DOI QR코드

DOI QR Code

Performance of an ML Modulation Classification of QAM Signals with Single-Sample Observation

단일표본관측을 이용한 직교진폭변조 신호의 치운 변조분류 성능

  • 강석근 (경상대학교 전기전자공학부)
  • Published : 2005.02.01

Abstract

In this paper, performance of a maximum-likelihood modulation classification for quadrature amplitude modulation (QAM) is studied. Unlike previous works, the relative classification performance with respect to the available modulations and performance limit with single-sample observation are presented. For those purposes, all constellations are set to have the same minimum Euclidean distance between symbols so that a smaller constellation is a subset of the larger ones. And only one sample of received waveform is used for multiple hypothesis test. As a result, classification performance is improved with increase in signal-to-noise ratio in all the experiments. Especially, when the true modulation format used in the transmitter is 4 QAM, almost perfect classification can be achieved without any additional information or observation samples. Though the possibility of false classification due to the symbols shared by subset constellations always exists, correct classification ratio of $80{\%}$ can be obtained with the single-sample observation when the true modulation formats are 16 and 64 QAM.

본 논문에서는 다중레벨 직교진폭변조 신호론 위한 최우 변조분류기법의 성능을 분석한다. 기존의 연구와는 달리 여기서는 가용 변조방식에 대한 상대적인 분규성능과 단일표본관측을 적용할 경우 최우 분류기의 성능한계 둥을 제시하였다. 이를 위하여 본 논문에서는 가용 성상도에서 심볼간 최소 유클리드 거리론 동일하게 하여 심볼의 수가 자은 성상도가 보다 큰 성상도의 부분집합이 되도록 하였다 그리고 다중가설시험을 위한 표본의 수는 하나로 정하였다. 그 결과 모든 실험에서 신호대잡음비의 증가에 따라 분류성능이 향상됨을 될 수 있다. 특히, 참인 성상도가 4진 직교진폭변조인 경우 추가적인 정보나 관측표본 없이도 송신기에서 사용된 변조방식을 거의 완벽하게 분류함을 확인할 수 있다. 또한 16진과 64진 신호의 경우 그 부분집합이 되는 성상도에 의하여 공유된 심볼들의 영향으로 오분류 가능성이 상존하지만 단일표본관측만으로도 약 $80{\%}$의 정분류 성능을 얻을 수 있다.

Keywords

References

  1. A. Swami and B. M. Sadler, 'Issues in military communications,' IEEE Signal Process. Mag., Vol.16, No.2, pp.31-33, Mar, 1999
  2. K. Assaleh, K. Farrell, and R. J. Mammone, 'A new method of modulation classification for digitally modulated signals,' Proc. IEEE MILCOM'92, San Diego, CA, Vol.2, pp.712-716, Dec., 1992 https://doi.org/10.1109/MILCOM.1992.244137
  3. S.-Z. Hsue and S. S. Soliman, 'Automatic modulation classification using zero crossing,' Proc. Inst. Elect. Eng., Vol.137, No.6, pp.459-464, Dec., 1990
  4. P. Marchand, C. Le Martret, and J.-L. Lacoume, 'Classification of linear modulations by a combination of different orders cyclic cumulants,' Proc. IEEE Signal Process. Workshop Higher-Order Statistics, Banff, Alberta, Canada, Vol.1, pp.47-51, July, 1997 https://doi.org/10.1109/HOST.1997.613485
  5. A. Swami and B. M. Sadler, 'Hierarchical digital modulation classification using cumulants,' IEEE Trans. Commun., Vol.COM-48, No.3, pp.416-429, Mar, 2000 https://doi.org/10.1109/26.837045
  6. B. F. Beidas and C. L. Weber, 'Higher-order-correlation-based approach to modulation classification of digitally frequency-modulated signals,' IEEE J. Select. Areas Commun., Vol.SAC-13, No.1, pp.89-101, Jan., 1995 https://doi.org/10.1109/49.363142
  7. B. F. Beidas and C. L. Weber, 'Modulation classification of MFSK signals using the higher-order correlation domain,' Proc. IEEE MILCOM'95, San Diego, CA, Vol.1, pp.186-191, Nov., 1995 https://doi.org/10.1109/MILCOM.1995.483296
  8. S. S. Soliman and S.-Z. Hsue, 'Signal classification using statistical moments,' IEEE Trans. Commun., Vol.40, No.5, pp.908-916, May, 1992 https://doi.org/10.1109/26.141456
  9. K. M. Chugg, C.-S. Long, and A. Polydoros, 'Combined likelihood power estimation and multiple hypothesis modulation classification,' Proc. IEEE ASILOMAR'95, Pacific Grove, CA, Vol.2, pp.1137-1141, Nov., 1995 https://doi.org/10.1109/ACSSC.1995.540877
  10. A. O. Hero and H. Hadinejad-Mahram, 'Digital modulation classification using power moment matrices,' Proc. IEEE ICASSP'98, Seattle, WA, Vol.6, pp.3285-3288, May, 1998 https://doi.org/10.1109/ICASSP.1998.679566
  11. K. C. Ho, W. Prokopiw, and Y. T. Chan, 'Modulation identification by the wavelet transform,' Proc. IEEE MILCOM'95, San Diego, CA, Vol.2, pp.886-890, Nov., 1995 https://doi.org/10.1109/MILCOM.1995.483654
  12. C. S. Long, K. M. Chugg, and A. Polydoros, 'Further results in likelihood classification of QAM signals,' Proc. IEEE MILCOM'95, Fort Monmouth, NJ, Vol.1, pp.57-61, Dec., 1994 https://doi.org/10.1109/MILCOM.1994.473837
  13. C.-Y. Huang and A. Polydoros, 'Likelihood methods for MPSK modulation classification,' IEEE Trans. Commun., Vol.43, No.2/3/4, pp.1493-1504, Feb./Mar./Apr., 1995 https://doi.org/10.1109/26.380199
  14. J. A. Sills, 'Maximum-likelihood modulation classification for PSK/QAM,' Proc. IEEE MILCOM'99, Atlantic City, NJ, Vol.1, pp.217-220, Oct., 1999 https://doi.org/10.1109/MILCOM.1999.822675
  15. W. Wei and J. M. Mendel, 'Maximum-likelihood classification for digital amplitude-phase modulations,' IEEE Trans. Commun., Vol.COM-48, No.2, pp.189-193, Feb., 2000 https://doi.org/10.1109/26.823550
  16. H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part I, Wiley, New York, NY, 1967
  17. T. Keller and L. Hanzo, 'Adaptive multicarrier modulation : A convenient framework for time-frequency processing in wireless communications,' IEEE Proc., Vol.88, No.5, pp.611-640, May, 2000 https://doi.org/10.1109/5.849157
  18. P. S. Chow, J. M. Cioffi, and J. A. C. Bingham, 'A practical discrete multitone transceiver loading algorithm for data transmission over spectrally shaped channels,' IEEE Trans. Commun., Vol.43, No.2/3/4, pp.773-775, Feb./Mar./Apr., 1995 https://doi.org/10.1109/26.380108