Abstract
In this paper, we propose a modulation classification algorithm for M-ary QAM signals in Rician and Rayleigh fading channels. The developed algorithms are based on the maximum log-likelihood functions, which are derived from received signals. First of all, we derived the amplitude PDF of M-ary QAM signal over flat and slowly Rayleigh and Rician fading channel, then we developed the log-likelihood functions and then the decision functions for classification. To demonstrate the performance of the proposed classifier, we give an example to classify the 16/32 QAM signals. Results indicate that the performance of classifier is heavily dependent on the severity of channel fading. When channel is AWGN, which means that there exists only one path (may be specular path) between transmitter and receiver, and the Rician factor k, approaches infinity in this case, henceforth, the performance is the best. The performance, however, is degraded with the decrease of k, and finally the classifier performs worst when channel becomes Rayleigh. Further performance improvement can be achieved by increasing the length of record.
Similar content being viewed by others
References
Y. Yang, C.H. Liu and T.W. Soong, “A Log-Likelihood Function-Based Algorithm for QAM Signal Classification”, Signal Processing, Vol. 70, No. 1, pp. 61–71, 1998.
Y. Yang and S.S. Soliman, “A Suboptimal Algorithm for Modulation Classification”, IEEE Trans. Aerosp. Electron. Syst., Vol. 33, pp. 38-45, 1997.
C. Schreyogg and J. Reichert, “Modulation Classification of QAM Schemes Using the DFT of Phase Histogram Combined With Modulus Information”, in Proc. IEEE MILCOM 1997, Monterey, California, Nov. 1997, pp. 474–477.
Wen Wei and J.M. Mendel, “Maximum-Likelihood Classification for Digital Amplitude-Phase Modulations”, IEEE Trans. on Commun., pp. 189–193, 2000.
F.E. Liedtke, “Computer Simulation of an Automatic Classification Procedure for Digitally Modulated Communication Signals with Unknown Parameters”, Signal Processing, Vol. 6, No. 4, pp. 311–323, 1984.
S. Taira, “Automatic Classification of QAM Signals in Fading Channel”, in Proc. IEEE VTC 2000, Tokyo, Vol. 3, May 2000, pp. 1717–1721.
A.E. El-Mahdy and N.M. Namazi, “Classification of Multiple M-ary Frequency-Shift Keying Signals over a Rayleigh Fading Channel”, IEEE Trans. on Commun., Vol. 50, No. 6, pp. 967–974, 2002.
A. Swami, S. Barbarossa and B.M. Sadler, “Blind Source Separation and Signal Classification”, in Proc. IEEE 34th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA. Vol. 2, Oct. 2000, pp. 1187–1191.
G. Hatzichristos and M.P. Fargues, “A Hierarchical Approach to the Classification of Digital Modulation Types inMultipath Environments”, in Proc. IEEE 35th Asilomar Conf. on Signals, Systems, and Computers, Vol. 2, Nov. 2001, pp. 1494–1498.
K.E. Nolan, L. Doyle, D. O'Mahony and P. Mackenzie, “Signal Space Based Adaptive Modulation for Software Radio”, in Proc. IEEE WCNC 2002, Orlando, Florida, Vol. 1, Mar. 2002, pp. 51–515.
J G. Proakis, Digital Communications, 4th edn, McGraw-Hill: New York, 2001.
A.D. Whalen, Detection of Signals in Noise, Academic Press: New York, 1971.
G.L. Stuber, Principles of Mobile Communication, Kluwer Academic Publishers: Norwell, MA, 1996.
I.S. Grandshteyn and I.M. Ryzbik, Table of Integrals, Series, and Products, 6th edn, Academic Press: New York, 2000.
A. Papoulis, Probability, Random Variables, and Stochastic Processes, 3rd edn, McGraw-Hill: New York, 1991.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Yang, Y., Chang, JN., Liu, JC. et al. Maximum Log-Likelihood Function-Based QAM Signal Classification over Fading Channels. Wireless Personal Communications 28, 77–94 (2004). https://doi.org/10.1023/B:WIRE.0000015422.23663.98
Issue Date:
DOI: https://doi.org/10.1023/B:WIRE.0000015422.23663.98