The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지

Fast Modulation Classifier for Software Radio

소프트웨어 라디오를 위한 고속 변조 인식기

  • 박철순 (국방과학연구소 기술연구본부) ;
  • 장원 (국방과학연구소 기술연구본부) ;
  • 김대영 (충남대학교 정보통신공학과)
  • Published : 2007.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we deals with automatic modulation classification capable of classifying incident signals without a priori information. The 7 key features which have good properties of sensitive with modulation types and insensitive with SNR variation are selected. The numerical simulations for classifying 9 modulation types using the these features are performed. The numerical simulations of the 4 types of modulation classifiers are performed the investigation of classification accuracy and execution time to implement the fast modulation classifier in software radio. The simulation result indicated that the execution time of DTC was best and SVC and MDC showed good classification performance. The prototype was implemented with DTC type. With the result of field trials, we confirmed the performance in the prototype was agreed with the numerical simulation result of DTC.

본 논문에서는 사전정보 없이 입사하는 신호의 변조 형태를 자동 식별하기 위해 변조타입에 대한 민감도가 우수하고, SNR에 대한 변화가 적은 속성을 가진 7개의 특징(key features)들을 선정하였다. 또한 선정된 특징들을 이용하여 총 9종의 변조 신호(아날로그와 디지털 신호 포함)를 분류하기 위한 시뮬레이션을 수행하였다. 소프트웨어 라디오의 고속 변조 인식기 탑재를 고려하여, 4 타입의 변조인식기에 대한 인식 정확도 및 수행시간을 검토하였다. 시뮬레이션 결과 인식시간은 DTC(Decision Tree Classifier)가 가장 빠르게 수행되었고, 인식정확도는 SVC(Support Vector Machine Classifier)과 MDC(Minimum Distance Classifier)가 우수하게 제시되었다. 변조 인식기의 프로토타입은 처리 속도가 가장 우수한 DTC로 구현되었다. 필드 실험 결과, 인식 성능은 DTC 시뮬레이션 결과와 일치하는 것을 확인하였다.

Keywords

References

  1. M. Vastram Naik et al., 'Blind adaptive recognition of different QPSK modulated signals for software defined radio applica tion,' '06 COMSWARE, pp. 1-6, Jan. 2006
  2. Octavia A. Dobre et al., 'A survey of automatic modulation classification Techniques: classi cal approaches and new trends,' IEE Proc. Communication, to be published in 2006
  3. Bin Le et al, 'Modulation identification using neural network for cognitive radios,' SDR forum technical conference, 2005
  4. E.E. Azzouz, and A.K.Nandi, 'Procedure for automatic recog nition of analogue and digital modulations,' IEE proceedings Communications, vol. 143, no.5, pp. 259-266, oct. 1996 https://doi.org/10.1049/ip-com:19960752
  5. A.K. Nandi and E.E. Azzouz, 'Algorithm for automatic modul ation recognition of communica tion signals,' IEEE Trans. Communications, vol. 46, no. 4, pp.431-436, April 1998 https://doi.org/10.1109/26.664294
  6. Ilan Druckmann et al., 'Automatic modulation type recog nition,' IEEE Canadian Conf. on Electrical and Computer Engineering, pp.65-68, May 1998
  7. Octavia A. Dobre et al., 'The classification of joint analog and digital modulations,' IEEE MILCOM '05, pp. 3010-3015, Oct. 2005
  8. 서승한, 윤여종, 진영환, 서영주, 임선민, 안재민, 은창수, 장원, 나선필, '아날로그 및 디지털 변조 신호의 자동인식,' 한국통신학회논문지, Vol.30, No.1C, pp.73-81, 2005
  9. Roongroj Nopsuwanchai, and Al ain Biem, 'Prototype-based minimum error classifier for handwritten digits recognition,' IEEE ICASSP'04, pp.845-848, May 2004
  10. L. Zhang, N. Fujiwara, K. Muramatsu, M. Daigo and S. Furumi, 'Land cover classification based on the universal pattern decomposition method,' Int'l Symp. on Remote Sensing of Environment, 2005
  11. Namjin Kim, Nasser Kehta rnavaz, Mark B. Yeary and Steve Thornton, 'DSP-based hirerarchical neural network modulation signal classification,' IEEE Trans. Neural Networks, vol.14, no.5, pp. 1065-1071, Sept. 2003 https://doi.org/10.1109/TNN.2003.816037
  12. Stefan C. Kremer and Joanne Sheils, 'A testbed for automatic modulation recognition using artificial neural networks,' IEEE Canadian Conf. on Electrical and Computer Engi neering, pp.67-70, May 199
  13. 신용조, 진용옥, '신경망을 이용한 디지털 변조 방식의 자동식별,' 한국통신학회논문지, Vol.25, No.10B, pp1769-1776, 2000
  14. Steve R. Gunn, Support Vector Machine for Classification and Regression, Technical Report, Univ. of Southampton, May 1998
  15. Hussam Mustafa and Milos Doroslovacki, 'Digital modula tion recognition using support vector machine classifier,' IEEE Conf. on Signals, Systems and Computers, pp.2238-2242, Nov. 2004
  16. Zhilu Wu et al., 'Automatic digital modulation recognition based on supp ort vector machine,' IEEE Conf. on Neural Networks and Brain, pp. 1025-1028, Oct. 2005
  17. Wu Dan, Gu Xuemai, and Guo Qing, 'A new scheme of automatic modulation classifica tion using wavelet and WSVM,' 2nd Int'l Conf. on Mobile Technology, Applications and Systems, Nov. 2004
  18. B.Q.Hu, J. Yang, and J.L.He, 'A multiclassification model based on FSVMs,' NAFIPS '05, pp. 205-209, June 2005
  19. Anil K. Jain et al., 'Statistical pattern recognition: a review,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4-37, Jan. 2000 https://doi.org/10.1109/34.824819
  20. Robert W. Jones, Spectrum Monitoring Handbook, ITU, 3rd ed, 1995, pp.222-237
  21. Andrew Webb, Statistical Pattern Recognition, 2nd ed., 2002, John Wiley & Sons, Ltd., pp.144-168