Robustness of digitally modulated signal features against variation in HF noise model

High frequency (HF) band has both military and civilian uses. It can be used either as a primary or backup communication link. Automatic modulation classification (AMC) is of an utmost importance in this band for the purpose of communications monitoring; e.g., signal intelligence and spectrum management. A widely used method for AMC is based on pattern recognition (PR). Such a method has two main steps: feature extraction and classification. The first step is generally performed in the presence of channel noise. Recent studies show that HF noise could be modeled by Gaussian or bi-kappa distributions, depending on day-time. Therefore, it is anticipated that change in noise model will have impact on features extraction stage. In this article, we investigate the robustness of well known digitally modulated signal features against variation in HF noise. Specifically, we consider temporal time domain (TTD) features, higher order cumulants (HOC), and wavelet based features. In addition, we propose new features extracted from the constellation diagram and evaluate their robustness against the change in noise model. This study is targeting 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, and 64QAM modulations, as they are commonly used in HF communications.

[1]  Reza Berangi,et al.  A Template Matching Approach to Classification of QAM Modulation using Genetic Algorithm , 2009 .

[2]  G. Bateson,et al.  A systems approach. , 1970, International journal of psychiatry.

[3]  Y. Bar-Ness,et al.  Selection combining for modulation recognition in fading channels , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[4]  Saleh A. Alshebeili,et al.  Classification of digitally modulated signals in presence of non-Gaussian HF noise , 2010, 2010 7th International Symposium on Wireless Communication Systems.

[5]  He Tao,et al.  Modulation classification using ARBF networks , 2004, Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004..

[6]  N. M. Maslin,et al.  HF Communications: A Systems Approach , 1987 .

[7]  Cheol-Sun Park,et al.  Automatic Modulation Recognition using Support Vector Machine in Software Radio Applications , 2007, The 9th International Conference on Advanced Communication Technology.

[8]  H. Smalley.  The systems approach. , 1972, Hospitals.

[9]  R. Berangi,et al.  Modulation classification of QAM and PSK from their constellation using Genetic Algorithm and hierarchical clustering , 2008, 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications.

[10]  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.

[11]  Ling-Ling Meng,et al.  An improved algorithm of modulation classification for digital communication signals based on wavelet transform , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[12]  Yanling Li,et al.  Modulation Classification of MQAM Signals from Their Constellation Using Clustering , 2010, 2010 Second International Conference on Communication Software and Networks.

[13]  R. M. Buehrer,et al.  Implementation of adaptive modulation on the Sunrise software radio , 2002, The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002..

[14]  Asoke K. Nandi,et al.  Automatic digital modulation recognition using artificial neural network and genetic algorithm , 2004, Signal Process..

[15]  Derek Abbott,et al.  An empirical study of the probability density function of HF noise , 2006 .

[16]  Gokhan Memik,et al.  Digital Modulation Classification using Temporal Waveform Features for Cognitive Radios , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

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

[18]  J. E. Giesbrecht,et al.  An empirical study of HF noise near Adelaide Australia , 2009 .

[19]  B. Mobasseri,et al.  Constellation shape as a robust signature for digital modulation recognition , 1999, MILCOM 1999. IEEE Military Communications. Conference Proceedings (Cat. No.99CH36341).

[20]  Olivier Hersent,et al.  M2M Communications: A Systems Approach , 2012 .

[21]  Charles W. Bostian,et al.  MODULATION IDENTIFICATION USING NEURAL NETWORKS FOR COGNITIVE RADIOS , 2005 .

[22]  Cheol-Sun Park,et al.  Automatic Modulation Recognition of Digital Signals using Wavelet Features and SVM , 2008, 2008 10th International Conference on Advanced Communication Technology.

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

[24]  Chi-Wah Kok,et al.  Clustering based distribution fitting algorithm for Automatic Modulation Recognition , 2007, 2007 12th IEEE Symposium on Computers and Communications.

[25]  Muazzam Ali Khan,et al.  Automatic Modulation Recognition of Communication Signals. , 2012 .

[26]  John G. Proakis,et al.  Digital Communications , 1983 .

[27]  Christophe Le Martret,et al.  A general maximum likelihood classifier for modulation classification , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[28]  Asoke K. Nandi,et al.  Automatic Modulation Recognition of Communication Signals , 1996 .

[29]  Philip Constantinou,et al.  Interclass and Intraclass Modulation Recognition using the Wavelet Transform , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[30]  Fuping Wang,et al.  Algorithm for Modulation Recognition Based on High-order Cumulants and Subspace Decomposition , 2006, 2006 8th international Conference on Signal Processing.