Automatic Modulation Classification for Low SNR Digital Signal in Frequency-Selective Fading Environments

Abstract In this research, a classifier is proposed for automatic modulation classification of some common modulation schemes, i.e., BPSK, QPSK, 8-PSK and 16-QAM. Our proposed classifier considers multipath fading effects on the received signal in a non-Gaussian noise environment. Automatic modulation classification is very challenging in real-world scenarios due to fading effects and additive Gaussian mixture noise on modulation schemes. Most of the available modulation classifiers do not consider the fading effects which results in degradation of classification in a blind channel environment. In our work, the channel is supposed to be suffering from excessive additive Gaussian mixture noise and frequency selective fading resulting in low signal SNR. The estimation of the unknown channel along with noise parameters is performed using ECM algorithm and then used in maximum-likelihood classifier for the classification of modulation schemes. Simulation results are presented that show 2 dB improvement in performance than classifier which considers only Gaussian noise in the received signal.

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

[2]  Venkata Gautham Chavali,et al.  Signal Detection and Modulation Classification in Non-Gaussian Noise Environments , 2012 .

[3]  Xiao-Li Meng,et al.  Maximum likelihood estimation via the ECM algorithm: A general framework , 1993 .

[4]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[5]  Liang Hong,et al.  Maximum likelihood BPSK and QPSK classifier in fading environment using the EM algorithm , 2006, 2006 Proceeding of the Thirty-Eighth Southeastern Symposium on System Theory.

[6]  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.

[7]  D. W. J. Stein.  Detection of random signals in Gaussian mixture noise , 1995, IEEE Trans. Inf. Theory.

[8]  David Middleton,et al.  Non-Gaussian Noise Models in Signal Processing for Telecommunications: New Methods and Results for Class A and Class B Noise Models , 1999, IEEE Trans. Inf. Theory.

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

[10]  Saleh A. Alshebeili,et al.  Automatic modulation classification of digital modulations in presence of HF noise , 2012, EURASIP Journal on Advances in Signal Processing.

[11]  Ataollah Ebrahimzadeh,et al.  Automatic Recognition of Digital Communication Signal , 2012 .

[12]  A. E. El-Mahdy.  Classification of MFSK signals over time-varying flat correlated fading channels under class-A impulsive noise environment , 2004 .

[13]  Octavia A. Dobre,et al.  Robust QAM modulation classification algorithm using cyclic cumulants , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

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

[15]  A. J. Han Vinck,et al.  Performance bounds for optimum and suboptimum reception under Class-A impulsive noise , 2002, IEEE Trans. Commun..

[16]  Muhammad Imran,et al.  Performance analysis of Automatic Modulation Classification in multipath fading environment , 2014, 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS).

[17]  Mobien Shoaib,et al.  Support vector machine based classifier for digital modulations in presence of HF noise , 2013, 2013 Saudi International Electronics, Communications and Photonics Conference.