Wideband spectrum sensing and non-parametric signal classification for autonomous self-learning cognitive radios

IEEE Transactions on Wireless Communications

Volumn 11, Issue 7, 2012, Pages 2596-2605

  Bkassiny, Mario ^a   Jayaweera, Sudharman K ^a   Li, Yang ^a   Avery, Keith A ^b  

^a University of New Mexico   (United States)

^b AIR FORCE RESEARCH LABORATORY   (United States)

Author keywords

Chinese restaurant process; Cognitive radios; cyclostationary detection; Dirichlet process; Radiobot; threshold learning

Indexed keywords

CHINESE RESTAURANT PROCESS; CYCLOSTATIONARY; DIRICHLET PROCESS; RADIOBOT; THRESHOLD-LEARNING;

COGNITIVE RADIO; DETECTORS; FADING CHANNELS; LEARNING ALGORITHMS; MODULAR ROBOTS; RADIO SYSTEMS; TIMING JITTER;

SIGNAL DETECTION;

EID: 84864131832     PISSN: 15361276     EISSN: None     Source Type: Journal    
DOI: 10.1109/TWC.2012.051512.111504     Document Type: Article

References (43)

1. J. Mitola, III and G. Maguire, Jr., "Cognitive radio: making software radios more personal," IEEE Personal 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. N. Devroye, M. Vu, and V. Tarokh, "Cognitive radio networks," IEEE Signal Process. Mag., vol. 25, no. 6, pp. 12-23, Nov. 2008.
4. A. Goldsmith, S. Jafar, I. Maric, and S. Srinivasa, "Breaking spectrum gridlock with cognitive radios: an information theoretic perspective," Proc. IEEE, vol. 97, no. 5, pp. 894-914, May 2009.
5. S. K. Jayaweera and C. G. Christodoulou, "Radiobots: architecture, algorithms and realtime reconfigurable antenna designs for autonomous, selflearning future cognitive radios," University of New Mexico, Technical Report EECE-TR-11-0001, Mar. 2011. Available: http://repository.unm. edu/handle/1928/12306
6. J. Mitola, "Cognitive radio architecture evolution," Proc. IEEE, vol. 97, no. 4, pp. 626-641, Apr. 2009.
7. Y. Tachwali, F. Basma, and H. Refai, "Cognitive radio architecture for rapidly deployable heterogeneous wireless networks," IEEE Trans. Consum. Electron., vol. 56, no. 3, pp. 1426-1432, Aug. 2010.
8. K. Smitha and A. Vinod, "A reconfigurable low complexity architecture for channel adaptation in cognitive radio," in Proc. 2008 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1-5.
9. S. Wang, H. Zhao, B. Zhang, H. Liu, and L. Xie, "An autonomic communication based conceptual and architecture model for cognitive radio nodes," in Proc. 2010 IET International Conference on Wireless, Mobile and Multimedia Networks, pp. 200-204.
10. S. Maleki, A. Pandharipande, and G. Leus, "Two-stage spectrum sensing for cognitive radios," in Proc. 2010 IEEE International Conference on Acoustics Speech and Signal Processing, pp. 2946-2949.
11. L. Luo, N. Neihart, S. Roy, and D. Allstot, "A two-stage sensing technique for dynamic spectrum access," IEEE Trans. Wireless Commun., vol. 8, no. 6, pp. 3028-3037, June 2009.
12. M. Ramon, T. Atwood, S. Barbin, and C. Christodoulou, "Signal classification with an SVM-FFT approach for feature extraction in cognitive radio," in Proc. 2009 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference, pp. 286-289.
13. H. Hu, J. Song, and Y. Wang, "Signal classification based on spectral correlation analysis and SVM in cognitive radio," in Proc. 2008 International Conference on Advanced Information Networking and Applications, pp. 883-887.
14. Y. W. Teh, "Dirichlet processes," in Encyclopedia of Machine Learning. Springer, 2007.
15. N. Shetty, S. Pollin, and P. Pawelczak, "Identifying spectrum usage by unknown systems using experiments in machine learning," in Proc. 2009 IEEE Wireless Communications and Networking Conference, pp. 1-6.
16. A. Galindo-Serrano and L. Giupponi, "Distributed Q-learning for aggregated interference control in cognitive radio networks," IEEE Trans. Veh. Technol., vol. 59, no. 4, pp. 1823-1834, May 2010.
17. J. Lunden, V. Koivunen, S. Kulkarni, and H. Poor, "Reinforcement learning based distributed multiagent sensing policy for cognitive radio networks," in Proc. 2011 IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 642-646.
18. M. Bkassiny, S. K. Jayaweera, and K. A. Avery, "Distributed reinforcement learning based MAC protocols for autonomous cognitive secondary users," in Proc. 2011 Wireless and Optical Communications Conference, pp. 1-6.
19. D. Pados, P. Papantoni-Kazakos, D. Kazakos, and A. Koyiantis, "Online threshold learning for Neyman-Pearson distributed detection," IEEE Trans. Syst., Man Cybernetics, vol. 24, no. 10, pp. 1519-1531, Oct. 1994.
20. T. Atwood, "RF channel characterization for cognitive radio using support vector machines," Ph.D. dissertation, University of New Mexico, Nov. 2009.
21. C.-C. Huang, C.-Y. Wang, and J.-T. Wu, "A CMOS 6-bit 16-gs/s timeinterleaved ADC using digital background calibration techniques," IEEE J. Solid-State Circuits, vol. 46, no. 4, pp. 848-858, Apr. 2011.
22. 12-Bit, 1-GSPSAnalog-to-Digital Converter. Available: http://focus.ti. com/lit/ds/symlink/ads5400.pdf
23. D. Wieruch and V. Pohl, "A cognitive radio architecture based on sub-Nyquist sampling," in Proc. 2011 IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 576-585.
24. Y. Tawk, J. Costantine, K. Avery, and C. Christodoulou, "Implementation of a cognitive radio front-end using rotatable controlled reconfigurable antennas," IEEE Trans. Antennas Propag., vol. 59, no. 5, pp. 1773-1778, May 2011.
25. M. Patzold, Mobile Fading Channels, 1st edition. John Wiley & Sons, Ltd., 2002.
26. A. Goldsmith, Wireless Communications, 1st edition. Cambridge University Press, 2005.
27. W. Gardner, "Measurement of spectral correlation," IEEE Trans. Acoustics, Speech Signal Process., vol. 34, no. 5, pp. 1111-1123, Oct. 1986.
28. M. Bkassiny, S. K. Jayaweera, Y. Li, and K. A. Avery, "Blind cyclostationary feature detection based spectrum sensing for autonomous selflearning cognitive radios," in 2012 IEEE International Conference on Communications.
29. F. Millioz and N. Martin, "Estimation of a white Gaussian noise in the short time Fourier transform based on the spectral kurtosis of the minimal statistics: application to underwater noise," in Proc. 2010 IEEE International Conference on Acoustics Speech and Signal Processing, pp. 5638-5641.
30. A. Sonnenschein and P. Fishman, "Radiometric detection of spreadspectrum signals in noise of uncertain power," IEEE Trans. Aerospace and Electron. Syst., vol. 28, no. 3, pp. 654-660, July 1992.
31. B. Shent, L. Huang, C. Zhao, Z. Zhou, and K. Kwak, "Energy detection based spectrum sensing for cognitive radios in noise of uncertain power," in Proc. 2008 International Symposium on Communications and Information Technologies, pp. 628-633.
32. O. Setter, M. Sharir, and D. Halperin, "Constructing two-dimensional Voronoi diagrams via divide-and-conquer of envelopes in space," in Proc. 2009 International Symposium on Voronoi Diagrams, pp. 43-52.
33. W. Gardner, W. Brown, and C.-K. Chen, "Spectral correlation of modulated signals-part II: digital modulation," IEEE Trans. Commun., vol. 35, no. 6, pp. 595-601, June 1987.
34. W. Gardner, A. Napolitano, and L. Paura, "Cyclostationarity: half a century of research," Signal Process., vol. 86, no. 4, pp. 639-697, Apr. 2006. Available: http://dx.doi.org/10.1016/j.sigpro.2005.06.016
35. W. A. Gardner, Introduction to Random Processes: With Applications to Signals and Systems, 2nd edition. McGraw-Hill, 1990.
36. J. Parsons and A. Bajwa, "Wideband characterisation of fading mobile radio channels," Commun., Radar and Signal Process., IEE Proceedings F, vol. 129, no. 2, p. 95, Apr. 1982.
37. D.-S. Yoo and W. Stark, "Characterization of wssus channels: normalized mean square covariance," IEEE Trans. Wireless Commun., vol. 4, no. 4, pp. 1575-1584, July 2005.
38. P. Hoeher, "A statistical discrete-time model for the WSSUS multipath channel," IEEE Trans. Veh. Technol., vol. 41, no. 4, pp. 461-468, Nov. 1992.
39. K.-W. Yip and T.-S. Ng, "Discrete-time model for digital communications over a frequency-selective Rician fading wssus channel," IEE Proc.-Commun., vol. 143, no. 1, pp. 37-42, Feb. 1996.
40. E. Fox, E. Sudderth, M. Jordan, and A. Willsky, "Bayesian nonparametric methods for learning Markov switching processes," IEEE Signal Process. Mag., vol. 27, no. 6, pp. 43-54, Nov. 2010.
41. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical Dirichlet processes," J. Amer. Statistical Assoc., vol. 101, no. 476, pp. 1566-1581, Dec. 2006.
42. T. S. Ferguson, "A Bayesian analysis of some nonparametric problems," The Annals of Statistics, vol. 1, no. 2, pp. 209-230, 1973. Available: http://dx.doi.org/10.2307/2958008
43. Y. M. Kim, G. Zheng, S. H. Sohn, and J. M. Kim, "An alternative energy detection using sliding window for cognitive radio system," in Proc. 2008 International Conference on Advanced Communication Technology, vol. 1, pp. 481-485.