Spectrum sensing for cognitive radio and radar systems

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Doctoral thesis (article-based)
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The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation. In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency. In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes. A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model.
cognitive radio, detection, intercept receiver, pattern recognition, radar, spectrum sensing
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  • [Publication 1]: J. Lundén, V. Koivunen, A. Huttunen and H. V. Poor, Spectrum sensing in cognitive radios based on multiple cyclic frequencies, in Proceedings of the 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom), Orlando, FL, USA, July 31 - August 3, 2007, pp. 37-43. © 2007 IEEE. By permission.
  • [Publication 2]: J. Lundén, V. Koivunen, A. Huttunen and H. V. Poor, Censoring for collaborative spectrum sensing in cognitive radios, in Proceedings of the 41st Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, November 4-7, 2007, pp. 772-776. © 2007 IEEE. By permission.
  • [Publication 3]: J. Lundén, V. Koivunen, A. Huttunen and H. V. Poor, Collaborative cyclostationary spectrum sensing for cognitive radio systems, IEEE Transactions on Signal Processing, vol. 57, no. 11, November 2009. © 2009 IEEE. By permission.
  • [Publication 4]: J. Lundén, S. A. Kassam and V. Koivunen, Nonparametric cyclic correlation based detection for cognitive radio systems, in Proceedings of the 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom), Singapore, May 15-17, 2008. © 2008 IEEE. By permission.
  • [Publication 5]: J. Lundén, S. A. Kassam and V. Koivunen, Robust nonparametric cyclic correlation based spectrum sensing for cognitive radio, IEEE Transactions on Signal Processing, 2009, to appear. © 2009 IEEE. By permission.
  • [Publication 6]: J. Lundén, L. Terho and V. Koivunen, Classifying pulse compression radar waveforms using time-frequency distributions, in Proceedings of the 39th Annual Conference on Information Sciences and Systems (CISS), Baltimore, USA, March 16-18, 2005. © 2005 by authors.
  • [Publication 7]: J. Lundén, L. Terho and V. Koivunen, Waveform recognition in pulse compression radar systems, in Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Mystic, CT, USA, September 28-30, 2005, pp. 271-276. © 2005 IEEE. By permission.
  • [Publication 8]: J. Lundén and V. Koivunen, Automatic radar waveform recognition, IEEE Journal of Selected Topics in Signal Processing, vol. 1, no. 1, pp. 124-136, June 2007. © 2007 IEEE. By permission.
  • [Publication 9]: J. Lundén and V. Koivunen, Robust estimation of radar pulse modulation, in Proceedings of the 6th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Vancouver, Canada, August 27-30, 2006, pp. 271-276. © 2006 IEEE. By permission.
  • [Publication 10]: J. Lundén and V. Koivunen, Scaled conjugate gradient method for radar pulse modulation estimation, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, HI, USA, April 15-20, 2007, vol. 2, pp. 297-300. © 2007 IEEE. By permission.
  • [Errata file]: Errata of publication 7