Towards Cognitive Networking: Automatic Wireless Network Recognition Based on MAC Feature Detection

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 116)

Abstract

A cognitive radio device must be able to discover and recognize wireless networks eventually present in the surrounding environment. This chapter presents a recognition method based on MAC sub-layer features. Based on the fact that every wireless technology has its own specific MAC sub-layer behaviour, as defined by the technology Standard, network recognition can be reached by exploiting this particular behaviour. From the packet exchange pattern, peculiar of a single technology, MAC features can be extracted, and later they can be used for automatic recognition. The advantage of these “high-level” features, instead of physical ones, resides in the simplicity of the method: only a simple energy detector and low-complexity algorithms are required. In this chapter automatic recognition based on MAC features is applied at three cases of wireless networks operating in the ISM 2.4 GHz band: Bluetooth, Wi-Fi and ZigBee. Furthermore, this idea is extended to underlay networks such as Ultra Wide Band networks. A study-case is also presented that provides an illustration of automatic classification between Wi-Fi and Bluetooth networks.

References

  1. 1.
    Mitola J III, Maguire GQ Jr (1999) Cognitive radio: making software radios more personal. IEEE Pers Commun 6(4):13–18. doi:10.1109/98.788210 CrossRefGoogle Scholar
  2. 2.
    IEEE Standard for Information Technology-Telecommunications and Information Exchange Between Systems-Local and Metropolitan Area Networks-Specific Requirements-Part 15.1: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Wireless Personal Area Networks (WPANs), IEEE Std 802.15.1-2005 (Revision of IEEE Std 802.15.1-2002), pp 0_1-580, 2005. doi: 10.1109/IEEESTD.2005.96290
  3. 3.
    IEEE Standard for Information Technology-Telecommunications and Information Exchange Between Systems-Local and Metropolitan Area Networks-Specific Requirements-Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Std 802.11-2007 (Revision of IEEE Std 802.11-1999), pp C1-1184, June 12 2007. doi: 10.1109/IEEESTD.2007.373646
  4. 4.
    IEEE Standard for Information Technology-Telecommunications and Information Exchange Between Systems-Local and Metropolitan Area Networks-Specific Requirements Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs), IEEE Std 802.15.4-2006 (Revision of IEEE Std 802.15.4-2003), pp 0_1-305, 2006. doi: 10.1109/IEEESTD.2006.232110
  5. 5.
    Haykin S, Thomson DJ, Reed JH (2009) Spectrum sensing for cognitive radio. Proc IEEE 97(5):849–877. doi:10.1109/JPROC.2009.2015711 CrossRefGoogle Scholar
  6. 6.
    Ghasemi A, Sousa ES (2008) Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs. IEEE Commun Mag 46(4):32–39. doi:10.1109/MCOM.2008.4481338 CrossRefGoogle Scholar
  7. 7.
    Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutor 11(1):116–130, First quarter 2009. doi: 10.1109/SURV.2009.090109 Google Scholar
  8. 8.
    Cabric D, Mishra SM, Brodersen RW (2004) Implementation issues in spectrum sensing for cognitive radios. In: Conference record of the thirty-eighth Asilomar conference on signals, systems and computers, vol 1, 7–10 Nov 2004, pp 772–776. doi: 10.1109/ACSSC.2004.1399240
  9. 9.
    Liang Y-C, Zeng Y, Peh ECY, Hoang AT (2008) Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans Wireless Commun 7(4):1326–1337. doi:10.1109/TWC.2008.060869 CrossRefGoogle Scholar
  10. 10.
    Lee W-Y, Akyildiz IF (2008) Optimal spectrum sensing framework for cognitive radio networks. IEEE Trans Wireless Commun 7(10):3845–3857. doi: 10.1109/T-WC.2008.070391 CrossRefGoogle Scholar
  11. 11.
    Zeng Yonghong, Liang Ying-Chang (2009) Spectrum-sensing algorithms for cognitive radio based on statistical covariances. IEEE Trans Veh Technol 58(4):1804–1815. doi:10.1109/TVT.2008.2005267 CrossRefGoogle Scholar
  12. 12.
    Chen Z, Guo N, Qiu RC (2010) Demonstration of real-time spectrum sensing for cognitive radio. In: Military communications conference, MILCOM 2010, Oct 31 2010–Nov 3 2010, pp 323–328. doi: 10.1109/MILCOM.2010.5680333
  13. 13.
    Zeng Y, Liang Y (2009) Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Trans Commun 57(6):1784–1793. doi:10.1109/TCOMM.2009.06.070402 CrossRefGoogle Scholar
  14. 14.
    Do T, Mark BL (2010) Joint spatial—temporal spectrum sensing for cognitive radio networks. IEEE Trans Vehicular Technol 59(7):3480–3490. doi:10.1109/TVT.2010.2050610 CrossRefGoogle Scholar
  15. 15.
    Filo M, Hossain A, Biswas AR, Piesiewicz R (2009) Cognitive pilot channel: Enabler for radio systems coexistence. In: Second international workshop on cognitive radio and advanced spectrum management, (CogART 2009), pp 17–23. doi: 10.1109/COGART.2009.5167226
  16. 16.
    Ishizu K, Murakami H, Harada H (2011) Feasibility study on spectrum sharing type cognitive radio system with outband pilot channel. In: 2011 sixth international ICST conference on cognitive radio oriented wireless networks and communications (CROWNCOM), pp 286–290Google Scholar
  17. 17.
    Di Benedetto M-G, Boldrini S, Martin Martin CJ, Roldan Diaz J (2010) Automatic network recognition by feature extraction: a case study in the ISM band. In: 2010 Proceedings of the fifth international conference on cognitive radio oriented wireless networks and communications (CROWNCOM), pp 1–5, 9–11. doi: 10.4108/ICST.CROWNCOM2010.9274
  18. 18.
    Zhuan Y, Memik G, Grosspietsch J (2008) Energy detection using estimated noise variance for spectrum sensing in cognitive radio networks. In: Proceedings of IEEE wireless communications and networking conference, WCNC 2008, March 31 2008–April 3 2008, pp 711–716. doi: 10.1109/WCNC.2008.131
  19. 19.
    Di Benedetto M-G, Giancola G (2004) Understanding ultra wide band radio fundamentals, 1st edn. Prentice Hall PTR, Englewood Cliffs. ISBN: 0-13-148003-0Google Scholar
  20. 20.
    Francone M, Domenicali D, Di Benedetto M-G (2006) Time-varying interference spectral analysis for Cognitive UWB networks. In: 32nd annual conference on IEEE industrial electronics, IECON 2006, 6–10 Nov 2006, pp 3205–3210. doi: 10.1109/IECON.2006.348076
  21. 21.
    Boldrini S, Ferrante GC, Di Benedetto M-G (2011) UWB network recognition based on impulsiveness of energy profiles. In: 2011 IEEE international conference on ultra-wideband (ICUWB), 14–16 Sept 2011, pp 327–330. doi: 10.1109/ICUWB.2011.6058856
  22. 22.
    Benco S, Boldrini S, Ghittino A, Annese S, Di Benedetto M-G (2010) Identification of packet exchange patterns based on energy detection: the Bluetooth case. In: 2010 3rd international symposium on applied sciences in biomedical and communication technologies (ISABEL),7–10 Nov 2010, pp 1–5. doi: 10.1109/ISABEL.2010.5702776
  23. 23.
    Theodoridis S, Koutroumbas K (2009) Pattern recognition, 4th edn. Elsevier Academic Press, New York. ISBN: 978-1-59749-272-0Google Scholar
  24. 24.
    Gallant SI (1990) Perceptron-based learning algorithms. IEEE Trans Neural Netw 1(2):179–191. doi:10.1109/72.80230 MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Maria-Gabriella Di Benedetto
    • 1
  • Stefano Boldrini
    • 1
  1. 1.DIET DepartmentSpaienza University of RomeRomeItaly

Personalised recommendations