Automatic Modulation Classification for adaptive Power Control in cognitive satellite communications

Spectrum Sensing (SS) and Power Control (PC) have been two important concepts of Cognitive Radio (CR). In this paper, a mechanism combining these two topics is proposed to allow a cognitive user, also called Secondary User (SU), to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The suggested SS technique considers Higher Order Statistical (HOS) features of the signal and an efficient Machine Learning (ML) detector, the Support Vector Machine (SVM), in order to constantly monitor the modulation scheme of the PU. Once the Automatic Modulation Classification (AMC) is ensured, the SU can attempt to access the frequency band of the PU and increase its transmitting power until it causes a change of the PU's modulation scheme due to interference. When the SU detects the change of the PU's modulation scheme, then it reduces its transmitting power to a lower level so as to regulate the induced interference. This Adaptive Power Control (APC) algorithm converges to the aforementioned interference limit and guarantees preservation of the PU link QoS.

[1]  Ekram Hossain,et al.  Pattern classification techniques for cooperative spectrum sensing in cognitive radio networks: SVM and W-KNN approaches , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[2]  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.

[3]  Symeon Chatzinotas,et al.  Satellite cognitive communications: Interference modeling and techniques selection , 2012, 2012 6th Advanced Satellite Multimedia Systems Conference (ASMS) and 12th Signal Processing for Space Communications Workshop (SPSC).

[4]  William A. Gardner,et al.  Statistical spectral analysis : a nonprobabilistic theory , 1986 .

[5]  Edwin K. P. Chong,et al.  Analysis of a class of distributed asynchronous power control algorithms for cellular wireless systems , 2000, IEEE Journal on Selected Areas in Communications.

[6]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[7]  V. Koivunen,et al.  Automatic Radar Waveform Recognition , 2007, IEEE Journal of Selected Topics in Signal Processing.

[8]  Symeon Chatzinotas,et al.  Implementation Issues of Cognitive Radio techniques for Ka-band (17.7–19.7 GHz) SatComs , 2014, 2014 7th Advanced Satellite Multimedia Systems Conference and the 13th Signal Processing for Space Communications Workshop (ASMS/SPSC).

[9]  Wei Yu,et al.  Iterative water-filling for Gaussian vector multiple-access channels , 2001, IEEE Transactions on Information Theory.

[10]  Ali Abdi,et al.  Cyclostationarity-Based Modulation Classification of Linear Digital Modulations in Flat Fading Channels , 2010, Wirel. Pers. Commun..

[11]  Jide Julius Popoola,et al.  Application of neural network for sensing primary radio signals in a cognitive radio environment , 2011, IEEE Africon '11.

[12]  Sudharman K. Jayaweera,et al.  Wideband Spectrum Sensing and Non-Parametric Signal Classification for Autonomous Self-Learning Cognitive Radios , 2012, IEEE Transactions on Wireless Communications.

[13]  Cem U. Saraydar,et al.  Efficient power control via pricing in wireless data networks , 2002, IEEE Trans. Commun..

[14]  Dandan Zhang,et al.  SVM-Based Spectrum Sensing in Cognitive Radio , 2011, 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing.

[15]  Mohamed G. El-Tarhuni,et al.  Comparison of linear and polynomial classifiers for co-operative cognitive radio networks , 2010, 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

[16]  Hang Liu,et al.  A New Approach to Improve Signal Classification in Low SNR Environment in Spectrum Sensing , 2008, 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  Symeon Chatzinotas,et al.  Cognitive Radio Techniques for Satellite Communication Systems , 2013, 2013 IEEE 78th Vehicular Technology Conference (VTC Fall).

[19]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[20]  Muqing Wu,et al.  Cooperative Spectrum Sensing Based on Artificial Neural Network for Cognitive Radio Systems , 2012, 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing.

[21]  Xianzhong Xie,et al.  SOM-GA-SVM Detection Based Spectrum Sensing in Cognitive Radio , 2012, 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing.

[22]  Sofie Pollin,et al.  Identifying Spectrum Usage by Unknown Systems using Experiments in Machine Learning , 2009, 2009 IEEE Wireless Communications and Networking Conference.

[23]  B. Ramkumar,et al.  Automatic modulation classification for cognitive radios using cyclic feature detection , 2009, IEEE Circuits and Systems Magazine.

[24]  Wei Lin,et al.  Artificial Neural Network Based Spectrum Sensing Method for Cognitive Radio , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[25]  Sudharman K. Jayaweera,et al.  Blind cyclostationary feature detection based spectrum sensing for autonomous self-learning cognitive radios , 2012, 2012 IEEE International Conference on Communications (ICC).

[26]  Eitan Altman,et al.  CDMA Uplink Power Control as a Noncooperative Game , 2002, Wirel. Networks.

[27]  Jeffrey H. Reed,et al.  A new approach to signal classification using spectral correlation and neural networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[28]  Symeon Chatzinotas,et al.  Cognitive radio scenarios for satellite communications: The CoRaSat approach , 2013, 2013 Future Network & Mobile Summit.

[29]  Dan Liu,et al.  A novel signal recognition algorithm based on SVM in cognitive networks , 2010, 2010 IEEE 12th International Conference on Communication Technology.

[30]  Marina Petrova,et al.  Multi-class classification of analog and digital signals in cognitive radios using Support Vector Machines , 2010, 2010 7th International Symposium on Wireless Communication Systems.

[31]  S. Ravi,et al.  Second-order Statistical Approach for Digital modulation Scheme Classification in Cognitive Radio using Support Vector Machine and k-Nearest Neighbor Classifier , 2013, J. Comput. Sci..

[32]  Aldebaro Klautau,et al.  Automatic modulation classification for cognitive radio systems: Results for the symbol and waveform domains , 2009, 2009 IEEE Latin-American Conference on Communications.

[33]  Andrea F. Cattoni,et al.  Neural Networks Mode Classification based on Frequency Distribution Features , 2007, 2007 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications.