Modulation Identification Algorithm for Adaptive Demodulator in Software Defined Radios Using Wavelet Transform
A generalized Digital Modulation Identification algorithm for adaptive demodulator has been developed and presented in this paper. The algorithm developed is verified using wavelet Transform and histogram computation to identify QPSK and QAM with GMSK and M–ary FSK modulations. It has been found that the histogram peaks simplifies the procedure for identification. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB and 12 dB for GMSK and QPSK respectively. When SNR is above 5 dB the throughput of the proposed algorithm is more than 97.8%. The receiver operating characteristics (ROC) has been computed to measure the performance of the proposed algorithm and the analysis shows that the probability of detection (Pd) drops rapidly when SNR is 5 dB and probability of false alarm (Pf) is smaller than 0.3. The performance of the proposed algorithm has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.
[1] E. E. Azzouz and A. K. Nandi, "Automatic Modulation Recognition of
Communication Signals," Kluwer Academic Publishers, 1996.
[2] Enrico Buracchini, CSELT, "The Software Radio Concept," IEEE
Communication Magazine, vol 38 no 9, pp 138-143, 2000.
[3] Druckmann. I, Plotkim. E.I, Swamy M.N.S., "Automatic Modulation
Type Classification", Electrical and Computer Engineering, 1998. IEEE
Canadian Conference on, vol. 1, pp. 65-68, 1998.
[4] Lopatka.J, Pedzisa.M., "Automatic Modulation Classification Using
Statistical Moments and a Fuzzy Classifier", Signal Processing
Procedings, WCCC-ICSP 2000. 5th International Conference on, vol. 3,
pp 1500-1506, 2000.
[5] Callaghan. T.G, Pery. J.L, Tjho. J.K., "Sampling and algorithms aid
modulation recoginition", Microwave & RF, 24, (9), pp.117-179, 1995.
[6] Jondral. F., "Foundations of Automatic Modulation Classification", ITG
- Fachbericht, 107, pp.201-206, 1989.
[7] Beran.R., "Minimum Hellinger distance Estimates for Parametric
Models", Annals of Statistics, vol. 5, pp.445-463, 1977.
[8] Hero. A.O, III, Hadinejad-Mahram.H., "Digital modulation
classification using power moment matrices," Acoustics, Speech and
Signal Processing, 1998. Proceedings of the 1998 IEEE International
Conference on, vol.6, pp.3285-3288, 1998.
[9] Aiello. A, Grimaldi.D, rapuno.s, "GMSK Neural Network based
Demodulator", Intelligent Data Acquisition and Advanced Computing
Systems: Technology and Applications, International Workshop on,
pp.2-5, 2001.
[10] Keith E.Nolan, Lida Doyle, Philip Mackenzia, Donald o-Mahory,
"Modulation scheme recognition for 4G software Radio Wireless,"
Networks Proceedings of the IASTED International Conference on
Signal Processing, Pattern Recognition, and Application,, pp 25-31,
2002.
[11] Ketterer, H., Jondral, F., Costa, A. H., "Classification of Modulation
Modes Using Time-Frequency Methods," IEEE International
Conference on Acoustics, Speech, and Signal Processing, vol. 5, 1999.
[12] Y.C. Lin, C.-C. Jay Kuo, "Modulation classification using wavelet
transform," in Proceedings SPIE, vol. 2303, pp. 260-271.
[13] Liang Hong K.C. Ho, "Identification of Digital Modulation types using
the Wavelet Transform," IEEE Conference Proc. MILCOM, pp. 427-
431, 1999.
[14] Radomir Pavlik, "Binary PSK/CPFSK and MSK Bandpass Modulation
Identifier Based On The Complex Shannon Wavelet Transform",
Journal of Electrical Engineering, vol. 56, no. 3-4, pp. 71-77, 2005.
[15] P.Prakasam and M.Madheswaran, "Automatic Modulation Identification
of QPSK and GMSK using Wavelet Transform for Adaptive
Demodulator in SDR", IEEE Conference Proc. International
Conference on Signal Processing, Communications and Networking
(IEEE-ICSCN 2007), MIT, Anna University, Chennai, pp 507-511,
February 2007.
[16] Simon Haykin, "Communication Systems," Wiley Eastern Limited,
2005.
[17] Y. T. Chan, "Wavelet Basics," Kluwer Academic Publishers, 1995.
[18] D. Le Guen, A. Mansour, "Automatic Recognition Algorithm for
Digitally Modulated Signals," Procedings of the IASTED International
Conference on Signal Processing, Pattern Recognition & Applications,
pp 32-37, June, 2002.
[19] P. C. Sapiano, J. Martin, and R. Holbeche, "Classification of PSK
signals using the DFT of phase histogram," in Conference Proc.
ICASSP, pp. 1868-1871, 1995.
[20] K. C. Ho, W. Prokopiw, and Y. T. Chan, "Modulation identification by
the wavelet transform," in Conference Proc. IEEE MILCOM, pp. 886-
890, 1995.
[21] C. Martret and D. M. Boiteau, "Modulation classification by means of
different order statistical moments," in Conference Proc. IEEE
MILCOM, pp. 1387-1391, 1997.
[22] O. A. Dobre, Y. Bar-Ness, and W. Su, "Higher-order cyclic cumulants
for high order modulation classification," in Conference Proc. IEEE
MILCOM, pp. 112-117, 2003.
[23] Z. Yu, Y. Q. Shi, and W. Su, "M-ary frequency shift keying signal
classification based on discrete Fourier transform," in Conference Proc.
IEEE MILCOM, pp. 1167-1172, 2003.
[24] J.A. Sills, "Maximum-Likelihood Modulation Classification for
PSK/QAM", in Conference Proc. IEEE MILCOM, 1999.
[1] E. E. Azzouz and A. K. Nandi, "Automatic Modulation Recognition of
Communication Signals," Kluwer Academic Publishers, 1996.
[2] Enrico Buracchini, CSELT, "The Software Radio Concept," IEEE
Communication Magazine, vol 38 no 9, pp 138-143, 2000.
[3] Druckmann. I, Plotkim. E.I, Swamy M.N.S., "Automatic Modulation
Type Classification", Electrical and Computer Engineering, 1998. IEEE
Canadian Conference on, vol. 1, pp. 65-68, 1998.
[4] Lopatka.J, Pedzisa.M., "Automatic Modulation Classification Using
Statistical Moments and a Fuzzy Classifier", Signal Processing
Procedings, WCCC-ICSP 2000. 5th International Conference on, vol. 3,
pp 1500-1506, 2000.
[5] Callaghan. T.G, Pery. J.L, Tjho. J.K., "Sampling and algorithms aid
modulation recoginition", Microwave & RF, 24, (9), pp.117-179, 1995.
[6] Jondral. F., "Foundations of Automatic Modulation Classification", ITG
- Fachbericht, 107, pp.201-206, 1989.
[7] Beran.R., "Minimum Hellinger distance Estimates for Parametric
Models", Annals of Statistics, vol. 5, pp.445-463, 1977.
[8] Hero. A.O, III, Hadinejad-Mahram.H., "Digital modulation
classification using power moment matrices," Acoustics, Speech and
Signal Processing, 1998. Proceedings of the 1998 IEEE International
Conference on, vol.6, pp.3285-3288, 1998.
[9] Aiello. A, Grimaldi.D, rapuno.s, "GMSK Neural Network based
Demodulator", Intelligent Data Acquisition and Advanced Computing
Systems: Technology and Applications, International Workshop on,
pp.2-5, 2001.
[10] Keith E.Nolan, Lida Doyle, Philip Mackenzia, Donald o-Mahory,
"Modulation scheme recognition for 4G software Radio Wireless,"
Networks Proceedings of the IASTED International Conference on
Signal Processing, Pattern Recognition, and Application,, pp 25-31,
2002.
[11] Ketterer, H., Jondral, F., Costa, A. H., "Classification of Modulation
Modes Using Time-Frequency Methods," IEEE International
Conference on Acoustics, Speech, and Signal Processing, vol. 5, 1999.
[12] Y.C. Lin, C.-C. Jay Kuo, "Modulation classification using wavelet
transform," in Proceedings SPIE, vol. 2303, pp. 260-271.
[13] Liang Hong K.C. Ho, "Identification of Digital Modulation types using
the Wavelet Transform," IEEE Conference Proc. MILCOM, pp. 427-
431, 1999.
[14] Radomir Pavlik, "Binary PSK/CPFSK and MSK Bandpass Modulation
Identifier Based On The Complex Shannon Wavelet Transform",
Journal of Electrical Engineering, vol. 56, no. 3-4, pp. 71-77, 2005.
[15] P.Prakasam and M.Madheswaran, "Automatic Modulation Identification
of QPSK and GMSK using Wavelet Transform for Adaptive
Demodulator in SDR", IEEE Conference Proc. International
Conference on Signal Processing, Communications and Networking
(IEEE-ICSCN 2007), MIT, Anna University, Chennai, pp 507-511,
February 2007.
[16] Simon Haykin, "Communication Systems," Wiley Eastern Limited,
2005.
[17] Y. T. Chan, "Wavelet Basics," Kluwer Academic Publishers, 1995.
[18] D. Le Guen, A. Mansour, "Automatic Recognition Algorithm for
Digitally Modulated Signals," Procedings of the IASTED International
Conference on Signal Processing, Pattern Recognition & Applications,
pp 32-37, June, 2002.
[19] P. C. Sapiano, J. Martin, and R. Holbeche, "Classification of PSK
signals using the DFT of phase histogram," in Conference Proc.
ICASSP, pp. 1868-1871, 1995.
[20] K. C. Ho, W. Prokopiw, and Y. T. Chan, "Modulation identification by
the wavelet transform," in Conference Proc. IEEE MILCOM, pp. 886-
890, 1995.
[21] C. Martret and D. M. Boiteau, "Modulation classification by means of
different order statistical moments," in Conference Proc. IEEE
MILCOM, pp. 1387-1391, 1997.
[22] O. A. Dobre, Y. Bar-Ness, and W. Su, "Higher-order cyclic cumulants
for high order modulation classification," in Conference Proc. IEEE
MILCOM, pp. 112-117, 2003.
[23] Z. Yu, Y. Q. Shi, and W. Su, "M-ary frequency shift keying signal
classification based on discrete Fourier transform," in Conference Proc.
IEEE MILCOM, pp. 1167-1172, 2003.
[24] J.A. Sills, "Maximum-Likelihood Modulation Classification for
PSK/QAM", in Conference Proc. IEEE MILCOM, 1999.
@article{"International Journal of Electrical, Electronic and Communication Sciences:50738", author = "P. Prakasam and M. Madheswaran", title = "Modulation Identification Algorithm for Adaptive Demodulator in Software Defined Radios Using Wavelet Transform", abstract = "A generalized Digital Modulation Identification algorithm for adaptive demodulator has been developed and presented in this paper. The algorithm developed is verified using wavelet Transform and histogram computation to identify QPSK and QAM with GMSK and M–ary FSK modulations. It has been found that the histogram peaks simplifies the procedure for identification. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB and 12 dB for GMSK and QPSK respectively. When SNR is above 5 dB the throughput of the proposed algorithm is more than 97.8%. The receiver operating characteristics (ROC) has been computed to measure the performance of the proposed algorithm and the analysis shows that the probability of detection (Pd) drops rapidly when SNR is 5 dB and probability of false alarm (Pf) is smaller than 0.3. The performance of the proposed algorithm has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.
", keywords = "Bit Error rate, Receiver Operating Characteristics,Software Defined Radio, Wavelet Transform.", volume = "3", number = "11", pages = "1927-8", }
