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Cited by 447Cognitive Radio Networks and Spectrum SensingWireless Signal Modulation ClassificationRadar Systems and Signal Processing

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A new approach to signal classification using spectral correlation and neural networks

Fehske, A., Gaeddert, J., Reed, J.H.·2005·First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005.
DOI 10.1109/dyspan.2005.1542629
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Abstract

Channel sensing and spectrum allocation has long been of interest as a prospective addition to cognitive radios for wireless communications systems occupying license-free bands. Conventional approaches to cyclic spectral analysis have been proposed as a method for classifying signals for applications where the carrier frequency and bandwidths are unknown, but is, however, computationally complex and requires a significant amount of observation time for adequate performance. Neural networks have been used for signal classification, but only for situations where the baseband signal is present. By combining these techniques a more efficient and reliable classifier can be developed where a significant amount of processing is performed offline, thus reducing online computation. In this paper we take a renewed look at signal classification using spectral coherence and neural networks, the performance of which is characterized by Monte Carlo simulations

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Research works printed but not linked: 23

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Metadata

Authors
Fehske, A., Gaeddert, J., & Reed, J. H.
Affiliations
Virginia Tech (US)
ORCID
Venue
First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005.
Publisher
IEEE
Published
2005-12-10
Pages
144-150
DOI
10.1109/dyspan.2005.1542629
OpenAlex
W2106186328
Cited by
447
References
0
Citations by year
2026: 2 · 2025: 6 · 2024: 11 · 2023: 8 · 2022: 12 · 2021: 26 · 2020: 12 · 2019: 24 · 2018: 31 · 2017: 25 · 2016: 26 · 2015: 14 · 2014: 26 · 2013: 36 · 2012: 33
FWCI
7.66
Type
conference paper
Language
en
Open access
closed
Locations
IEEE International Symposium on Dynamic Spectrum Access Networks (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
  • Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
  • Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
Keywords
signal classification, spectral correlation, spectral coherence, artificial neural networks, cognitive radio, spectrum allocation, channel sensing, cyclic spectral analysis, offline preprocessing

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CoChat. (2026). Citation check: A new approach to signal classification using spectral correlation and neural networks. https://app.cochat.ai/papers/10.1109/dyspan.2005.1542629
The paper
Fehske, A., Gaeddert, J., & Reed, J. H. (2005). A new approach to signal classification using spectral correlation and neural networks. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. https://doi.org/10.1109/dyspan.2005.1542629