Advertisement

Telecommunication Systems

, Volume 64, Issue 4, pp 749–761 | Cite as

Real-time spectrum sensing using software defined radio platforms

  • Alexandru MartianEmail author
Article

Abstract

As the lack of frequency resources became a critical problem in recent years, solutions had to be found in order to use the available spectrum in a more efficient way. Cognitive radio (CR) technology is a possible answer to this, by proposing a dynamic spectrum access approach, which allows unlicensed users to access unused licensed frequency bands. In order to detect the presence of licensed users, any CR equipment has to perform a spectrum sensing process. This paper presents a practical solution for building this essential part of a frequency agile device. The implementation of the proposed real-time spectrum sensing solution is based on commercial software defined radio platforms. Hardware and software details regarding the described prototype are given, together with aspects related to the optimal configuration of the used platforms for such an application. Moreover, the performance that can be obtained using the proposed solution is evaluated through measurements performed in several different scenarios.

Keywords

Spectrum sensing Energy detection Cognitive radio Software defined radio USRP 

Notes

Acknowledgments

The work has been funded by the Romanian Space Agency, Competition C2-2013, under RDI Grant No.240/2013 “ADANSPACE” and by the Sectoral Operational Programme Human Resources Development 2007–2013 of the Ministry of European Funds through the Financial Agreement POSDRU/159/1.5/S/132395.

References

  1. 1.
    Lopez-Benitez, M., Umbert, A., & Casadevall, F. (2009). Evaluation of spectrum occpancy in spain for cognitive radio applications. In Proceedings of the IEEE 69th Vehicular Technology Conference (VTC 2009 Spring), Barcelona, April.Google Scholar
  2. 2.
    Angueira, P., Fadda, M., Morgade, J., Murroni, M., & Popescu, V. (2015). Field measurements for practical unlicensed communication in the UHF band. In Telecommunication Systems, January.Google Scholar
  3. 3.
    Marţian, A. (2014). Evaluation of spectrum occupancy in urban and rural environments of Romania. In Revue Roumaine des Sciences Techniques—Serie Electrotechnique et Energetique, 59(1), 87–96.Google Scholar
  4. 4.
    Mitola, J., III. (1999). Cognitive radio for flexible mobile multimedia communications. In Proceedings of the IEEE International Workshop on Mobile Multimedia (pp. 3–10).Google Scholar
  5. 5.
    Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116–130.CrossRefGoogle Scholar
  6. 6.
    Ettus Research USRP product family. Retrieved November 2015, from https://www.ettus.com/product.
  7. 7.
    GNU Radio Software Development Toolkit. Retrieved November 2015, from http://gnuradio.org/redmine/projects/gnuradio.
  8. 8.
    Martian, A. (2014). Real-time spectrum sensor based on USRP. In Proceedings of the 10th International Conference on Communications COMM2014, Bucharest, Romania (pp. 429–432).Google Scholar
  9. 9.
    Cabric, D., Mishra, S. M., & Brodersen, R. W. (2004). Implementation issues in spectrum sensing. In Asilomar Conference on Signal, Systems and Computers.Google Scholar
  10. 10.
    Mishra, S. M., Cabric, D., Chang, C., Willkomm, D., van Schewick, Wolisz, A., & Brodersen, R. W. (2005). A real time cognitive radio testbed for physical and link layer experiments. In 2005 First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), 8–11 November (pp. 562–567).Google Scholar
  11. 11.
    Chen, Zhe, Guo, Nan, & Qiu, R. C. (2010). Demonstration of real-time spectrum sensing for cognitive radio. IEEE Communications Letters, 14(10), 915–917.CrossRefGoogle Scholar
  12. 12.
    Liu, W., Yaron, O., Moerman, I., Bouckaert, S., Jooris, B., & Demeester, P. (2011). Real-time wide-band spectrum sensing for cognitive radio. In Proceedings of the 2011 18th IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT), 22–23 November (pp. 1–6).Google Scholar
  13. 13.
    Nguyen, T. T., Dang, K. L., Nguyen, H. V., & Nguyen, P. H. (2013). A real-time FPGA implementation of spectrum sensing applying for DVB-T primary signal. In Proceedings of the 2013 International Conference o Advanced Technologies for Communications (ATC), 16–18 October (pp. 164–169).Google Scholar
  14. 14.
    Nafkha, A., Naoues, M., Cichon, K., & Kliks, A. (2014). Experimental spectrum sensing measurements using USRP Software Radio platform and GNU-radio. In Proceedings of the 2014 9th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), 2–4 June (pp. 429–434).Google Scholar
  15. 15.
    Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531.CrossRefGoogle Scholar
  16. 16.
    López-Benítez, M., & Casadevall, F. (2012). Improved energy detection spectrum sensing for cognitive radio. Special issue on cognitive communications. IET Communications, 6(8), 785–796.CrossRefGoogle Scholar
  17. 17.
    Ettus Research USRP N210 networked series platform. Retrieved November 2015, from http://www.ettus.com/product/details/UN210-KIT.
  18. 18.
    Ettus Research X310 X series platform. Retrieved November 2015, from http://www.ettus.com/product/details/X310-KIT.
  19. 19.
    Sirio SD-3000N Wideband Discone Antenna. Retrieved July 2016, from http://www.sirioantenne.it/vhf-base/sd-3000-u-n.
  20. 20.
    Ettus Research WBX RF daughterboard. Retrieved November 2015, from https://www.ettus.com/product/details/WBX.
  21. 21.
    Ettus Research CBX 120 MHz RF daughterboard. Retrieved November 2015, from http://www.ettus.com/product/details/CBX120.
  22. 22.
    USRP Hardware Driver (UHD). Retrieved November 2015, from http://code.ettus.com/redmine/ettus/projects/uhd/wiki.
  23. 23.
    Mathworks MATLAB Environment. Retrieved November 2015, from http://www.mathworks.com/products/matlab.
  24. 24.
    Fastest Fourier Transform in the West (FFTW). Retrieved November 2015, from http://www.fftw.org.
  25. 25.
    Numerical Python package (NumPy). Retrieved November 2015, from http://numpy.scipy.org.
  26. 26.
    Marţian, A., Vlădeanu, C., Marcu, I., & Marghescu, I. (2010). Evaluation of spectrum occupancy in an urban environment in a cognitive radio context. International Journal on Advances in Telecommunications, 3(3&4), 172–181.Google Scholar
  27. 27.
    IEEE 802.11h, Amendment to Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Spectrum and Transmit Power Management extensions in the 5 GHz band in Europe. IEEE Std 802.11h-2003, October 2003.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Telecommunications DepartmentUniversity Politehnica of BucharestBucharestRomania

Personalised recommendations