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On Maximizing Blind Rendezvous Probability in Cognitive Radio Ad Hoc Networks

  • Aishwarya Sagar Anand UkeyEmail author
  • Meenu Chawla
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 546)

Abstract

Establishment of wireless communication links among the cognitive radio (CR) users via rendezvous on commonly available channels is the crucial process in cognitive radio ad hoc networks. Extant rendezvous methods utilize the channel hopping (CH) technique (also referred as blind rendezvous) and generate the CH sequence on the top of potential licensed channels and employ random replace operation to contrive the CH sequence for individual CR user. However, incorporation of random replace operation may exhibit poorer performance with the increase in the number of available channels owing to the severe decrease in the probability of replacing an unavailable channel with the same available channel by two or more CR users. In this paper, we present a channel-ranking rendezvous procedure that ranks available channels based on the received SINR and constructs the CH sequence by replacing the unavailable channels with higher ranked available channels. We verify the superiority of channel-ranking procedure through extensive simulation, and the simulation results confirm that utilization of channel-ranking mechanism severely maximizes the rendezvous occurrence probability as compared with the random replace operation.

Keywords

Cognitive radio ad hoc network Blind rendezvous Rendezvous probability Channel ranking 

References

  1. 1.
    Akyildiz IF, Lee WY, Vuran MC, Mohanty S (2006) Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput. Netw. 50(13):2127–2159CrossRefGoogle Scholar
  2. 2.
    Akyildiz IF, Lee WY, Chowdhury KR (2009) CRAHNs: cognitive radio ad hoc networks. Ad Hoc Netw. 7(5):810–836CrossRefGoogle Scholar
  3. 3.
    Chawla M, Ukey ASA, Reshma P (2017) Comprehensive asynchronous symmetric rendezvous algorithm in cognitive radio networks. Sadhana 42(11):1825–1834MathSciNetCrossRefGoogle Scholar
  4. 4.
    Liu H, Lin Z, Chu X, Leung YW (2012) Taxonomy and challenges of rendezvous algorithms in cognitive radio networks. In: 2012 international conference on computing, networking and communications (ICNC). IEEE Press, Maui, USA, pp 645–649Google Scholar
  5. 5.
    Joshi GP, Nam SY, Kim SW (2014) Rendezvous issues in ad hoc cognitive radio networks. KSII Trans. Internet Inform. Syst. 8(11):3655–3673Google Scholar
  6. 6.
    Htike Z, Hong CS, Lee S (2013) The life cycle of the rendezvous problem of cognitive radio ad hoc networks: a survey. J. Comput. Sci. Eng. 7(2):81–88CrossRefGoogle Scholar
  7. 7.
    Ukey ASA, Chawla M (2018) Rendezvous in cognitive radio ad hoc networks: a survey. Int. J. Ad Hoc Ubiquitous Comput. 29(4):233–254CrossRefGoogle Scholar
  8. 8.
    Liu H, Lin Z, Chu X, Leung YW (2012) Jump-stay rendezvous algorithm for cognitive radio networks. IEEE Trans. Parallel Distrib. Syst. 23(10):1867–1881CrossRefGoogle Scholar
  9. 9.
    Lin Z, Liu H, Chu X, Leung YW (2013) Enhanced jump-stay rendezvous algorithm for cognitive radio networks. IEEE Commun. Lett. 17(9):1742–1745CrossRefGoogle Scholar
  10. 10.
    Gandhi R, Wang CC, Hu YC (2012) Fast rendezvous for multiple clients for cognitive radios using coordinated channel hopping. In: 9th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks. IEEE Press, Seoul, South Korea, pp 434–442Google Scholar
  11. 11.
    Gu Z, Hua QS, Wang Y, Lau FC (2013) Nearly optimal asynchronous blind rendezvous algorithm for cognitive radio networks. IN: 2013 international conference on sensing, communications and networking (SECON). IEEE Press, New Orleans, LA, pp 371–379Google Scholar
  12. 12.
    Chuang I, Wu HY, Lee KR, Kuo YH (2013) Alternate hop-and-wait channel rendezvous method for cognitive radio networks. In: Proceedings IEEE INFOCOM’13. IEEE Press, Seoul, South Korea, pp 746–754Google Scholar
  13. 13.
    Chuang IH, Wu HY, Kuo YH (2014) A fast blind rendezvous method by alternate hop-and-wait channel hopping in cognitive radio networks. IEEE Trans. Mobile Comput. 13(10):2171–2184CrossRefGoogle Scholar
  14. 14.
    Yang B, Zheng M, Liang W (2016) A time-efficient rendezvous algorithm with a full rendezvous degree for heterogeneous cognitive radio networks. In: 35th annual IEEE international conference on computer communications. IEEE Press, San Francisco, CA, pp. 1–9Google Scholar
  15. 15.
    Qi X, Gao R, Liu L, Yang W (2017) ADFC-CH: adjusted disjoint finite cover rendezvous algorithms for cognitive radio networks. Wirel. Netw. 24(7):2621–2630CrossRefGoogle Scholar
  16. 16.
    Ohize H, Dlodlo M (2016) Ant colony system based control channel selection scheme for guaranteed rendezvous in cognitive radio ad-hoc network. In: 27th annual international symposium on personal, indoor, and mobile radio communications (PIMRC). IEEE Press, Valencia, Spain, pp 1–7Google Scholar
  17. 17.
    Stevenson CR, Chouinard G, Lei Z, Hu W, Shellhammer SJ, Caldwell W (2009) IEEE 802.22: the first cognitive radio wireless regional area network standard. IEEE Commun. Mag. 47(1):130–138CrossRefGoogle Scholar
  18. 18.
    Huang XL, Zhai YB, Wu J, Xu Y, Wu X, Wu X (2014) Channel quality ranking in cognitive radio networks. In: 10th international conference on wireless communications, networking and mobile computing. IEEE Press, Beijing, China, pp 191–194Google Scholar
  19. 19.
    Elderini T, Kaabouch N, Reyes H (2017) Channel quality estimation metrics in cognitive radio networks: a survey. IET Commun. 11(8):1173–1179CrossRefGoogle Scholar
  20. 20.
    Ohize H, Dlodlo M (2017) A channel hopping algorithm for guaranteed rendezvous in cognitive radio ad hoc networks using swarm intelligence. Wirel. Pers. Commun. 96(1):879–893CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMaulana Azad National Institute of TechnologyBhopalIndia

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