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


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.

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  1. 1.


  1. 1.

    Mishra, A. K., & Johnson, D. L. (2015). White space communication. Berlin: Springer.

    Google Scholar 

  2. 2.

    Regulatory Requirements for White Space Device in the UHF TV Band, OFCOM. Technical report, 2012.

  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).

  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.

  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.

  6. 6.

    IEEE 802.22 Working Group on Wireless Regional Area Networks. Standard for Spectrum Characterization and Occupancy Sensing. Technical report, 2014.

  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.

    Article  Google Scholar 

  8. 8.

    Ulversoy, T. (2010). Software defined radio: Challenges and opportunities. IEEE Communications Surveys & Tutorials, 12(4), 531–550.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  11. 11.

    Martian, A. (2017). Real-time spectrum sensing using software defined radio platforms. Telecommunication Systems, 64(4), 749–761.

    Article  Google 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.

    Article  Google 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).

  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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  17. 17.

    LabVIEW Communications 802.11 Application Framework 1.1 White Paper. Technical report, 2016.

  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.

    Article  Google Scholar 

  19. 19.

    Gallager, R. (1962). Low-density parity-check codes. IRE Transactions on Information Theory, 8(1), 21–28.

    Article  Google Scholar 

  20. 20.

    Cho, Y. S., Kim, J., Yang, W. Y., & Kang, C. G. (2010). MIMO-OFDM wireless communications with MATLAB. New York: Wiley.

    Google 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.

    Article  Google 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.

    Article  Google 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).

  24. 24.

    Zeng, Y., & Liang, Y. C. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57(6), 1784–1793.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

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

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Correspondence to Abhijeet Bishnu.

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This work is supported by Ministry of Electronics and Information Technology (MeitY), Govt. of India under Visvesvaraya Ph.D. Scheme Grant Nos. PhD-MLA/4(05)/2014 and 14(3)/2014-CC & BT.

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Bishnu, A., Bhatia, V. An IEEE 802.22 transceiver framework and its performance analysis on software defined radio for TV white space. Telecommun Syst 68, 657–668 (2018).

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  • TVWS
  • OFDM
  • IEEE 802.22
  • SDR
  • Spectrum sensing