Advertisement

Wireless Networks

, Volume 25, Issue 8, pp 4991–4999 | Cite as

Study of polarization spectrum sensing based on stochastic resonance in partial polarized noise

  • Jin LuEmail author
  • Ming Huang
  • Jingjing Yang
Article
  • 21 Downloads

Abstract

Wireless communications is one of the most rapidly developing segments of the telecommunications industry. A large amount of intelligent terminals occupy the radio spectrum, resulting in the reduction of radio spectrum resources. Cognitive radio, which requires rapid and exact spectrum sensing, is considered the most effective method to resolve this problem. This paper applies bistable stochastic resonance to polarization antenna spectrum sensing. To adhere to its requirements, frequency re-scaling is adopted to degrade high frequency to low frequency. Differential computing is used to wipe the direction current component from the output signal. Three algorithms including differential energy detection, generalized likelihood ratio test, generalized Hadamard ratio test, are then employed for spectrum sensing. The simulation experiment compares the three algorithms above in various channel conditions including additional white Gaussian noise, Rayleigh-fading, and partial polarized noise. The results indicate that bistable stochastic resonance can drastically enhance detection probability in low signal-to-noise-ratio, and partial polarized noise degenerates the accuracy of spectrum sensing.

Keywords

Spectrum sensing Bistable stochastic resonance Polarized antenna Generalized likelihood ratio test 

Notes

Acknowledgements

This work was supported by Youth project of National Natural Science Foundation of China under Grant 61701432; the Spectrum Sensing and Borderlands Security Key Laboratory of Universities in Yunnan under Grant C6165903. Additionally, the authors would like to thank the editors and the anonymous reviewers for their constructive comments and helpful suggestions.

References

  1. 1.
    Wang, J., Jiang, C., Zhu, H., Yong, H., & Hanzo, L. (2016). Network association strategies for an energy harvesting aided super-WiFi network relying on measured solar activity. IEEE Journal on Selected Areas in Communications, PP, 1–1.CrossRefGoogle Scholar
  2. 2.
    Amjad, M., Rehmani, H., & Mao, S. (2018). Wireless multimedia cognitive radio networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, PP, 1–1.Google Scholar
  3. 3.
    Lu, Q. N., Yang, J. J., Jin, Z. Y., Chen, D. Z., & Huang, M. (2017). State of the art and challenges of radio spectrum monitoring in China. Radio Science, 52, 1261–1267.CrossRefGoogle Scholar
  4. 4.
    Lu, J., Huang, M., & Yang, J. J. (2017). A novel spectrum sensing method based on tri-stable stochastic resonance and quantum particle swarm optimization. Wireless Personal Communications, 95, 1–13.CrossRefGoogle Scholar
  5. 5.
    Atapattu, S., Tellambura, C., & Hai, J. (2011). Energy detection based cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 10, 1232–1241.CrossRefGoogle Scholar
  6. 6.
    Cacciapuoti, A. S., Caleffi, M., & Paura, L. (2011). Widely linear cooperative spectrum sensing for cognitive radio networks. In Global telecommunications conference.Google Scholar
  7. 7.
    Cacciapuoti, A. S., Caleffi, M., Izzo, D., & Paura, L. (2011). Cooperative spectrum sensing techniques with temporal dispersive reporting channels. IEEE Transactions on Wireless Communications, 10, 3392–3402.CrossRefGoogle Scholar
  8. 8.
    Atapattu, S., Tellambura, C., & Hai, J. (2009). Relay based cooperative spectrum sensing in cognitive radio networks. In Global telecommunications conference.Google Scholar
  9. 9.
    Xin, L., Min, J., Na, Z., Lu, W., & Feng, L. (2018). Multi-modal cooperative spectrum sensing based on Dempster–Shafer fusion in 5G-based cognitive radio. IEEE Access, 6, 199–208.CrossRefGoogle Scholar
  10. 10.
    Xin, L., Feng, L., & Na, Z. (2017). Optimal resource allocation in simultaneous cooperative spectrum sensing and energy harvesting for multichannel cognitive radio. IEEE Access, 5, 1–1.CrossRefGoogle Scholar
  11. 11.
    Xin, L., Zhang, X., Min, J., Fan, L., Lu, W., & Zhai, X. (2018). 5G-based green broadband communication system design with simultaneous wireless information and power transfer. Physical Communication, 28, 130–137.CrossRefGoogle Scholar
  12. 12.
    Wang, J., Ren, X., Zhang, S., Zhang, D., Li, H., & Li, S. (2014). Adaptive bistable stochastic resonance aided spectrum sensing. IEEE Transactions on Wireless Communications, 13, 4014–4024.CrossRefGoogle Scholar
  13. 13.
    He, D., Lin, Y., He, C., & Jiang, L. (2010). A novel spectrum-sensing technique in cognitive radio based on stochastic resonance. IEEE Transactions on Vehicular Technology, 59, 1680–1688.CrossRefGoogle Scholar
  14. 14.
    Li, Q., & Li, Z. (2013). A novel sequential spectrum sensing method in cognitive radio using suprathreshold stochastic resonance. IEEE Transactions on Vehicular Technology, 63, 1717–1725.Google Scholar
  15. 15.
    Guo, C., Wu, X., Feng, C., & Zeng, Z. (2013). Spectrum sensing for cognitive radios based on directional statistics of polarization vectors. IEEE Journal on Selected Areas in Communications, 31, 379–393.CrossRefGoogle Scholar
  16. 16.
    Guo, C., Li, H., & Chen, S. (2016). Study of spectrum sensing exploiting polarization: From optimal LRT to practical detectors. Digital Signal Processing, 49, 1–13.CrossRefGoogle Scholar
  17. 17.
    Sharma, S. K., Chatzinotas, S., & Ottersten, B. (2012). Exploiting polarization for spectrum sensing in cognitive SatComs. In International ICST conference on cognitive radio oriented wireless networks and communications, pp. 36–41.Google Scholar
  18. 18.
    Guo, C., Chen, S., & Liu, F. (2016). Polarization-based spectrum sensing algorithms for cognitive radios: Upper and practical bounds and experimental assessment. IEEE Transactions on Vehicular Technology, 65, 8072–8086.CrossRefGoogle Scholar
  19. 19.
    Jiao, H., Feng, L., Wang, K., Liu, N., & Yang, Z. (2017). Analysis of polarization noise in transmissive single-beam-splitter resonator optic gyro based on hollow-core photonic-crystal fiber. Optics Express, 25, 27806–27817.CrossRefGoogle Scholar
  20. 20.
    Xu, Y., & Lim, M. S. (2012). Spectrum sensing using dual polarized multiple antennas in cognitive radio systems. In Communications, pp. 914–917.Google Scholar
  21. 21.
    Jeruchim, M. C., Balaban, P., & Shanmugan, K. S. (2000). Simulation of communication systems: Modeling, methodology and techniques (pp. 1–907). New York: Kluwer Academic.Google Scholar
  22. 22.
    Tan, J., Chen, X., Wang, J., Chen, H., Cao, H., Zi, Y., et al. (2009). Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis. Mechanical Systems and Signal Processing, 23, 811–822.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYunnan UniversityKunmingChina

Personalised recommendations