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
Keywords
Computer scienceArtificial neural networkBasebandWirelessArtificial intelligenceComputationSignal processingClassifier (UML)Bandwidth (computing)Cognitive radioPattern recognition (psychology)AlgorithmElectronic engineeringDigital signal processingTelecommunicationsEngineering
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Publication Info
- Year
- 2005
- Type
- article
- Pages
- 144-150
- Citations
- 448
- Access
- Closed
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Cite This
Albrecht Fehske,
Joseph Gaeddert,
Jeffrey H. Reed
(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.
, 144-150.
https://doi.org/10.1109/dyspan.2005.1542629
Identifiers
- DOI
- 10.1109/dyspan.2005.1542629