Abstract
Most of the algorithms proposed in the literature deal with the problem of digital modulation classification and consider classic probabilistic or decision tree classifiers. In this paper, we compare and analyze the performance of 2 neural network classifiers and 3 support vector machine classifiers (i.e. 1-v-r type, 1-v-1 type and DAG type multi-class classifier). This paper also deals with the modulation classification problems of classifying both analog and digital modulation signals in military and civilian communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulation signals. It is known that the existing technology is able to classify reliably (accuracy ≥ 90%) only at SNR above 10dB when a large range of modulation types including both digital and analog is being considered. Numerical simulations were conducted to compare performance of classifiers. Results indicated an overall success rate of over 95% at the SNR of 10dB in all classifiers. Especially, it was shown that 3 support vector machine classifiers can achieve the probabilities of correct classification (Pcc) of 96.0%, 97.3% and 97.8% at the SNR of 5dB, respectively.
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References
Le, B., et al.: Modulation Identification using Neural Network for Cognitive Radios. In: SDR forum technical conference (2005)
Dobre, O.A., et al.: The Classification of Joint Analog and Digital Modulations. In: IEEE MILCOM, pp. 3010–3015 (2005)
Dobre, O.A., Abdi, A., Bar-Ness, Y., Su, W.: A survey of Automatic Modulation Classification Techniques: Classical Approaches and New Trends. IEE Proc. Communication (1996)
Kremer, S.C., Sheils, J.: A Testbed for Automatic Modulation Recognition using Artificial Neural Networks. In: IEEE Canadian Conference on Electrical and Computer Engineering, pp. 67–70 (1997)
Nandi, A.K., Azzouz, E.E.: Algorithm for Automatic Modulation Recognition of Communication Signals. IEEE Trans. Communications 46(4), 431–436 (1998)
Azzouz, E.E., Nandi, A.K.: Procedure for Autumatic Recognition of Ananlogue and Digital Modulations. IEE Proc. Communications 143(5), 259–266 (1996)
Park, C.S., Kim, D.Y.: A Novel Robust Feature of Modulation Classification for Reconfigurable Software Radio. IEEE Trans. Consumer Electronics 52(4) (2006)
Lingras, P., Buts, C.: Interval Set Representation of 1-v-r Support Vector Machine Multi-classifiers. In: IEEE Conf. Granular Computing, pp. 193–198 (2006)
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© 2007 Springer Berlin Heidelberg
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Park, CS., Kim, D.Y. (2007). Modulation Classification of Analog and Digital Signals Using Neural Network and Support Vector Machine. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_47
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DOI: https://doi.org/10.1007/978-3-540-72395-0_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
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