various modulation schemes but also exhibits high detection probability and strong robustness. Full article
(This article belongs to the Special Issue Sensors for Enabling Wireless Spectrum Access)
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Review

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28 pages, 372 KB  
Review
A Survey of Performance Metrics for Spectrum Sensing and Spectrum Hole Geolocation for Wireless Spectrum Access
by Dayan Adionel Guimarães
Sensors 2025, 25(12), 3770; https://doi.org/10.3390/s25123770 - 17 Jun 2025
Cited by 3 | Viewed by 1468
Abstract
This paper presents a survey of performance metrics applicable to spectrum sensing and spectrum hole geolocation within the context of dynamic spectrum access (DSA) in cognitive radio networks. While grounded in binary hypothesis testing, the review emphasizes metrics specialized for sensing reliability, interference [...] Read more.
This paper presents a survey of performance metrics applicable to spectrum sensing and spectrum hole geolocation within the context of dynamic spectrum access (DSA) in cognitive radio networks. While grounded in binary hypothesis testing, the review emphasizes metrics specialized for sensing reliability, interference risk, spatial accuracy, and network efficiency. The work also highlights trade-offs among metrics and provides guidelines for their practical application. A key contribution of this work is to provide researchers and practitioners with a comprehensive set of evaluation tools, extending well beyond the applicability of the conventional probabilities of detection and false alarm. Full article
(This article belongs to the Special Issue Sensors for Enabling Wireless Spectrum Access)
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