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Distributed Spectrum Sensing Using Low Cost Hardware


A distributed spectrum sensing network is prototyped using off the shelf hardware consisting of Raspberry Pi mini-computers and DVB-T receivers with software defined radio capabilities. Using the prototype network, coordinated, distributed wideband spectrum sensing is performed in a geographical area. The spectrum sensing data from the nodes is collected in a database. Well established low-complexity algorithms for distributed spectrum sensing are applied, and the results are compared against a professional spectrum sensing system. We show that with this simple low-cost setup, the decisions made on the availability of spectrum using the distributed sensing data correspond well with the decisions made on the reference data.

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  1. 1.

    Akyildiz, I.F., Lee, W.Y., Vuran, M.C., & Mohanty, S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Computer Networks, 50(13), 2127–2159. doi:10.1016/j.comnet.2006.05.001.

    Article  MATH  Google Scholar 

  2. 2.

    Akyildiz, I.F., Lo, B.F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: a survey. Physics Communication, 4(1), 40–62. doi:10.1016/j.phycom.2010.12.003.

    Article  Google Scholar 

  3. 3.

    Arcia-Moret, A., Pietrosemoli, E., & Zennaro, M. (2013). Whisppi: White space monitoring with Raspberry Pi. In Global Information Infrastructure Symposium pp. 1–6. doi:10.1109/GIIS.2013.6684374.

  4. 4.

    CRFS Ltd. (2014). RFeye Node. Data Sheet NOD-EYE0002, (Accessed 15.06.2014).

  5. 5.

    Digham, F.F., Alouini, M.S., & Simon, M.K. (2007). On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications, 55(1), 21–24.

    Article  Google Scholar 

  6. 6.

    Grönroos, S., Nybom, K., Björkqvist, J., Hallio, J., Auranen, J., & Ekman, R. (2014). Distributed spectrum sensing using low cost hardware. In Proceedings of the Wireless Innovation European Conference on Wireless Communications Technologies and Software Defined Radio (WInnComm-Europe 2014) (pp. 54–61).

  7. 7.

    Ma, J., Li, G., & Juang, B.H. (2009). Signal processing in cognitive radio. Proceedings of the IEEE, 97(5), 805–823. doi:10.1109/JPROC.2009.2015707.

    Article  Google Scholar 

  8. 8.

    MongoDB, Inc. (2014). mongoDB. (Accessed 13.06.14).

  9. 9.

    Nika, A., Zhang, Z., Zhou, X., Zhao, B.Y., & Zheng, H. (2014). Towards commoditized real-time spectrum monitoring. In Proceedings of the 1st ACM Workshop on Hot Topics in Wireless, HotWireless ’14. (pp. 25–30). New York, USA: ACM. doi:10.1145/2643614.2643615

  10. 10.

    Osmocom (2014). OsmocomSDR (Wiki). (Accessed 13.06.14).

  11. 11.

    Ready, M., Downey, M., & Corbalis, L. (1997). Automatic noise floor spectrum estimation in the presence of signals. In Signals, Systems amp Computers, 1997. Conference Record of the Thirty-First Asilomar Conference on, vol 1, (Vol. 1 pp. 877–881). doi:10.1109/ACSSC.1997.680569

  12. 12.

    Sequeira, S., Mahajan, R., & Spasojevic, P. (2012). On the noise power estimation in the presence of the signal for energy-based sensing. In Sarnoff Symposium (SARNOFF), 2012 35th IEEE, pp. 1–5. doi:10.1109/SARNOF.2012.6222753.

  13. 13.

    Wellens, M., Riihijarvi, J., Gordziel, M., & Mahonen, P. (2008). Evaluation of cooperative spectrum sensing based on large scale measurements. In New Frontiers in Dynamic Spectrum Access Networks, 2008. DySPAN 2008. 3rd IEEE Symposium on, pp 1–12. doi:10.1109/DYSPAN.2008.27.

  14. 14.

    Willkomm, D., & Wolisz, A. (2010). Is oversensitive spectrum sensing the door opener for initial cognitive radio deployments?. In Proceedings of the 2010 ACM Workshop on Wireless of the Students, by the Students, for the Students, S3 ’10, pp. 21–24. New York, USA: ACM. doi:10.1145/1860039.1860046.

  15. 15.

    Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials, 11(1), 116–130. doi:10.1109/SURV.2009.090109.

    Article  Google Scholar 

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Correspondence to Stefan Grönroos.

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Grönroos, S., Nybom, K., Björkqvist, J. et al. Distributed Spectrum Sensing Using Low Cost Hardware. J Sign Process Syst 83, 5–17 (2016).

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  • Distributed spectrum sensing
  • Software defined radio
  • Raspberry Pi