Advertisement

SpectraMap: Efficiently Constructing a Spatio-temporal RF Spectrum Occupancy Map

  • Aditya AhujaEmail author
  • Vinay J. Ribeiro
  • Ranveer Chandra
  • Amit Kumar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10340)

Abstract

The RF spectrum is typically monitored from a single, or few, vantage points. A larger spatio-temporal view of spectrum occupancy, such as over a few weeks on a city-wide scale, would be beneficial for several applications, for example, spectrum inventory by regulators or spectrum monitoring by wireless carriers. However, achieving such a view requires a dense deployment of spectrum analyzers, both in space and time, which is prohibitively expensive.

In this paper, we present a novel efficient approach to obtain an accurate extrapolated spatio-temporal view of spectrum occupancy. Our method uses RSSI measurements alone and does not require a-priori information of terrain, transmitter location, transmit power or path-loss model. We present our method as an algorithmic framework, called SpectraMap, which through targeted deployment of both static and mobile spectrum analyzers, gives a view of the spectrum occupancy over both time and space. We contrast SpectraMap’s accuracy with that of Kriging (an accepted well performing method of RSSI spatial extrapolation) through simulations and present RSSI map construction savings achieved through actual deployment on a large university campus. Finally, we draw a theoretical distinction between SpectraMap and relevant contemporary solutions in the fields of space-time RSSI maps and spectrum management.

Keywords

Dynamic spectrum access Network measurements Algorithms 

Notes

Acknowledgements

This work was partially supported by project RP02565 titled “SPARC: Spectrum Aware Rural Connectivity” at IIT Delhi funded by the Ministry of Electronics and Information Technology, Government of India. The authors would also like to thank Himanshu Varshney and Sanoj Kumar for contributing to the spectrum measurement setup.

References

  1. 1.
    Darpa strategic technology office radiomap program. http://www.darpa.mil/Our_Work/STO/Programs/Advanced_RF_Mapping_(Radio_Map).aspx
  2. 2.
    The microsoft spectrum observatory. https://observatory.microsoftspectrum.com
  3. 3.
    Radio environmental maps (rems): A cognitive tool for environmental awareness. http://www-syscom.univ-mlv.fr/~najim/gdr-ecoradio/sayrac.pdf
  4. 4.
    The university of washington spectrum observatory. http://specobs.ee.washington.edu
  5. 5.
    The radio-spectrum inventory act (2010). https://www.govtrack.us/congress/bills/111/hr3125/text
  6. 6.
  7. 7.
    Achtzehn, A., et al.: Improving coverage prediction for primary multi-transmitter networks operating in the TV whitespaces. In: 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pp. 623–631. IEEE (2012)Google Scholar
  8. 8.
    Baykas, T., et al.: Overview of TV white spaces: current regulations, standards and coexistence between secondary users. In: 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops (PIMRC Workshops), pp. 38–43. IEEE (2010)Google Scholar
  9. 9.
    Elbassioni, K., Fishkin, A.V., Mustafa, N.H., Sitters, R.: Approximation algorithms for euclidean group TSP. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1115–1126. Springer, Heidelberg (2005). doi: 10.1007/11523468_90 CrossRefGoogle Scholar
  10. 10.
    Fette, B.A.: Cognitive radio technology. Academic Press, New York (2009)Google Scholar
  11. 11.
    Gaeddert, J., et al.: Radio environment map enabled situation-aware cognitive radio learning algorithms. In: Software Defined Radio Forum (SDRF) Technical Conference (2006)Google Scholar
  12. 12.
    Hong, S.S., Katti, S.R.: DOF: a local wireless information plane. In: ACM SIGCOMM Computer Communication Review, vol. 41, pp. 230–241. ACM (2011)Google Scholar
  13. 13.
    Lunnamo, S.: Radio Environment Maps: Capabilities and challengesGoogle Scholar
  14. 14.
    Nekovee, M.: A survey of cognitive radio access to TV white spaces. In: 2009 International Conference on Ultra Modern Telecommunications and Workshops, pp. 1–8. IEEE (2009)Google Scholar
  15. 15.
    Vogel, C., et al.: An Analysis of a Low-Complexity Received Signal Strength Indicator for Wireless Applications (2004)Google Scholar
  16. 16.
    Wei, Z., Zhang, Q., Feng, Z., Li, W., Gulliver, T.A.: On the construction of radio environment maps for cognitive radio networks. In: 2013 IEEE Wireless Communications and Networking Conference (WCNC), pp. 4504–4509. IEEE (2013)Google Scholar
  17. 17.
    Wellens, M., Riihijärvi, J., Mähönen, P.: Spatial statistics and models of spectrum use. Comput. Commun. 32(18), 1998–2011 (2009)CrossRefGoogle Scholar
  18. 18.
    Ying, X., et al.: Revisiting TV coverage estimation with measurement-based statistical interpolation. In: 2015 7th International Conference on Communication Systems and Networks (COMSNETS), pp. 1–8. IEEE (2015)Google Scholar
  19. 19.
    Zhang, T., et al.: A vehicle-based measurement framework for enhancing whitespace spectrum databases. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, pp. 17–28. ACM (2014)Google Scholar
  20. 20.
    Zhao, Y., Reed, J.: Radio Environment Map Design and Exploitation. MPRG Technical (2005)Google Scholar
  21. 21.
    Zhao, Y.: Enabling cognitive radios through radio environment maps (2007)Google Scholar
  22. 22.
    Zhao, Y., et al.: Applying radio environment maps to cognitive wireless regional area networks. In: 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 115–118. IEEE (2007)Google Scholar
  23. 23.
    Zhao, Y., et al.: Development of radio environment map enabled case-and knowledge-based learning algorithms for IEEE 802.22 WRAN cognitive engines. In: 2007 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, pp. 44–49. IEEE (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aditya Ahuja
    • 1
    Email author
  • Vinay J. Ribeiro
    • 1
  • Ranveer Chandra
    • 2
  • Amit Kumar
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology DelhiNew DelhiIndia
  2. 2.Microsoft ResearchRedmondUSA

Personalised recommendations