ENG  Vol.13 No.1 , January 2021
Non-Cooperative Spectrum Sensing Based on Cyclostationary Model of Digital Signals in the Context of Cognitive Radio
Abstract: This paper addresses the problem of the opportunistic spectrum access in Cognitive Radio. Indeed, most spectrum sensing algorithms suffer from a high computational cost to achieve the detection process. They need a prior knowledge of signal characteristics and present a bad performance in low Signal to Noise Ratio (SNR) environment. The choice of the optimal detection threshold is another issue for these spectrum sensing algorithms. To overcome the limits of spectrum detectors, we propose in this paper, a blind detection method based on the cyclostationary features of communication signals. Our detector evaluates the level of hidden periodicity contained in the observed signal to make decision on the state of a bandwidth. In order to reduce the computational cost, we take advantage of the FFT Accumulation Method to estimate the cyclic spectrum of the observed signal. Then, we generate the Cyclic Domain Profile of the cyclic spectrum which allows us to evaluate the level of the hidden periodicity in the signal. This level of periodicity is quantified through the crest factor of Cyclic Domain Profile, which represents the decision statistic of the proposed detector. We have established the analytic expression of the optimal threshold of the detection and the probability of detection to evaluate the performance of the proposed detector. Simulation results show that the proposed detector is able to detect the presence of a communication signal on a bandwidth in a very low SNR scenario.
Cite this paper: Kadjo, J. , Agoua, R. , Bamba, A. , Konaté, A. and Asseu, O. (2021) Non-Cooperative Spectrum Sensing Based on Cyclostationary Model of Digital Signals in the Context of Cognitive Radio. Engineering, 13, 56-70. doi: 10.4236/eng.2021.131005.

[1]   Gardner, W. (1986) Introduction to Random Process with Applications to Signals and Systems. Gardner, W., Ed., North-Holland, Macmillan, New York.

[2]   Gardner, W.A. (1986) Statistical Spectral Analysis: A Nonprobabilistic Theory. Prentice-Hall, Inc.,Tokyo.

[3]   Gardner, W. (1991) Exploitation of Spectral Redundancy in Cyclostationary Signals. IEEE Signal Processing Magazine, 8, 14-36.

[4]   Prithiviraj, V., Sarankumar, B., Kalaiyarasan, A., Praveen, P. and Signh, N. (2011) Cyclostionary Analysis Method of Spectrum Sensing for Cognitive Radio. Wireless VITAE, IEEE, Chennai, India, 1-5.

[5]   Gardner, W. (1994) Cyclostationarty in Communications and Signal Processing. IEEE Press, New York.

[6]   Tom, C. (1995) Investigation and implementation of computationally-efficient algorithm for cyclic spectral analysis. Master’s Thesis, Carleton University Ottawa, Ontario, Canada.

[7]   Jang, M. (2014) Blind Cyclostationary Spectrum Sensing in Cognitive Radios. IEEE Communications Letters, 8, 393-396.

[8]   Bennett, W.R. (1958) Statistics of Regenerative Digital Transmission. The Bell System Technical Journal, 37, 1501-1542.

[9]   Gudzenko, L. (1959) On Periodically Nonstationary Processes. Radiotekhnika i Electronika, 4, 1062-1064.

[10]   Markelov, V. (1966) Axis Crossings and Relative Time of Existence of a Periodically Nonstationary Random Process. Soviet Radiophysics, 9, 440-443.

[11]   Gladyshev, E. (1961) Periodically Correlated Random Sequence. Soviet Mathematics, 2, 385-388.

[12]   Herbst, L., et al. (1963) Almost Periodic Variances. The Annals of Mathematical Statistics, 34, 1549-1557.

[13]   Herbst, L.J. (1963) Periodogram Analysis and Variance Uctuations. Journal of the Royal Statistical Society: Series B (Methodological), 25, 442-450.

[14]   Herbst, L. (1965) The Statistical Fourier Analysis of Variances. Journal of the Royal Statistical Society: Series B (Methodological), 27, 159-165.

[15]   Herbst, L.J. (1964) Spectral Analysis in the Presence of Variance Uctuations. Journal of the Royal Statistical Society: Series B (Methodological), 26, 354-360.

[16]   de Feriet, J.K. (1962) Correlation and Spectrum of Asymptotically Stationary Random Functions. Mathematical Studies, 30, 55-67.

[17]   Parzen, E. (1963) On Spectral Analysis with Missing Observations and Amplitude Modulation. Sankhyā: The Indian Journal of Statistics, Series A, 25, 383-392.

[18]   (1961) Spectral Analysis of asymptotically Stationary Time Series. Stanford University Calif. Applied Mathematics and Statistics Lab, Tech. Rep.

[19]   Monin, A. (1963) Stationary and Periodic Time Series in the General Circulation of the Atmosphere. Proceedings of the Symposium on Time Series Analysis, Wiley, New York, 144-151.

[20]   Gladyshev, E. (1963) Periodically and Almost-Periodically Correlated Random Processes with a Continuous Time Parameter. Theory of Probability Its Applications, 8, 173-177.

[21]   Dehay, D. and Hurd, H. (1994) Representation and Estimation for Periodically and Almost Periodically Correlated Random Processes. Cyclostationarity in Communications and Signal Processing, IEEE Press, New York, 295-328.

[22]   Dandawate, A.V. and Giannakis, G.B. (1995) Asymptotic Theory of Mixed Time Averages and kth-Order Cyclic-Moment and Cumulant Statistics. IEEE Transactions on Information Theory, 41, 216-232.

[23]   Gardner, W. (1986) The Spectral Correlation Theory of Cyclostationary Time-Series. Signal Processing, Elsevier Science Publishers, 11, 13-36.

[24]   Gardner, W. and Spooner, C. (1992) Signal Interception: Performance Advantages of Cyclic-Feature Detector. IEEE Transactions on Communications, 40, 149-159.

[25]   Rostaing, P. (1997) Détection de signaux modulés en exploitant leurs propriétés cyclostationnaires: Application aux signaux sonar. Ph.D. Dissertation, Université de Nice, Nice.

[26]   Capdessus, C. (1992) Aide au diagnostic des machines tournantes par traitement du signal. Ph.D. Dissertation, Institut National Polytechnique de Grenoble, Grenoble.

[27]   Randall, R.B., Antoni, J. and Chobsaard, S. (2001) The Relationship between Spectral Correlation and Envelope Analysis in the Diagnostics of Bearing Faults and Other Cyclostationary Machine Signals. Mechanical Systems and Signal Processing, 15, 945-962.

[28]   Weber, R. and Faye, C. (1998) Real Time Detector for Cyclostationary Rfi in Radio Astronomy. 9th European Signal Processing Conference (EUSIPCO 1998), Island of Rhodes, 8-11 September 1998, 1-4.

[29]   Roussel, J. (2014) Modélisation cyclostationnaire et séparation de sources des signaux électromyographiques. Ph.D. dissertation, Université d’Orléans, France.

[30]   Sutton, P.D., Nolan, K.E. and Doyles, L.E. (2008) Cyclostationary Signatures in Pratical Cognitive Radio Applications. IEEE Journal on Selected Areas in Communications, 26, 13-24.

[31]   Napolitano, A. (2016) Cyclostationarity: New Trends and Applications. Signal Processing, 120, 385-408.

[32]   Kadjo, J.-M., Yao, K.C. and Mansour, A. (2016) Blind Detection of Cyclostationary Features in the Context of Cognitive Radio. IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Limassol, Cyprus, December 2016, 150-155.

[33]   Brown, W. (1987) On the Theory of Cyclostationary Signals. Ph.D. Dissertation, University of California, Davis, California, USA.

[34]   Brown, W. and Loomis, H. (1993) Digital Implementation of Spectral Correlation Analyzers. IEEE Transactions on Signal Processing, 41, 703-720.

[35]   Pace, P. (2009) Detecting and Classifying Low Probability of Intercept Radar. 2nd Edition, House, A., Ed., Artech House, Boston, London.

[36]   Robert, R., Brown, W. and Loomis, H. (1991) Computationally Efficient Algorithms for Cyclic Spectral Analysis. Signal Processing Magazine, 8, 38-49.

[37]   (1993) A Review of Digital Spectral Correlation Analysis: Theory and Implementation. Article 6 in Part II of Cyclostationarity in Communications and Signal Processing, Gardner, W.A., Ed., IEEE Press, New York, 455-479.

[38]   Tom, C. (1995) Cyclostationary Spectral Analysis of Typical Satcom Signals Using the FFT Accumulation Method. Defence Research Establishement Ottawa, Ontario, Canada, Technical Report 1280.

[39]   Kay, S.M. (1993) Fundamentals of Statistical Signal Processing. Prentice Hall PTR.

[40]   Dupuis, D. and Field, C. (1998) Robust Estimation of Extremes. Canadian Journal of Statistics, 26, 199-215.

[41]   De Haan, L. and Ferreira, A. (2007) Extreme Value Theory: An Introduction. Springer Science & Business Media.

[42]   Kotz, S. and Nadarajah, S. (2000) Extreme Value Distributions: Theory and Applications. World Scientific.