Robust, non-gaussian wideband spectrum sensing in cognitive radios

IEEE Transactions on Wireless Communications

Volumn 13, Issue 11, 2014, Pages 6410-6421

  Bkassiny, Mario ^a   Jayaweera, Sudharman K ^b  

^a STATE UNIVERSITY OF NEW YORK AT OSWEGO   (United States)

^b MSC01 1070   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; FREQUENCY BANDS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); ITERATIVE METHODS; NEWTON-RAPHSON METHOD; SIGNAL DETECTION;

DETECTION PERFORMANCE; FIXED-POINT ITERATIONS; NON-LINEAR REGRESSION; PARAMETRIC DETECTORS; SIGNALS OF INTERESTS; WIDE FREQUENCY BANDS; WIDE FREQUENCY RANGE; WIDEBAND SPECTRUM SENSING;

COGNITIVE RADIO;

EID: 84910650946     PISSN: 15361276     EISSN: None     Source Type: Journal    
DOI: 10.1109/TWC.2014.2346772     Document Type: Article

References (43)

1. J. Mitola, III and G. Maguire, Jr., Cognitive radio: Making softwareradios more personal, IEEE Pers. Commun., vol. 6, no. 4, pp. 13-18,Aug. 1999.
2. S. Haykin, Cognitive radio: Brain-empowered wireless communications,IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201-220,Feb. 2005.
3. J. Mitola, Cognitive radio architecture evolution, Proc. IEEE, vol. 97,no. 4, pp. 626-641, Apr. 2009.
4. T. Yucek and H. Arslan, A survey of spectrum sensing algorithms forcognitive radio applications, IEEE Commun. Surveys Tuts., vol. 11, no. 1,pp. 116-130, 2009.
5. E. Biglieri, Effect of uncertainties in modeling interferences in coherentand energy detectors for spectrum sensing, in Proc. IEEE ISIT,Aug. 2011, pp. 2418-2421.
6. Z. Tian and G. B. Giannakis, A wavelet approach to wideband spectrumsensing for cognitive radios, in Proc. 1st Int. Conf. Cogn. Radio OrientedWireless Netw. Commun., Mykonos Island, Greece, Jun. 2006, pp. 1-5.
7. J. Unnikrishnan and V. V. Veeravalli, Algorithms for dynamic spectrumaccess with learning for cognitive radio, IEEE Trans. Signal Process.,vol. 58, no. 2, pp. 750-760, Feb. 2010.
8. Q. Zhao, L. Tong, A. Swami, and Y. Chen, Decentralized cognitiveMAC for opportunistic spectrum access in ad hoc networks: A POMDPframework, IEEE J. Sel. Areas Commun., vol. 25, no. 3, pp. 589-600,Apr. 2007.
9. Y. Li, S. Jayaweera, M. Bkassiny, and K. Avery, Optimal myopic sensingand dynamic spectrum access in cognitive radio networks with lowcomplexityimplementations, IEEE Trans. Wireless Commun., vol. 11,no. 7, pp. 2412-2423, Jul. 2012.
10. S. K. Jayaweera and C. G. Christodoulou, Radiobots: Architecture, algorithmsand realtime reconfigurable antenna designs for autonomous,self-learning future cognitive radios, Univ. New Mexico, Albuquerque,NM, USA, Tech. Rep. EECE-TR-11-0001. [Online]. Available: http://repository.unm.edu/handle/1928/12306
11. M. Bkassiny, S. K. Jayaweera, Y. Li, and K. A. Avery, Wideband spectrumsensing and non-parametric signal classification for autonomousself-learning cognitive radios, IEEE Trans. Wireless Commun., vol. 11,no. 7, pp. 2596-2605, Jul. 2012.
12. M. Bkassiny, S. K. Jayaweera, Y. Li, and K. A. Avery, Blind cyclostationaryfeature detection based spectrum sensing for autonomous self-learningcognitive radios, in Proc. IEEE ICC, Ottawa, ON, Canada, Jun. 2012,pp. 1507-1511.
13. R. Martin and D. Thomson, Robust-resistant spectrum estimation, Proc.IEEE, vol. 70, no. 9, pp. 1097-1115, Sep. 1982.
14. A. Sonnenschein and P. Fishman, Radiometric detection of spreadspectrumsignals in noise of uncertain power, IEEE Trans. Aerosp. Electron.Syst., vol. 28, no. 3, pp. 654-660, Jul. 1992.
15. H. V. Poor, An Introduction to Signal Detection and Estimation., 2nd ed.New York, NY, USA: Springer-Verlag, 1998.
16. Y. M. Kim, G. Zheng, S. H. Sohn, and J. M. Kim, An alternative energydetection using sliding window for cognitive radio system, in Proc. 10thICACT, Gangwon-Do, Korea, Feb. 2008, vol. 1, pp. 481-485.
17. D. W. J. Stein, Detection of random signals in Gaussian mixture noise,IEEE Trans. Inf. Theory, vol. 41, no. 6, pp. 1788-1801, Nov. 1995.
18. S. A. Kassam, Signal Detection in Non-Gaussian Noise. New York, NY,USA: Springer-Verlag, 1988.
19. J. Miller and J. Thomas, The detection of signals in impulsive noisemodeled as a mixture process, IEEE Trans. Commun., vol. COM-24,no. 5, pp. 559-563, May 1976.
20. J. Miller and J. Thomas, Robust detectors for signals in non-gaussiannoise, IEEE Trans. Commun., vol. COM-25, no. 7, pp. 686-690,Jul. 1977.
21. D. Middleton, Non-Gaussian noise models in signal processing fortelecommunications: New methods an results for class a and class bnoise models, IEEE Trans. Inf. Theory, vol. 45, no. 4, pp. 1129-1149,May 1999.
22. P. J. Huber (1964, Mar.). Robust estimation of a location parameter. Ann.Math. Stat. [Online]. 35(1), pp. 73-101. Available: http://www.jstor.org/stable/2238020
23. A. Blomqvist and B. Wahlberg, On the relation between weightedfrequency-domain maximum-likelihood power spectral estimation and theprefiltered covariance extension approach, IEEE Trans. Signal Process.,vol. 55, no. 1, pp. 384-389, Jan. 2007.
24. D. Bell and J. Gowdy, Power spectral estimation via nonlinear frequencywarping, IEEE Trans. Acoust., Speech, Signal, vol. ASSP-26, no. 5,pp. 436-441, Oct. 1978.
25. S. Cheung and J. Lim, Combined multiresolution (wide-band/narrowband)spectrogram, IEEE Trans. Signal Process., vol. 40, no. 4, pp. 975-977, Apr. 1992.
26. E. Candes and M. Wakin, An introduction to compressive sampling,IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, Mar. 2008.
27. Z. Tian and G. Giannakis, Compressed sensing for wideband cognitiveradios, in Proc. IEEE ICASSP, Honolulu, HI, USA, Apr. 2007, vol. 4,pp. IV-1357-IV-1360.
28. H. Wu and S. Wang, An efficient and robust approach for widebandcompressive spectrum sensing, in Proc. IEEE ICSPCC, Hong Kong,Aug. 2012, pp. 499-502.
29. S. Oh and R. Kashyap, A robust approach for high resolution frequencyestimation, IEEE Trans. Signal Process., vol. 39, no. 3, pp. 627-643,Mar. 1991.
30. S. Kassam and H. Poor, Robust techniques for signal processing: Asurvey, Proc. IEEE, vol. 73, no. 3, pp. 433-481, Mar. 1985.
31. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.:Cambridge Univ. Press, 2004.
32. S. Banach, Sur les operations dans les ensembles abstraits et leurapplication aux equations integrales, Fund. Math., vol. 3, pp. 133-181,1922.
33. Y. Li, S. K. Jayaweera, C. Ghosh, andM. Bkassiny, Learning-aided sensingscheduling for wide-band cognitive radios, in Proc. IEEE VTC-Fall,Las Vegas, NV, USA, Sep. 2013, pp. 1-5.
34. Y. Tawk, J. Costantine, K. Avery, and C. Christodoulou, Implementationof a cognitive radio front-end using rotatable controlled reconfigurableantennas, IEEE Trans. Antennas Propag., vol. 59, no. 5, pp. 1773-1778,May 2011.
35. Y. Tawk, S. K. Jayaweera, C. G. Christodoulou, and J. Costantine, Acomparison between different cognitive radio antenna systems, in Proc.Int. Symp. ISPACS, Chiangmai, Thailand, Dec. 2011, pp. 1-6.
36. Y. Li, S. K. Jayaweera, M. Bkassiny, and C. Ghosh, Learning-aided subbandselection algorithms for spectrum sensing in wide-band cognitiveradios, IEEE Trans. Wireless Commun., vol. 13, no. 4, pp. 2012-2024,Apr. 2014.
37. R. Martin and S. Schwartz, Robust detection of a known signal in nearlygaussian noise, IEEE Trans. Inf. Theory, vol. IT-17, no. 1, pp. 50-56,Jan. 1971.
38. Y. Zeng and Y.-C. Liang, Covariance based signal detections for cognitiveradio, in Proc. 2nd IEEE Int. Symp. New Frontiers DySPAN, Dublin,Ireland, Apr. 2007, pp. 202-207.
39. T.-H. Li, A nonlinear method for robust spectral analysis, IEEE Trans.Signal Process., vol. 58, no. 5, pp. 2466-2474, May 2010.
40. M. Ramon, T. Atwood, S. Barbin, and C. Christodoulou, Signal classificationwith an SVM-FFT approach for feature extraction in cognitiveradio, in Proc. SBMO/IEEE MTT-S IMOC, Belem, Brazil, Nov. 2009,pp. 286-289.
41. T.-H. Li, Laplace periodogram for time series analysis, J. Amer. Stat.Assoc., vol. 103, no. 482, pp. 757-768, Jun. 2008.
42. J. Rojo-Alvarez, M. Martinez-Ramon, A. Figueiras-Vidal, A. Garcia-Armada, and A. Artes-Rodriguez, A robust support vector algorithmfor nonparametric spectral analysis, IEEE Signal Process. Lett., vol. 10,no. 11, pp. 320-323, Nov. 2003.
43. Z. Liang, R. Jaszczak, and R. Coleman, Parameter estimation of finitemixtures using the EM algorithm and information criteria with applicationto medical image processing, IEEE Trans. Nucl. Sci., vol. 39, no. 4,pp. 1126-1133, Aug. 1992.