Advertisement

OFDM-Based Cognitive Radios for Dynamic Spectrum Access Networks

  • Rakesh Rajbanshi
  • Alexander M. Wyglinski
  • Gary J. Minden

Keywords

Orthogonal Frequency Division Multiplex Cognitive Radio Primary User Secondary User Orthogonal Frequency Division Multiplex System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Federal Communications Commission, “Spectrum policy task force report,” ET Docket No. 02–135, 2002.Google Scholar
  2. 2.
    M. J. Marcus, “Unlicensed cognitive sharing of TV spectrum: The controversy at the federal communications commission,” IEEE Commun. Mag., vol. 43, pp. 24–25, May 2005.CrossRefGoogle Scholar
  3. 3.
    Federal Communications Commission, “Unlicensed operation in the TV broadcast bands,” ET Docket No. 04-186, 2004.Google Scholar
  4. 4.
    J. Mitola III, “Cognitive radio for flexible mobile multimedia communications,” in Proc. IEEE Int. Wksp. Mobile Multimedia Commun., vol. 1, (San Diego, CA, USA), pp. 3–10, Nov. 1999.Google Scholar
  5. 5.
    F. N. Hatfield and P. J. Weiser, “Property rights in spectrum: Taking the next step,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 43–55, Nov. 2005.CrossRefGoogle Scholar
  6. 6.
    T. R. Newman, B. A. Barker, A. M. Wyglinski, A. Agah, J. B. Evans, and G. J. Minden, “Cognitive engine implementation for wireless multicarrier transceivers,” Wireless Commun. Mobile Comput., vol. 6, in press.Google Scholar
  7. 7.
    J. Zhao, H. Zheng, and G.-H. Yang, “Distributed coordination in dynamic spectrum allocation networks,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 259–268, Nov. 2005.CrossRefGoogle Scholar
  8. 8.
    T. W. Rondeau, B. Le, D. Maldonado, D. Scaperoth, and C. W. Bostian, “Cognitive radio formulation and implementation,” in Proc. 1st Int. Conf. on Cognitive Radio Oriented Wireless Networks and Commun. (Mykonos, Greece), June 2006.Google Scholar
  9. 9.
    G. J. Minden, J. B. Evans, L. Searl, D. DePardo, R. Rajbanshi, J. Guffey, Q. Chen, T. Newman, V. R. Petty, F. Weidling, M. Lehnherr, B. Cordill, D. Datla, B. Barker, and A. Agah, “An agile radio for wireless innovation,” submitted to IEEE Commun. Mag., Oct. 2006.Google Scholar
  10. 10.
    A. J. Viterbi, “Wireless digital communication: A view based on three lessons learned,” IEEE Commun. Mag., vol. 29, pp. 33–36, Sept. 1991.CrossRefGoogle Scholar
  11. 11.
    J. A. C. Bingham, “Multicarrier modulation for data transmission: An idea whose time has come,” IEEE Commun. Mag., vol. 28, pp. 5–14, May 1990.CrossRefMathSciNetGoogle Scholar
  12. 12.
    B. R. Saltzberg, “Comparison of single-carrier and multitone digital modulation for ADSL applications,” IEEE Commun. Mag., vol. 36, pp. 114–121, Nov. 1998.CrossRefGoogle Scholar
  13. 13.
    L. J. Cimini Jr., “Analysis and simulation of a digital mobile channel using orthogonal frequency division multiplexing,” IEEE Trans. Commun., vol. 33, pp. 665–675, July 1985.CrossRefGoogle Scholar
  14. 14.
    T. A. Weiss and F. K. Jondral, “Spectrum pooling: An innovative strategy for the enhancement of spectrum efficiency,” IEEE Commun. Mag., vol. 43, pp. S8–14, Mar. 2004.CrossRefGoogle Scholar
  15. 15.
    H. Tang, “Some physical layer issues of wide-band cognitive radio systems,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 151–159, Nov. 2005.Google Scholar
  16. 16.
    J. D. Poston and W. D. Horne, “Discontiguous OFDM considerations for dynamic spectrum access in idle TV channels,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 607–610, Nov. 2005.CrossRefGoogle Scholar
  17. 17.
    M. P. Wylie-Green, “Dynamic spectrum sensing by multiband OFDM radio for interference mitigation,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 619–625, Nov. 2005.Google Scholar
  18. 18.
    R. Rajbanshi, Q. Chen, A. M. Wyglinski, J. B. Evans, and G. J. Minden, “Comparative study of frequency agile data transmission schemes for cognitive radio transceivers,” in Proc. 2nd Annual International Wireless Internet Conference – Int. Wksp. on Technol. and Policy for Accessing Spectrum (Boston, MA, USA), Aug. 2006.Google Scholar
  19. 19.
    R. Rajbanshi, Q. Chen, A. M. Wyglinski, G. J. Minden, and J. B. Evans, “Quantitative comparison of agile modulation techniques for cognitive radio transceivers,” in Proc. IEEE Consumer Commun. and Networking Conf. – Workshop on Cognitive Radio Networks (Las Vegas, NV, USA), Jan. 2007.Google Scholar
  20. 20.
    A. M. Wyglinski, “Effects of bit allocation on non-contiguous multicarrier-based cognitive radio transceivers,” in Proc. 64th IEEE Veh. Technol. Conf. – Fall, (Montreal, Canada), Sept. 2006.Google Scholar
  21. 21.
    J. Tellado, Multicarrier Modulation with low PAR: Applications to DSL and Wireless. Massachusetts, USA: Kluwer Academic Publishers, 2000.Google Scholar
  22. 22.
    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 Journal (Elsevier), vol. 50, pp. 2127–2159, Sept. 2006.MATHCrossRefGoogle Scholar
  23. 23.
    C. Raman, R. D. Yates, and N. B. MAndayam, “Scheduling variable rate links via a spectrum server,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 110–118, Nov. 2005.Google Scholar
  24. 24.
    C. Peng, H. Zheng, and B. Y. Zhao, “Utilization and fairness in spectrum assignment for opportunistic spectrum access,” Mobile Networks Appl., vol. 11, pp. 555–576, Aug. 2006.CrossRefGoogle Scholar
  25. 25.
    M. M. Buddhikot, P. Kolodzy, S. Miller, K. Ryan, and J. Evans, “DIMSUMnet: New directions in wireless networking using coordinated dynamic spectrum,” in Proc. IEEE Int. Symp. World of Wireless Mobile Multimedia Networks (Taormina, Italy), pp. 78–85, June 2005.Google Scholar
  26. 26.
    V. Brik, E. Rozner, S. Banerjee, and P. Bahl, “DSAP: A protocol for coordinated spectrum access,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 611–614, Nov. 2005.Google Scholar
  27. 27.
    L. Cao and H. Zheng, “Distributed spectrum allocation via local bargaining,” in Proc. IEEE Sensor and Ad Hoc Commun. and Networks (Santa Clara, CA, USA), pp. 475–486, Sept. 2005.Google Scholar
  28. 28.
    J. Huang, R. A. Berry, and M. L. Honig, “Spectrum sharing with distributed interference compensation,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 88–93, Nov. 2005.Google Scholar
  29. 29.
    W. Krenik and A. Batra, “Cognitive radio techniques for wide area networks,” in 42nd Design Automation Conf. (Anaheim, CA, USA), pp. 409–412, June 2005.Google Scholar
  30. 30.
    A. Ghasemi and E. S. Sousa, “Collaborative spectrum sensing for opportunistic access in fading environments,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 131–136, Nov. 2005.Google Scholar
  31. 31.
    H. Zheng and L. Cao, “Device centric spectrum management,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks (Baltimore, MD, USA), pp. 56–65, Sept. 2005.Google Scholar
  32. 32.
    S. Sankaranarayanan, P. Papadimitratos, A. Mishra, and S. hershey, “A bandwidth sharing approach to improve licensed spectrum utilization,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 611–614, Nov. 2005.Google Scholar
  33. 33.
    Q. Zhao, L. Tong, and A. Swami, “Decentralized cognitive MAC for dynamic spectrum access,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 224–232, Nov. 2005.Google Scholar
  34. 34.
    R. Etkin, A. Parekh, and D. Tse, “Spectrum sharing for unlicensed bands,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 251–258, Nov. 2005.Google Scholar
  35. 35.
    M. Z. Win and R. A. Scholtz, “Impulse radio: How it works,” in IEEE Communications Letters, vol. 2, No. 2, pp. 36–38, Feb. 1998.CrossRefGoogle Scholar
  36. 36.
    Federal Communications Commission, “FCC first report and order: Revision of part 15 of the commissions’s rules regarding ultra-wideband transmission systems,” ET Docket No. 98-153, Apr. 2002.Google Scholar
  37. 37.
    K. S. Gilhousen, I. M. Jacobs, R. Padovani, A. J. Viterbi, L. A. Weaver Jr., and C. E. Wheatley III, “On the capacity of a cellular CDMA system,” IEEE Trans. Veh. Technol., vol. 40, pp. 303–312, May 1991.CrossRefGoogle Scholar
  38. 38.
    R. Menon, R. M. Buehrer, and J. H. Reed, “Outage probability based comparison of underlay and overlay spectrum sharing techniques,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 101–109, Nov. 2005.Google Scholar
  39. 39.
    J. Wang, “Narrowband interference suppression in time hopping impulse radio,” in Proc. 60th IEEE Veh. Technol. Conf., vol. 3, (Los Angeles, CA, USA), pp. 2138–2142, Sept. 2005.Google Scholar
  40. 40.
    C. Rose, S. Ulukus, and R. D. Yates, “Wireless systems and interference avoidance,” IEEE Trans. Wireless Commun., vol. 1, pp. 415–428, July 2002.CrossRefGoogle Scholar
  41. 41.
    S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Select. Areas Commun., vol. 23, pp. 201–220, Feb. 2005.CrossRefGoogle Scholar
  42. 42.
    U. Berthold and F. K. Jondral, “Guidelines for designing OFDM overlay systems,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 626–629, Nov. 2005.Google Scholar
  43. 43.
    T. Weiss, J. Hillenbrand, A. Krohn, and F. K. Jondral, “Mutual interference in OFDMbased spectrum pooling systems,” in Proc. 59th IEEE Veh. Technol. Conf. – Spring, vol. 4, (Milan, Italy), pp. 1873–1877, May 2004.Google Scholar
  44. 44.
    S. Kapoor and S. Nedic, “Interference suppression in DMT receivers using windowing,” in Proc. IEEE Int. Conf. Commun., vol. 2, (New Orleans, LA, USA), pp. 778–782, June 2000.Google Scholar
  45. 45.
    I. Cosvic, S. Brandes, and M. Schnell, “A technique for sidelobe suppression in OFDM systems,” in Proc. IEEE Global Telecommun. Conf., vol. 1, (St. Louis, MO, USA), pp. 204–208, Nov. 2005.Google Scholar
  46. 46.
    S. Brandes, I. Cosvic, and M. Schnell, “Sidelobe suppression in OFDM systems by insertion of cancellation carriers,” in Proc. 62nd IEEE Veh. Technol. Conf. – Fall, vol. 1, (Dallas, TX, USA), pp. 152–156, Sept. 2005.Google Scholar
  47. 47.
    F. Weidling, D. Datla, V. Petty, P. Krishnan, and G. J. Minden, “A framework for RF spectrum measurements and analysis,” in Proc. IEEE Int. Symp. New Frontiers Dynamic Spectr. Access Networks, vol. 1, (Baltimore, MD, USA), pp. 573–576, Nov. 2005.Google Scholar
  48. 48.
    S. B. Weinstein and P. M. Ebert, “Data transmission by frequency division multiplexing using the discretefourier transform,” IEEE Trans. Commun. Technol., vol. 19, pp. 628–634, Oct. 1971.CrossRefGoogle Scholar
  49. 49.
    J. D. Markel, “FFT Pruning,” IEEE Trans. Audio Electroacoust., vol. 19, pp. 305–311, Dec. 1971.CrossRefGoogle Scholar
  50. 50.
    R. G. Alves, P. L. Osorio, and M. N. S. Swamy, “General FFT pruning algorithm,” in Proc. 43rd IEEE Midwest Symp. Circuits and Systems, vol. 3, (Lansing, MI, USA), pp. 1192–1195, Aug. 2000.Google Scholar
  51. 51.
    S. Holm, “FFT pruning applied to time domain interpolation and peak localization,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 35, (Dallas, TX, USA), pp. 1776–1778, Dec. 1987.Google Scholar
  52. 52.
    Z. Hu and H. Wan, “A novel generic fast Fourier transform pruning technique and complexity analysis,” IEEE Trans. Signal Process., vol. 53, pp. 274–282, Jan. 2005.CrossRefMathSciNetGoogle Scholar
  53. 53.
    L. P. Jaroslavski, “Comments on ‘FFT algorithm for both input and output pruning’,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 29, (Atlanta, GA, USA), pp. 448–449, June 1981.Google Scholar
  54. 54.
    J. Schoukens, R. Pintelon, and H. Van Hamme, “The interpolated fast Fourier transform: A comparative study,” IEEE Trans. Instrum. Meas., vol. 41, pp. 226–232, Apr. 1992.CrossRefGoogle Scholar
  55. 55.
    D. P. Skinner, “Pruning the Decimation in time FFT algorithm,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 24, (Philadelphia, PA, USA), pp. 193–194, Apr. 1976.Google Scholar
  56. 56.
    T. V. Sreenivas and P. V. S. Rao, “High-resolution narrow band spectra by FFT pruning,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 28, (Denver, CO, USA), pp. 254–257, Apr. 1980.Google Scholar
  57. 57.
    T. V. Sreenivas and P. Rao, “FFT algorithm for both input and output pruning,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 27, (Washington, DC, USA), pp. 291–292, June 1979.Google Scholar
  58. 58.
    R. Rajbanshi, A. M. Wyglinski, and G. J. Minden, “An efficient implementation of NCOFDM transceivers for cognitive radios,” in Proc. 1st Int. Conf. on Cogn. Radio Oriented Wireless Networks and Commun. (Mykonos, Greece), June 2006.Google Scholar
  59. 59.
    J. G. Proakis, Digital Communications, 4th ed. New York, NY, USA: McGraw-Hill, 2001.Google Scholar
  60. 60.
    T. S. Rappaport, Wireless Communications: Principles and Practice. Upper Saddle River, NJ, USA: Prentice Hall, 1996.Google Scholar
  61. 61.
    K. S. Shanmugan and A. M. Breipohl, Random Signals: Detection, Estimation and Data Analysis. New York, NY, USA: Wiley, 1988.Google Scholar
  62. 62.
    C. Tellambura, “Computation of the continuous time PAR of an OFDMsignal with BPSK subcarriers,” IEEE Commun. Lett., vol. 5, pp. 185–187, May 2001.CrossRefGoogle Scholar
  63. 63.
    H. Yu and G. Wei, “Computation of the continuous time PAR of an OFDM signal,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 2003, (Hong Kong), pp. 529–531, Apr. 2003.Google Scholar
  64. 64.
    X. Li and L. J. Cimini, “Effects of clipping and filtering on the performance of OFDM,” IEEE Commun. Lett., vol. 2, pp. 131–133, May 1998.MATHCrossRefGoogle Scholar
  65. 65.
    L. Wang and C. Tellambura, “A simplified clipping and filtering technique for PAR reduction in OFDM systems,” IEEE Signal Process. Lett., vol. 12, pp. 453–456, June 2005.CrossRefGoogle Scholar
  66. 66.
    H. Ahn, Y. M. Shin, and S. Im, “A block coding scheme for peak to average power ratio reduction in an orthogonal frequency division multiplexing system,” in Proc. 51st IEEE Veh. Technol. Conf. – Spring, vol. 1, (Tokyo, Japan), pp. 56–60, May 2000.Google Scholar
  67. 67.
    A. Jones, T. Wilkinson, and S. Barton, “Block coding scheme for reduction of peak to mean envelope power ratio of multicarrier transmission schemes,” Electron. Lett., vol. 30, pp. 2098–2099, Dec. 1994.CrossRefGoogle Scholar
  68. 68.
    S. Sezginer and H. Sari, “Peak power reduction in OFDM systems using dynamic constellation shaping,” in Proc. 13th Eur. Signal Process. Conf. (Antalya, Turkey), Sept. 2005.Google Scholar
  69. 69.
    B. S. Krongold and D. L. Jones, “PAR reduction in OFDM via active constellation extension,” IEEE Trans. Broadcast., vol. 49, pp. 258–268, Sept. 2003.CrossRefGoogle Scholar
  70. 70.
    S. H. Han and J. H. Lee, “Peak-to-average power ratio reduction of an OFDM signal by signal set expansion,” in Proc. IEEE Int. Conf. Commun., vol. 2, (Paris, France), pp. 867–971, June 2004.Google Scholar
  71. 71.
    B. S. Krongold, “New techniques for multicarrier communication systems,” PhD Thesis, University of Illinois at Urbana-Champaign, Urbana, Illinois, 2003.Google Scholar
  72. 72.
    R. J. Baxley and G. T. Zhou, “Power savings analysis of peak-to-average power ratio reduction in OFDM,” IEEE Trans. Consumer Electron., vol. 50, pp. 792–798, Aug. 2004.CrossRefGoogle Scholar
  73. 73.
    J. Armstrong, “Peak-to-average power reduction for OFDM by repeated clipping and frequency domain filtering,” Electron. Lett., vol. 38, pp. 246–247, Feb. 2002.CrossRefGoogle Scholar
  74. 74.
    A. D. S. Jayalath and C. Tellambura, “The use of interleaving to reduce the peak to average power ratio of an OFDM signal,” in Proc. IEEE Global Telecommun. Conf., vol. 1, (San Francisco, CA, USA), pp. 82–86, Nov. 2000.Google Scholar
  75. 75.
    R. W. Bauml, R. F. H. Fischer, and J. B. Huber, “Reducing the peak to average power ratio of multicarrier modulation by selective mapping,” Electron. Lett., vol. 32, pp. 2056–2057, Oct. 1996.CrossRefGoogle Scholar
  76. 76.
    S. H. Muller and J. B. Huber, “OFDM with reduced peak to average power ratio by optimum combination of partial transmit sequences,” Electron. Lett., vol. 33, pp. 368–369, Feb. 1997.CrossRefGoogle Scholar
  77. 77.
    A. M. Wyglinski, F. Labeau, and P. Kabal, “Bit loading with ber-constraint for multicarrier systems,” IEEE Trans. Wireless Commun., vol. 4, pp. 1383–1387, July 2005.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Rakesh Rajbanshi
    • 1
  • Alexander M. Wyglinski
    • 1
  • Gary J. Minden
    • 1
  1. 1.The University of KansasUSA

Personalised recommendations