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Telecommunication Systems

, Volume 68, Issue 4, pp 657–668 | Cite as

An IEEE 802.22 transceiver framework and its performance analysis on software defined radio for TV white space

  • Abhijeet BishnuEmail author
  • Vimal Bhatia
Article

Abstract

With rapid increase in new applications and services, there is huge demand for internet bandwidth. Several researchers around the world have found that, majority of licensed bands (mostly terrestrial TV band) are either unused or underused. These underutilized bands allocated for TV transmission are known as TV white space (TVWS). For effective utilization of TVWS, the IEEE 802.22 is proposed. The IEEE 802.22 wireless regional area network (WRAN) is the latest standard for effective utilization of TV bands. This standard is based on orthogonal frequency division multiplexing with various modulation techniques to provide different data rates. In this paper, an implementation framework for physical layer of IEEE 802.22 WRAN standard for normal mode is demonstrated and analyzed. This transceiver is implemented using the National Instruments Laboratory Virtual Instrument Engineering Workbench programming software on the National Instruments universal software radio peripheral 2952R. We have also analyzed different blocks of IEEE 802.22 based on their execution time, and identify the critical blocks of IEEE 802.22 that should be optimized for real-time applications for commercial product development and field deployments. We have also highlighted the difference between theoretical and practical performance of the considered error control codes for IEEE 802.22 specified block size. Additionally, various covariance based spectrum sensing methods are also analyzed for real-world environment.

Keywords

TVWS OFDM IEEE 802.22 SDR Spectrum sensing 

Notes

Acknowledgements

The authors would like to thank IIT Indore and Ministry of Electronics and Information Technology (MeitY) for all the support.

References

  1. 1.
    Mishra, A. K., & Johnson, D. L. (2015). White space communication. Berlin: Springer.CrossRefGoogle Scholar
  2. 2.
    Regulatory Requirements for White Space Device in the UHF TV Band, OFCOM. Technical report, 2012.Google Scholar
  3. 3.
    Naik, G., Singhal, S., Kumar, A., & Karandikar, A. (2014). Quantitative assessment of TV white space in India. In Proceedings of IEEE national conference on communications, 2014 (pp. 1–6).Google Scholar
  4. 4.
    IEEE 802.22 Working Group on Wireless Regional Area Networks. Functional Requirements for the 802.22 WRAN, Doc: IEEE 802.22-05/0007r46. Technical report, 2005.Google Scholar
  5. 5.
    IEEE 802.22 Working Group on Wireless Regional Area Networks. Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in the TV Bands Amendment: Enhancement for Broadband Services and Monitoring Applications. Technical report, 2011.Google Scholar
  6. 6.
    IEEE 802.22 Working Group on Wireless Regional Area Networks. Standard for Spectrum Characterization and Occupancy Sensing. Technical report, 2014.Google Scholar
  7. 7.
    Stevenson, C. R., Chouinard, G., Lei, Z., Hu, W., Shellhammer, S. J., & Caldwell, W. (2009). IEEE 802.22: The first cognitive radio wireless regional area network standard. IEEE Communications Magazine, 47(1), 130–138.CrossRefGoogle Scholar
  8. 8.
    Ulversoy, T. (2010). Software defined radio: Challenges and opportunities. IEEE Communications Surveys & Tutorials, 12(4), 531–550.CrossRefGoogle Scholar
  9. 9.
    Jondral, F. K., Elsner, J., & Schwall, M. (2012). Software defined radio-guest editorial. Journal of Signal Processing Systems, 69(1), 1–3.CrossRefGoogle Scholar
  10. 10.
    Xiong, X., Xiang, W., Zheng, K., Shen, H., & Wei, X. (2015). An open source SDR-based NOMA system for 5G networks. IEEE Wireless Communications, 22(6), 24–32.CrossRefGoogle Scholar
  11. 11.
    Martian, A. (2017). Real-time spectrum sensing using software defined radio platforms. Telecommunication Systems, 64(4), 749–761.CrossRefGoogle Scholar
  12. 12.
    Gandhiraj, R., & Soman, K. P. (2014). Modern analog and digital communication systems development using GNU radio with USRP. Telecommunication Systems, 56(3), 367–381.CrossRefGoogle Scholar
  13. 13.
    Khurram, M., & Mirza, S. H. (2006). A general purpose processor based IEEE802. 11a compatible OFDM receiver design. In Proceedings of IEEE GCC, 2006 (pp. 1–5).Google Scholar
  14. 14.
    Zheng, K., Huang, L., Li, G., Cao, H., Wang, W., & Dohler, M. (2008). Beyond 3G evolution. IEEE Vehicular Technology Magazine, 3(2), 30–36.CrossRefGoogle Scholar
  15. 15.
    Wu, D., Eilert, J., & Liu, D. (2011). Implementation of a high-speed MIMO soft-output symbol detector for software defined radio. Journal of Signal Processing Systems, 63(1), 27–37.CrossRefGoogle Scholar
  16. 16.
    Demel, J., Koslowski, S., & Jondral, F. K. (2015). A LTE receiver framework using GNU radio. Journal of Signal Processing Systems, 78(3), 313–320.CrossRefGoogle Scholar
  17. 17.
    LabVIEW Communications 802.11 Application Framework 1.1 White Paper. Technical report, 2016.Google Scholar
  18. 18.
    Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13(2), 260–269.CrossRefGoogle Scholar
  19. 19.
    Gallager, R. (1962). Low-density parity-check codes. IRE Transactions on Information Theory, 8(1), 21–28.CrossRefGoogle Scholar
  20. 20.
    Cho, Y. S., Kim, J., Yang, W. Y., & Kang, C. G. (2010). MIMO-OFDM wireless communications with MATLAB. New York: Wiley.CrossRefGoogle Scholar
  21. 21.
    Wang, B., & Liu, K. R. (2011). Advances in cognitive radio networks: A survey. IEEE Journal of Selected Topics in Signal Processing, 5(1), 5–23.CrossRefGoogle Scholar
  22. 22.
    Zeng, Y., & Liang, Y. C. (2009). Spectrum-sensing algorithms for cognitive radio based on statistical covariances. IEEE Transactions on Vehicular Technology, 58(4), 1804–1815.CrossRefGoogle Scholar
  23. 23.
    Zeng, Y., Koh, C. L., & Liang, Y. C. (2008). Maximum eigenvalue detection: Theory and application. In Proceedings of IEEE international conference on communications (pp. 4160–4164).Google Scholar
  24. 24.
    Zeng, Y., & Liang, Y. C. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57(6), 1784–1793.CrossRefGoogle Scholar
  25. 25.
    Lin, F., Qiu, R. C., Hu, Z., Hou, S., Browning, J. P., & Wicks, M. C. (2012). Generalized FMD detection for spectrum sensing under low signal-to-noise ratio. IEEE Communications Letters, 16(5), 604–607.CrossRefGoogle Scholar
  26. 26.
    Atungire, P., Rahman, T. F., Granelli, F., & Sacchi, C. (2014). Open-field emulation of cooperative relaying in LTE-A downlink using the GNU radio platform. IEEE Network, 28(5), 20–26.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Signal and Software Group, Discipline of Electrical EngineeringIndian Institute of Technology IndoreIndoreIndia

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