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Classification of digital modulation schemes using neural networks

Conference Paper


Abstract


  • Modulation recognition systems have to be able to correctly classify the incoming signal's modulation scheme in the presence of noise. This paper addresses the problem of automatic modulation recognition of digital communication signals using neural networks. Seven digital modulation schemes have been considered and seven features have been used as inputs to the neural network (NN) to perform the classification. Several NN structures have been tested that perform at over 99% accuracy at signal-to-noise ratios (SNR) of 10 dB. Design considerations for the NN classifier are discussed and the implementation of these has been shown to produce significant reduction in network size. The performance of the NN-based classifier has also been compared with that of a decision-theoretic classifier; it was found that the NN slightly outperforms the decision-theoretic classifier. © 1999 IEEE.

Publication Date


  • 1999

Citation


  • Arulampalam, G., Ramakonar, V., Bouzerdoum, A., & Habibi, D. (1999). Classification of digital modulation schemes using neural networks. In ISSPA 1999 - Proceedings of the 5th International Symposium on Signal Processing and Its Applications Vol. 2 (pp. 649-652). doi:10.1109/ISSPA.1999.815756

Scopus Eid


  • 2-s2.0-0842263489

Web Of Science Accession Number


Start Page


  • 649

End Page


  • 652

Volume


  • 2

Abstract


  • Modulation recognition systems have to be able to correctly classify the incoming signal's modulation scheme in the presence of noise. This paper addresses the problem of automatic modulation recognition of digital communication signals using neural networks. Seven digital modulation schemes have been considered and seven features have been used as inputs to the neural network (NN) to perform the classification. Several NN structures have been tested that perform at over 99% accuracy at signal-to-noise ratios (SNR) of 10 dB. Design considerations for the NN classifier are discussed and the implementation of these has been shown to produce significant reduction in network size. The performance of the NN-based classifier has also been compared with that of a decision-theoretic classifier; it was found that the NN slightly outperforms the decision-theoretic classifier. © 1999 IEEE.

Publication Date


  • 1999

Citation


  • Arulampalam, G., Ramakonar, V., Bouzerdoum, A., & Habibi, D. (1999). Classification of digital modulation schemes using neural networks. In ISSPA 1999 - Proceedings of the 5th International Symposium on Signal Processing and Its Applications Vol. 2 (pp. 649-652). doi:10.1109/ISSPA.1999.815756

Scopus Eid


  • 2-s2.0-0842263489

Web Of Science Accession Number


Start Page


  • 649

End Page


  • 652

Volume


  • 2