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A Modulation Recognition Method Based on Carrier Frequency Estimation and Decision Theory

A Modulation Recognition Method Based on Carrier Frequency Estimation and Decision Theory. APCC 2010 – The 16 th Asia-Pacific Conference on Communications. Author: Xudong Liu Jinzhao Su Wei Wu. State Key Laboratory of Virtual Reality and Systems. Overview.

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A Modulation Recognition Method Based on Carrier Frequency Estimation and Decision Theory

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  1. A Modulation Recognition Method Based on Carrier Frequency Estimation and Decision Theory APCC 2010 – The 16th Asia-Pacific Conference on Communications Author: Xudong LiuJinzhao Su Wei Wu State Key Laboratory of Virtual Reality and Systems

  2. Overview • Background • Basic Idea and Design • Simulation and Results • Conclusions - 2 -

  3. Background • Wireless communication gets rapid development • Military applications • Electronic information warfare • Interference identification and monitoring • Civilian applications • Spectrum management • Signal confirmation • Interference identification - 3 -

  4. Background - 4 -

  5. Overview • Background • Basic Idea and Design • Simulation and Results • Conclusions - 5 -

  6. Basic Idea and Design • Two main categories • The statistical pattern recognition approach • Easy to accomplish • Work well at high Signal-to-Noise Ratio(SNR) • The decision theoretic approach • High accuracy in low SNR • The decision theoretic method • A feature extraction subsystem • A modulation recognition subsystem - 6 -

  7. Basic Idea and Design - 7 -

  8. Carrier frequency Unknown for blind signals Important for calculating key parameters Key parameters Unknown for blind signals Used for modulation recognition Basic Idea and Design - 8 -

  9. Basic Idea and Design • The effect of center frequency on success rate. - 9 -

  10. Three Carrier frequency estimation methods: The Center Frequency method The Zero-Crossing method The Spectrum of Short Frame method Basic Idea and Design - 10 -

  11. Basic Idea and Design - 11 -

  12. Basic Idea and Design • Key parameters • The maximum value of the power spectral density of the normalized-centered instantaneous amplitude of the intercepted signal segment • The standard deviation of the direct value of thecentered, non-linear component of the instantaneous phase,evaluated over the non-weak intervals of a signal segment • The kurtosis of the normalized instantaneousamplitude - 12 -

  13. Basic Idea and Design • The standard deviation of the absolute value of thecentered, non-linear component of the instantaneous phase,evaluated over the non-weak intervals of a signal segment - 13 -

  14. Overview • Background • Basic Idea and Design • Simulation and Results • Conclusion - 14 -

  15. Simulation and Results • Conditions • Simulation environment: MATLAB • The carrier frequency : 100kHz • The sampling rate : 1MHz • 11 analog or digital modulation signals • Carried out 100 times • SNR varies from -5dB to 25dB • Signals corrupted by band-limited Gaussian noise - 15 -

  16. Simulation and Results • is used for discriminating 4PSK or MFSK, FM. - 16 -

  17. Simulation and Results • is used for discriminating DSB, 2PSK or FM, MFSK. - 17 -

  18. Simulation and Results • is used for discriminating AM, MASK or DSB, USB,LSB, FM, MPSK, MFSK. - 18 -

  19. Simulation and Results • is used for discriminating AM or MASK. - 19 -

  20. Simulation and Results • With 9 key parameters, identify 11 kinds of modulatedsignals - 20 -

  21. Simulation and Results - 21 -

  22. Overview • Background • Basic Idea and Design • Simulation and Results • Conclusions - 22 -

  23. Conclusions • The spectrum of short frame method can be used for estimating carrier frequency even in low SNR condition. • Center frequency is very important and can be used for calculating other parameters. • With 9 key parameters and decision theoretical algorithms, we can discriminate 11 blind signals. • Success rate is greater than 95% when the SNR is higher than 10dB. - 23 -

  24. References [1] H. Guldemir. A. Sengur, “Online modulation recognition of analog communication signals using neural network,” Expert Systems with Application,vol. 33, pp. 206–214, 2006; [2] S. Gulati. R.Bhattacharjee, “Automatic Blind Recognition of Noisy and Faded Digitally Modulated MQAM Signals,” Digital Object Identifier, pp. 1–6, 2006; [3] A. Ebrahimzadeh, “Automatic modulation recognition using RBFNN and efficient features in fading channels,” Networked Digital Technologies, pp. 485–488, 2009; [4] A. Sengur, “Multicalss least-squares support vector machines for analog modulation classification,” Expert Systems with Applications, vol. 36,pt. II, pp. 6681–6685, 2009; [5] D. A. Visan. M. Jurian. I. B. Cioc, “Modeling and simulation of an recognition system for digital modulated signals,” ISSE 2009˙International Spring Seminar, pp. 1–5, 2009; [6] W. J. Ping. H. Y. Zheng. Z. J. Mei. W. H. Kui. R. S. Ping, “Automatic Modulation Recognition of Digital Communication Signals Using Statistical Parameters Methods,” ICCCAS 2007.International Conference, pp. 697–700, 2007; [7] A. K. Nandi. E. E. Azzouz, “Algorithms for automatic modulation recognition of communication signals,” IEEE Transactions on Communications, vol. 46, pp. 431–436, 1998; [8] A. K. Nandi. E. E. Azzouz, “Modulation recognition using artificial neural networks ,” Signal Processing, vol. 56, pp. 165–175, 1997; - 24 -

  25. Thank You! - 25 -

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