Design and Implementation of Cognitive Radio Sensor Network for Emergency Communication Using Discrete Wavelet Packet Transform Technique

  • Mariappan RamasamyEmail author
  • Rama Subramanian M.
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11319)


The Cognitive Radio network is one of the challenging field, where researchers are working to utilize the underutilized spectrum bands for the needful and emergency purposes. This paper proposes one such possible solution to design Cognitive Radio network with Universal Software Radio Peripheral (USRP) based Software Defined Radio (SDR). It explores the efficient implementation of Software Defined Radio (SDR) to sense and detect white spaces in the spectrum for the use of emergency communication during disaster using Discrete Wavelet Packet Transform (DWPT). The SDR testbed setup was simulated using MATLAB and observed its performance parameters and it performs better in terms of detected Power level, SNR and detection accuracy. The simulation results prove that this new devised approach has zero interference to the primary user frequency bands and hence not affecting the existing users. Hence, this paper concludes the Proof- of-Concept proposed with improved Quality of Service (QoS).


Cognitive radio Emergency communications Software defined radio (SDR) 


  1. 1.
    IEEE Standard Definitions and Concepts for Dynamic Spectrum Access: Terminology Relating to Emerging Wireless Networks, System Functionality, and Spectrum Management, IEEE Std. 1900.1-2008, September 2008Google Scholar
  2. 2.
    Mitola III, J.: Cognitive radio: an integrated agent architecture for software defined radio. Ph.D. dissertation, Royal Institute of Technology, Stockholm, Sweden, May 2000Google Scholar
  3. 3.
    Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)CrossRefGoogle Scholar
  4. 4.
    Haykin, S.: Cognitive Radio. IEEE J. Sel. Areas Commn. 23, 201–220 (2005)CrossRefGoogle Scholar
  5. 5.
    Vo, Q.D., Choi, J.-P., Chang, H.M., Lee, W.C.: Green perspective cognitive radio-based M2 M communications for smart meters. In: Proceedings of International Conference on Information and Communication Technology Convergence (ICTC), pp. 382–383, November 2010Google Scholar
  6. 6.
    Ko, C.-H., Huang, D.H., Wu, S.-H.: Cooperative spectrum sensing in TV white spaces: when cognitive radio meets cloud. In: Proceedings of IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 672–677, April 2011Google Scholar
  7. 7.
    Ge, F., et al.: Cognitive radio rides on the cloud. In: Proceedings of IEEE Military Communications Conference MILCOM, October– November 2010, pp. 1448–1453 (2010)Google Scholar
  8. 8.
    Abdulsattar, M.A., Hussein, Z.A.: Energy detection technique for spectrum sensing in cognitive radio: a survey. IJCNC 4, 223 (2012)CrossRefGoogle Scholar
  9. 9.
    Rawat, D.B., Yan, G.: Spectrum sensing methods and dynamic spectrum sharing in cognitive radio networks: a survey. IJRRWSN 1(1), 1–3 (2011)Google Scholar
  10. 10.
    Jiang, Z.-L., Zhang, Q.-Y., Wang, Y., Shang, X.-Q.: Wavelet packet entropy based spectrum sensing in cognitive radio. In: IEEE 3rd ICCSN, Italy, pp. 121–125 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Electronics and Communication EngineeringSri Venkateswara College of EngineeringTirupatiIndia
  2. 2.Madras Institute of TechnologyChennaiIndia

Personalised recommendations