Soft Combination Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networks

  • Shen, Bin (Graduate School of IT & Telecommunication, Inha University) ;
  • Kwak, Kyung-Sup (Graduate School of IT & Telecommunication, Inha University)
  • Received : 2008.09.09
  • Accepted : 2009.04.14
  • Published : 2009.06.30

Abstract

This paper investigates linear soft combination schemes for cooperative spectrum sensing in cognitive radio networks. We propose two weight-setting strategies under different basic optimality criteria to improve the overall sensing performance in the network. The corresponding optimal weights are derived, which are determined by the noise power levels and the received primary user signal energies of multiple cooperative secondary users in the network. However, to obtain the instantaneous measurement of these noise power levels and primary user signal energies with high accuracy is extremely challenging. It can even be infeasible in practical implementations under a low signal-to-noise ratio regime. We therefore propose reference data matrices to scavenge the indispensable information of primary user signal energies and noise power levels for setting the proposed combining weights adaptively by keeping records of the most recent spectrum observations. Analyses and simulation results demonstrate that the proposed linear soft combination schemes outperform the conventional maximal ratio combination and equal gain combination schemes and yield significant performance improvements in spectrum sensing.

Keywords

References

  1. S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications,” IEEE J. Selected Areas in Communications, vol. 23, no. 2, Feb. 2005, pp. 201-220.
  2. Q. Zhao and B.M. Sadler, “A Survey of Dynamic Spectrum Access: Signal Processing, Networking, and Regulatory Policy,” IEEE Signal Processing Magazine, vol. 24, no. 3, May 2007, pp. 79-89.
  3. J.J. Lehtomaki et al., “Spectrum Sensing with Forward Methods,”Proc. IEEE MILCOM, Oct. 2006, pp. 1-7.
  4. D. Cabric, S.M. Mishra, and R.W. Brodersen, “Implementation Issues in Spectrum Sensing for Cognitive Radios,” Proc. 38th Asilomar Conf. Signals Systems, Computers, 2004, pp. 772-776.
  5. R. Tandra and A. Sahai, “Fundamental Limits on Detection in Low SNR under Noise Uncertainty,” Proc. Int'l Conf. on Wireless Networks, Communications and Mobile Computing, June 2005, pp. 464-469.
  6. A. Sonnenschein and P.M. Fishman, “Radiometric Detection of Spread-Spectrum Signals in Noise of Uncertain Power,” IEEE Trans. Aerosp. Electron. Syst., vol. 28, no. 3, July 1992, pp. 654-660. https://doi.org/10.1109/7.256287
  7. A. Ghasemi and E.S. Sousa, “Impact of User Collaboration on the Performance of Sensing-Based Opportunistic Spectrum Access,” Proc. IEEE VTC Fall, Sept. 2006, pp. 1-6.
  8. S.M. Mishra, A. Sahai, and R.W. Brodersen, “Cooperative Sensing among Cognitive Radios,” Proc. IEEE ICC, vol. 4, June 2006, pp. 1658-1663.
  9. Z. Quan, S. Cui, and A.H. Sayed, “Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks,” IEEE J. Selected Topics in Signal Processing, vol. 2, no. 1, Feb. 2008, pp. 28-40.
  10. J. Ma and Y. Li, “Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks,” Proc. IEEE GLOBECOM, Nov. 2007, pp. 3139-3143.
  11. C.W. Helstronm, “Improved Multilevel Quantization for Detection of Narrowband Signals,” IEEE Trans. Aerosp. Electron. Syst., vol. 24, no. 2, Mar. 1988, pp. 142-147. https://doi.org/10.1109/7.1047
  12. J.G. Proakis, Digital Communications, Second Edition, McGraw-Hill Book Company, New York, 1989.
  13. H. Urkowitz, “Energy Detection of Unknown Deterministic Signals,” Proceedings of IEEE, vol. 55, Apr. 1967, pp. 523-231.
  14. H.V. Poor, An Introduction to Signal Detection and Estimation, Springer-Verlag, New York, 1994.