Optimization of Handoff Latency Using Efficient Spectrum Sensing for CR System

  • Rohini S. Kale
  • J. B. Helonde
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)


Cognitive radio (CR) is a new technology in wireless communications. Presently, we are facing spectrum scarcity problem. CR gives solution to spectrum scarcity problem. The idea behind cognitive radio is the utilization of unused frequency bands of primary or licensed users (PU) by secondary or unlicensed users (SU). This unused frequency band is called white spaces or spectrum hole. This scenario needs the demand of cognitive radio. Spectrum sensing is the main task in CR. Proposed system has used energy detection method for spectrum sensing by using new threshold formulations. Novel algorithm for optimized handoff decision using fuzzy logic and artificial neural network and proactive strategy is used for channel allocation. The proposed system saves the power and optimized handoff delay.


Cognitive radio Threshold RSS Bit rate CPE Fuzzy logic ANN Handoff Idle to busy ratio SSC 


  1. 1.
    R.S. Kale, Dr. J.B. Helonde, Dr. V.M. Wadhai, “Efficient Spectrum Sensing in Cognitive Radio Using Energy Detection Method with New Threshold Formulation”, IEEE conference ICMICR 2013, 4–6 June 2013Google Scholar
  2. 2.
    Ivan Christian, Sangman Moh, Ilyong Chung, and Jinyi Lee, Chosun University, “Spectrum Mobility in Cognitive Radio Networks” IEEE Communications Magazine • June 2012Google Scholar
  3. 3.
    J. Eric Salt, Member, IEEE, and Ha H. Nguyen, Senior Member, IEEE, “Performance Prediction for Energy Detection of Unknown Signals”, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 57, NO. 6, NOVEMBER 2008Google Scholar
  4. 4.
    Shilian Zheng, Member, IEEE, Xiaoniu Yang, Shichuan Chen, and Caiyi Lou, “Target Channel Sequence Selection Scheme for Proactive-Decision Spectrum Handoff”, IEEE COMMUNICATIONS LETTERS, VOL. 15, NO. 12, DECEMBER 2011Google Scholar
  5. 5.
    Shunqing Zhang, Tianyu Wu, and Vincent K. N. Lau, “A Low-Overhead Energy Detection Based Cooperative Sensing Protocol for Cognitive Radio Systems” IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 8, NO. 11, NOVEMBER 2009 5575Google Scholar
  6. 6.
    Shukla, A.; “Cognitive Radio Technology: A Study for of com – Volume 1.” QINETIQ/06/00420 Issue 1.1, February 12th, 2007Google Scholar
  7. 7.
    FCC Spectrum Policy Task Force, November 2002, ET Docket No. 02 135, Washington DCGoogle Scholar
  8. 8.
    XG Working Group. The XG vision. Request for comments. Version 2.0. Technical report, BBN Technologies, 2005Google Scholar
  9. 9.
    Ayman A EI Saleh, “Optimizing Spectrum Sensing Parameters for Local and Co-operative Cognitive Radios” ISBN 978-89-5519-139-4 Feb. 15–18, ICACT 2009Google Scholar
  10. 10.
    Eric Salt, Member, IEEE, and Ha H. Nguyen, Senior Member, IEEE, “Performance Prediction for Energy Detection of Unknown Signals” IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 57, NO. 6, NOVEMBER 2008Google Scholar
  11. 11.
    Yonghong Zeng, Ying-Chang Liang, and Rui Zhang, “Blindly Combined Energy Detection for Spectrum Sensing in Cognitive Radio”, IEEE SIGNAL PROCESSING LETTERS, VOL. 15, 2008 649Google Scholar
  12. 12.
    C. Cordeiro, K. Challapali, D. Birru, S. Shankar, and IEEE 802.22: the first worldwide wireless standard based on cognitive radios, in Proc. IEEE DySPAN 2005, November 2005, pp. 328–337Google Scholar
  13. 13.
    Park, “Implementation Issues of Wide Band Multi Resolution Spectrum Sensing (MRSS) Technique for Cognitive Radio (CR) System”, IEEE 2006Google Scholar
  14. 14.
    E. Del Re, R. Fantacci and G. Giambene, ―A Dynamic channel Allocation Technique Based on Hopfield Neural Networks‖, IEEE Transactions on Vehicular Technology, Vol. VT-45, no. 1, pp. 26–32, 1995Google Scholar
  15. 15.
    S. Rajasekaran, G.A. Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic, and Genetic Algorithms”Google Scholar
  16. 16.
    R.S. Kale, Dr. J.B. Helonde, Dr. V.M. Wadhai, “New Algorithm for Handoff Optimization in Cognitive Radio Networks Using Fuzzy Logic and Artificial Neural Network” ERCICA 2013Google Scholar
  17. 17.
    L. Giupponi, Ana I. Perez-Neira, “Fuzzy-based Spectrum Handoff in Cognitive Radio Networks”, 2010Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.MAEERs MIT PolytechnicPuneIndia
  2. 2.ITM College of EngineeringNagpurIndia

Personalised recommendations