The week's pick
Reseach Article
Signal Type Detection in CRN :A Machine Learning Framework Using Spectral Correlation Feature
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 7 |
| Year of Publication: 2017 |
| Authors: Nayan Basumatary, Bhabesh Nath, Nityananda Sarma |
10.5120/ijca2017915792
|
Nayan Basumatary, Bhabesh Nath, Nityananda Sarma . Signal Type Detection in CRN :A Machine Learning Framework Using Spectral Correlation Feature. International Journal of Computer Applications. 177, 7 ( Nov 2017), 1-7. DOI=10.5120/ijca2017915792
Abstract
Spectrum sensing is the core component in cognitive radio for ensuring effective dynamic spectrum access.Accurate signal classification in fading channels and low signal to noise ratio environment is a major challenge.Due to lack of information about the modulation scheme used it cannot distinguish whether signal present is that of the primary or any other secondary user communication.In this paper a multiclass modulation classification hierarchical framework is proposed which exploits the cyclostationary features extracted for identifying modulation form without any priori knowledge of signal properties like frequency,phase and symbol rate.The cyclostationary features extracted using spectral correlation analysis at each secondary user are treated as features and fed into the framework.The classification done is based on one-against-all approach to get the modulation scheme of the signal under observation. The performance of proposed framework for each classifiers used is quantified in terms of detection accuracy,average training time and classification delay. It is demonstrated through simulation that an optimal feature set can be obtained to classify a range of modulation schemes with the proposed hierarchical framework. The proposed framework is found to be effective for modulation detection of signals when compared with two existing methods.
References
- S. Haykin, Cognitive radio: brain-empowered wireless communications IEEE J. Select. Areas Communication, vol. 23,(2005), 201-220.
- S. D. Cabric, S. M. Mishra and R. W. Brodersen, Implementation issues in spectrum sensing for cognitive radios, (in Proc. of Asilomar Conf. on Signals, Systems, and Computers ), vol. 1 (2004), pp. 772 776.
- I. F. Akyildiz, W.Y. Lee, M. C. Vuran and S. Mohanty NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey, (Computer Networks) (2006). vol.50, no.13 pp.2127-2159
- R. Brodersen, A. Wolisz, D. Cabric, S. Mishra, and D. Willkomm, Corvus: A cognitive radio approach for usage of virtual unlicensed spectrum, available at http://bwrc.eecs.berkeley.edu/Research/MCMA/.
- R. Tandra and A. Sahai, SNR walls for signal detection, IEEE J. Sel.Topics Signal Process vol.2, no.1 (2008), pp. 4-17.
- C. Spooner, Automatic radio frequency environment analysis, in Proceedings on the Thirty-Fourth Asilomar Conference, 2000.
- On the utility of sixth-order cyclic cumulants for rf signal classification, in Conference Record of the Thirty-Fifth Asilomar Conference on (Signals, Systems and Computers), vol. 1, (2001), pp. 8907.
- Q. Zhao and B. M. Sadler, A survey of dynamic spectrum access, (IEEE Signal Process. Mag.) vol.24, no.3, (2007), pp. 79-89.
- C. Cortes and V. Vapnik, Support-vector networks,vol. 20, no. 3, (Machine Learning Journal.)(1995),pp. 273297.
- R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley,2nd Edition,( 2001).
- J. S. Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge, UK.
- A. Cormiejols and M. Moulet Machine Learning : A Survey ,Knowledge-Based Systems: Advanced Concepts, Techniques and Applications. (World Scientific Press, Athens) (1997) pp.61-86.
- M.Sun, J. A multi class Support vector Machine: Theory and Model, (World Scientific Press, 2013)vol. 12, No.6, pp.11751199.
- A. Ghasemi, E. Sousa,Collaborative spectrum sensing for opportunistic access in fading environments, IEEE DySPAN (2005) pp. 131136.
- S. Mishra, A. Sahai, R. Brodersen, Cooperative sensing among cognitive radios, (Proc. of IEEE ICC 2006), (vol. 4, 2006,) pp. 16581663.
- J. Unnikrishnan, V.V. Veeravalli,Cooperative sensing for primary detection in cognitive radio, (IEEE Journal of Selected Topics in Signal Processing), (2008) ,(2(1)) 1827.
- T. Yucek, H. Arslan,A survey of spectrum sensing algorithms for cognitive radio applications (IEEE Communications Surveys Tutorials,(2009) 116130.
- I. F. Akyildiz, B.F Lo, and R. Balakrishnan, Cooperative Spectrum Sensing in Cognitive Radio Networks: A survey, (Physical communication 4.1, (2011)), 40 - 62.
- W. Gardner, Statistical Spectral Analysis: A Nonprobabilistic Theory. New Jersey: Prentice Hall, (1987).
- W.A.Gardner, An Introduction to Cyclostationary Signalsl (IEEE Press, Piscataway, NJ.) vol. 12, No.6, (1993)
- W. A. Gardner,Measurement of Spectral Correlation (IEEE Transaction on Acoustics, Speech, and Signal Processing) vol. ASSP-34, No. 5,(Oct.1986).
- A. Fehske, J. Gaeddert and J. H. Reed, A New Approach to Signal Classification Using Spectral Correlation and Neural Networks,IEEE International Symposium on New Frontiers in DySpan, (2005).
- B. Le, T. Rondeau, D. Maldonado, and C. Bostian, Modulation identification using neural networks for cognitive radios,( SDR Forum Technical Conf.), (2005).
- A. Swami and B. Sadler, Hierarchical digital modulation classification using cumulants, vol. 48, no. 3, IEEE Transactions on Communication,2000), pp. 416429.
- Hao Hu, Cyclostationary Approach to signal detection and Classification in Cognitive radio systems, (Beijing University of Posts and Telecommunications, P.R.China.)(2009).
- P.Sutton et.al Cyclostationary Signatures in Practical Cognitive Radio Applications, vol. 26,No. 1,(IEEE Journal on selected areas in communications,(2008.)
- K.Kim,et.al Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio, (In 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks), 17-20 April 2007,Page(s):212 - 215.
- M.Bkassiny,Y.Li, and S.K. Jayaweera, A survey on machine Learning techniques in Cognitive radios, IEEE Communications Surveys and Tutorials, vol.15,no.3 (2013), pp. 1136- 1159.
- K.M Thilina, K.W. Choi, N. Saquib and E. Hossain Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks, (IEEE J. Selected areas in communications, vol.31. no.11., (2013).
- L. Freitas et.al Data Mining Applied to cognitive radio systems , Advances in Data Mining Knowledge Discovery and Applications,(2012).
Index Terms
Keywords