Blind Modulation Classification over Fading Channels Using Expectation-Maximization

We propose a blind modulation classification algorithm when the channel coefficient, the noise power and the energy of the transmitted signal are unknown at the receiver. First, under each candidate modulation scheme, we evaluate the unknown parameters using the iterative expectation maximization algorithm. Modulation classification is then accomplished by minimizing the distance between the log-likelihood of the received data and the expected log-likelihood under each candidate modulation scheme. Results are presented from simulations in terms of detection probability vs. SNR for the class of BPSK, QPSK, 16QAM and 64QAM modulation schemes. The results show a significant improvement over QHLRT and are very close to the upper bound ALRT-UB [1].

[1]  Jeffrey H. Reed,et al.  Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[2]  C. Spooner.  On the utility of sixth-order cyclic cumulants for RF signal classification , 2001, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

[3]  Y. Bar-Ness,et al.  Blind modulation classification: a concept whose time has come , 2005, IEEE/Sarnoff Symposium on Advances in Wired and Wireless Communication, 2005..

[4]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[5]  Y. Bar-Ness,et al.  Modulation classification in fading channels using antenna arrays , 2004, IEEE MILCOM 2004. Military Communications Conference, 2004..

[6]  William C. Headley,et al.  Asynchronous Classification of Digital Amplitude-Phase Modulated Signals in Flat-Fading Channels , 2011, IEEE Transactions on Communications.

[7]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[8]  Octavia A. Dobre,et al.  Likelihood-Based Algorithms for Linear Digital Modulation Classification in Fading Channels , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[9]  J. E. Glynn,et al.  Numerical Recipes: The Art of Scientific Computing , 1989 .

[10]  Claudio R. C. M. da Silva,et al.  Maximum-Likelihood Classification of Digital Amplitude-Phase Modulated Signals in Flat Fading Non-Gaussian Channels , 2011, IEEE Transactions on Communications.

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

[12]  C.M. Spooner,et al.  Classification of co-channel communication signals using cyclic cumulants , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[13]  Octavia A. Dobre,et al.  On the likelihood-based approach to modulation classification , 2009, IEEE Transactions on Wireless Communications.

[14]  Ali Abdi,et al.  Survey of automatic modulation classification techniques: classical approaches and new trends , 2007, IET Commun..