Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio

Spectrum awareness is currently one of the most challenging problems in cognitive radio (CR) design. Detection and classification of very low SNR signals with relaxed information on the signal parameters being detected is critical for proper CR functionality as it enables the CR to react and adapt t...

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Bibliographic Details
Published in2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks pp. 212 - 215
Main Authors Kim, Kyouwoong, Akbar, Ihsan A., Bae, Kyung K., Um, Jung-Sun, Spooner, Chad M., Reed, Jeffrey H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2007
Subjects
Adaptive signal detection
Chromium
Cognitive radio
Feature extraction
Frequency domain analysis
Hidden Markov models
Pattern classification
Robustness
Signal detection
Signal processing
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Summary:Spectrum awareness is currently one of the most challenging problems in cognitive radio (CR) design. Detection and classification of very low SNR signals with relaxed information on the signal parameters being detected is critical for proper CR functionality as it enables the CR to react and adapt to the changes in its radio environment. In this work, the cycle frequency domain profile (CDP) is used for signal detection and preprocessing for signal classification. Signal features are extracted from CDP using a threshold-test method. For classification, a Hidden Markov Model (HMM) has been used to process extracted signal features due to its robust pattern-matching capability. We also investigate the effects of varied observation length on signal detection and classification. It is found that the CDP-based detector and the HMM-based classifier can detect and classify incoming signals at a range of low SNRs.
ISBN:1424406633
9781424406630
DOI:10.1109/DYSPAN.2007.35