An Automatic Digital Modulation Classifier for Measurement on Telecommunication Networks

This paper presents a method for the automatic classification of digital modulations without a priori knowledge of the signal parameters. This method can recognize classical single- carrier modulations such as M-ary phase-shift keying, M-ary frequency-shift keying, M-ary amplitude-shift keying, and M-ary quadrature amplitude modulation, as well as orthogonal frequency-division multiplexing modulations such as discrete mul- titone that is used for asymmetric digital subscriber line and very high speed digital subscriber line standards and for power-line carrier transmissions. After identification of the modulation type, the method automatically estimates some parameters characterizing the modulation. To evaluate the method performance, several simulations have been carried out in different operating conditions that should be particularly critical by varying the values of signal- to-noise ratio and the main parameters of each identifiable modulation. To assess the advantages, comparison with other classification methods has been given. To validate the assumption that is made, experimental tests have been performed.

[1]  Georgios B. Giannakis,et al.  Time-domain tests for Gaussianity and time-reversibility , 1994, IEEE Trans. Signal Process..

[2]  Brian M. Sadler,et al.  Frequency estimation via sparse zero crossings , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[3]  J. Mendel,et al.  A new maximum-likelihood method for modulation classification , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[4]  Pasquale Daponte,et al.  A non-intrusive estimation of the carrier frequency in GMSK signals , 2000, 2000 IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. Future Sustainment for Military Aerospace (Cat. No.00CH37057).

[5]  Per Ola Börjesson,et al.  ML estimation of time and frequency offset in OFDM systems , 1997, IEEE Trans. Signal Process..

[6]  Elsayed Elsayed Azzouz,et al.  Algorithms for automatic modulation recognition of communication signals , 1998, IEEE Trans. Commun..

[7]  Jean-Louis Lacoume,et al.  Multiple hypothesis modulation classification based on cyclic cumulants of different orders , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[8]  K. C. Ho,et al.  Modulation identification of digital signals by the wavelet transform , 2000 .

[9]  Domenico Grimaldi,et al.  AUTOMATIC MODULATION CLASSIFICATION AND MEASUREMENT OF DIGITALLY MODULATED SIGNALS , 2001 .

[10]  J. Sills Maximum-likelihood modulation classification for PSK/QAM , 1999, MILCOM 1999. IEEE Military Communications. Conference Proceedings (Cat. No.99CH36341).

[11]  C. Schreyogg,et al.  Modulation classification of QAM schemes using the DFT of phase histogram combined with modulus information , 1997, MILCOM 97 MILCOM 97 Proceedings.

[12]  R. Mammone,et al.  Modulation classification using a neutral tree network , 1993, Proceedings of MILCOM '93 - IEEE Military Communications Conference.

[13]  L. Angrisani,et al.  A measurement method based on time-frequency representations for the qualification of GSM equipment , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[14]  W. Akmouche,et al.  Detection of multicarrier modulations using 4th-order cumulants , 1999, MILCOM 1999. IEEE Military Communications. Conference Proceedings (Cat. No.99CH36341).

[15]  R. Mammone,et al.  A new method of modulation classification for digitally modulated signals , 1992, MILCOM 92 Conference Record.

[16]  Pasquale Daponte,et al.  A measurement method based on time-frequency representations for testing GSM equipment , 2000, IEEE Trans. Instrum. Meas..

[17]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[18]  Samir S. Soliman,et al.  Signal classification using statistical moments , 1992, IEEE Trans. Commun..

[19]  Yawpo Yang,et al.  An asymptotic optimal algorithm for modulation classification , 1998, IEEE Communications Letters.

[20]  Pasquale Daponte,et al.  Neural network and DSP based decoder for DTMF signals , 2000 .

[21]  Brian M. Sadler,et al.  Hierarchical digital modulation classification using cumulants , 2000, IEEE Trans. Commun..

[22]  A. K. Nandi,et al.  Procedure for automatic recognition of analogue and digital modulations , 1996 .

[23]  C. J. Le Martret,et al.  A general maximum likelihood framework for modulation classification , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[24]  Pasquale Daponte,et al.  An algorithm for finding the zero crossing of time signals with Lipschitzean derivatives , 1995 .

[25]  Pasquale Daponte,et al.  Fast detection of the first zero-crossing in a measurement signal set , 1996 .

[26]  Friedrich Jondral,et al.  Classification of modulation modes using time-frequency methods , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[27]  Kiseon Kim,et al.  On the detection and classification of quadrature digital modulations in broad-band noise , 1990, IEEE Trans. Commun..