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A new approach to signal classification using spectral correlation and neural networks
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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“Some recent methods take a step beyond this using pattern recognition on expert feature maps, such as the spectral coherence function or α -profile, combined with NN-based classification [54].”
From An Introduction to Deep Learning for the Physical Layer (2017) · cites this paper as [54]
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NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey
Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2006) · Computer Networks · doi:10.1016/j.comnet.2006.05.001 · Cited by 6,356
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
Metadata
- Authors
- Fehske, A., Gaeddert, J., & Reed, J. H.
- Affiliations
- Virginia Tech (US)
- 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
- 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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