한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제17권9호
  • /
  • Pages.2009-2019
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  • 2013
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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Cyclic Moment 및 변형 Cumulant를 기반으로 한 아날로그 및 디지털 변조신호 자동변조인식 알고리즘

Automatic Modulation Recognition Algorithm Based on Cyclic Moment and New Modified Cumulant for Analog and Digital Modulated Signals

  • 투고 : 2013.05.29
  • 심사 : 2013.07.03
  • 발행 : 2013.09.30
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초록

본 논문에서는 cyclic moment 및 새로운 인자인 변형 cumulant를 기반으로 하여 아날로그 및 디지털 신호의 변조방식을 인식하는 알고리즘을 제안한다. 각 변조신호들은 cyclic moment 차수에 따라 서로 다른 cycle frequency 특성을 가진다. 이러한 특성을 분류인자로 하여 다양한 변조신호를 효과적으로 분류해 낼 수 있다. 또한 cycle frequency 특성이 같은 변조신호들 간의 분리를 위해서 진폭 및 위상 변화와 변형 cumulant를 decision tree의 분류인자로 사용하였다. 심볼 수, SNR, 주파수 옵셋을 고려하여 알고리즘 성능검증을 수행하였다. 약 819개의 심볼이 수집되었을 경우, 제안하는 자동변조인식 알고리즘은 SNR 10dB 이상, 주파수 옵셋 25% 이하 조건에서 평균 95% 이상의 정확도를 나타내었다.

In this paper, we propose an automatic modulation recognition algorithm based on cyclic moment and new modified cumulant for analog and digital modulation signals. It is noteworthy that each modulated signal has different cycle frequency characteristics according to its order of cyclic moment. By means of this characteristics as classification features, various modulated signals can be efficiently classified. Also, to identify modulated signals having the same cycle frequency characteristics, we take advantage of the additional classification factors such as variations of envelope and phase as well as modified cumulant. The proposed algorithm was evaluated by considering the number of symbols, SNR, and frequency offset. In the simulation condition where the number of gathered symbols was about 819, and SNR and frequency offset were above 10dB and below 25%, respectively, the average accuracy of the proposed algorithm was more than 95%.

키워드

참고문헌

  1. O. A. Dobre, Y. Bar-Ness and W. Su, "Higher-order cyclic cumulants for high order modulation classification," Military Communications Conference, vol. 1, pp.112-117, 13-16 Oct. 2003.
  2. A. Fehske, J. Gaeddert and J. H. Reed, "A new approach to signal classification using spectral correlation and neural networks," New Frontiers in Dynamic Spectrum Access Networks, pp.144-150, 8-11 Nov. 2005.
  3. A. V. Dandawate and G. B. Giannakis, "Nonparametric polyspectral estimators for kth-order (almost) cyclostationary process," IEEE Transactions on Information Theory, vol. 40, no. 1, pp.67-84, Jan. 1994. https://doi.org/10.1109/18.272456
  4. Z. Yu, "Automatic modulation classification of communication signals," Ph. D. dissertation, New Jersey Institute of Technology, pp.139-203, 2006.
  5. A. V. Dandawate and G. B. Giannakis, "Statistical tests for presence of cyclostationarity," IEEE Transactions on Signal processing, vol. 42, no. 9, pp.2355-2369, Sep. 1994. https://doi.org/10.1109/78.317857
  6. A. K. Nandi and E. E. Azzouz, "Algorithms for automatic modulation recognition of communication signals," IEEE Transactions on Communications, vol. 46, no. 4, pp.431-436, Apr. 1998. https://doi.org/10.1109/26.664294

피인용 문헌

  1. 적응 빔형성기의 입력신호 모델 분석 vol.12, pp.3, 2017, https://doi.org/10.13067/jkiecs.2017.12.3.433