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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: 30 January 2026 | Viewed by 2160

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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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 (3 papers)

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Research

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26 pages, 9222 KiB  
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 166
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 KiB  
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 3 | Viewed by 1344
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 KiB  
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
Viewed by 270
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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