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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 1507

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 (1 paper)

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Research

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 1198
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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