Performance study of cyclostationary based digital modulation classification schemes
| Autor: | Barathram Ramkumar, M. S. Manikandan, Udit Satija |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Structured support vector machine
Computer science Cyclostationary process business.industry Speech recognition Pattern recognition Linear classifier Quadratic classifier Linear discriminant analysis Support vector machine Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Margin classifier Artificial intelligence business |
| Zdroj: | 2014 9th International Conference on Industrial and Information Systems (ICIIS). |
| DOI: | 10.1109/iciinfs.2014.7036609 |
| Popis: | Automatic Modulation Classification (AMC) is a essential component in Cognitive Radio (CR) for recognizing the modulation scheme. Many modulated signals manifest the property of cyclostationarity as a feature so it can be exploited for classification. In this paper, we study the performance of digital modulation classification technique based on the cy-clostationary features and different classifiers such as Neural Network, Support Vector Machine, k-Nearest Neighbor, Naive Bayes, Linear Discriminant Analysis and Neuro-Fuzzy classifier. In this study we considered modulations i.e. BPSK, QPSK, FSK and MSK for classification. All classification methods studied using performance matrix including classification accuracy and computational complexity (time). The robustness of these methods are studied with SNR ranging from 0 to 20dB. Based upon the result we found that combining cyclostationary features with Naive Bayes and Linear Discriminant Analysis classifiers leads to provide better classification accuracy with less computational complexity. |
| Databáze: | OpenAIRE |
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