Delay in Cognitive Radio Networks

  • Yaling Yang
  • Chuan Han
  • Bo Gao


This chapter presents analysis for delays for both multihop cognitive radio networks and single-hop cognitive radio networks. For multihop cognitive radio networks, we analyze the amount of time that a packet spends to travel over the intermittent relaying links over multiple relaying hops and characterize it with the metric called information propagation speed. Optimal relaying node placement strategies are derived to maximize information propagation speed. For single-hop cognitive radio networks, we will analyze how delay is affected by multiple cognitive radio design options, including the number of channels to be aggregated, the duration of transmission, the channel separation constraint on channel aggregation, and the time needed for spectrum sensing and protocol handshake. How these different options may affect the delay under different secondary and primary user traffic loads is revealed. Methods for computing optimal cognitive radio design and operation strategy are derived.


Primary User Relay Node Secondary User Cognitive Radio Network Primary User Activity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by the US National Science Foundation under grant CNS-0831865 and the Institute for Critical Technology and Applied Science (ICTAS) of Virginia Tech.


  1. 1.
    Y. Xu and W. Wang, “The speed of information propagation in large wireless networks,” in Proc. of IEEE INFOCOM, Phoenix, AZ, 2008.Google Scholar
  2. 2.
    R. Zheng, “Information dissemination in power-constrained wireless networks,” in Proc. of IEEE INFOCOM, Barcelona, Catalunya, Spain, 2006.Google Scholar
  3. 3.
    P. Jacquet, B. Mans, P. Muhlethaler, and G. Rodolakis, “Opportunistic routing in wireless ad hoc networks: Upper bounds for the packet propagation speed,” in Proc. of IEEE MASS, Atlanta, Georgia, USA, 2008.Google Scholar
  4. 4.
    P. Jacquet, B. Mans, and G. Rodolakis, “Information propagation speed in mobile and delay tolerant networks,” in Proc. of IEEE INFOCOM, Rio de Janeiro, Brazil, 2009.Google Scholar
  5. 5.
    D. Willkomm, S. Machiraju, J. Bolot, and A. Wolisz, “Primary user behavior in cellular networks and implications for dynamic spectrum access,” IEEE Communications Magazine, vol. 47, pp. 88–95, 2009.CrossRefGoogle Scholar
  6. 6.
    P. Popovski, H. Yomo, K. Nishimori, R. D. Taranto, and R. Prasad, “Opportunistic interference cancellation in cognitive radio systems,” in Proc. of IEEE DySPAN, Dublin, Ireland, 2007.Google Scholar
  7. 7.
    R. Zhang and Y.-C. Liang, “Exploiting hidden power-feedback loops for cognitive radio,” in Proc. of IEEE DySPAN, Chicago, Illinois, 2008.Google Scholar
  8. 8.
    G. Zhao, G. Y. Li, and C. Yang, “Proactive detection of spectrum opportunities in primary systems with power control,” IEEE Transactions on Wireless Communications, vol. 8, pp. 4815–4823, 2009.CrossRefGoogle Scholar
  9. 9.
    G. Bolch, S. Greiner, H. de Meer, K. S. Trivedi, H. de Meer, and K. S. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation With Computer Science Applications. Wiley-Interscience, Wiley, 2006.MATHCrossRefGoogle Scholar
  10. 10.
    C. Han and Y. Yang, “Information propagation speed in cognitive radio networks: Network and flow analysis,” Virginia Tech, Tech. Rep., 2010.Google Scholar
  11. 11.
    R. Chandra, R. Mahajan, T. Moscibroda, R. Raghavendra, and P. Bahl, “A Case for Adapting Channel Width in Wireless Networks,” in Proc. ACM SIGCOMM’08, Seattle, WA, Aug. 2008.Google Scholar
  12. 12.
    IEEE 802.22 WG, “IEEE P802.22/D0.1 Draft Standard for Wireless Regional Area Networks Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in TV Bands,” IEEE Standard, May 2006.Google Scholar
  13. 13.
    S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications,” IEEE Journal on Selected Areas in Communications, Vol.23, No.2, pp. 201–220, Feb. 2005.CrossRefGoogle Scholar
  14. 14.
    H. Kim, and K. G. Shin, “Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks,” IEEE Transactions on Mobile Computing, Vol.7, No.5, pp. 533–545, May 2008.MathSciNetCrossRefGoogle Scholar
  15. 15.
    F. Huang, W. Wang, H. Luo, G. Yu, and Z. Zhang, “Prediction-Based Spectrum Aggregation with Hardware Limitation in Cognitive Radio Networks,” in Proc. IEEE VTC’10-Spring, Taipei, Taiwan, May 2010.Google Scholar
  16. 16.
    P. Bahl, A. Adya, J. Padhye, and A. Wolman, “Reconsidering Wireless Systems with Multiple Radios,” ACM SIGCOMM Comp. Comm. Rev., Vol.34, No.5, pp. 39–46, Oct. 2004.CrossRefGoogle Scholar
  17. 17.
    Y. Yuan, P. Bahl, R. Chandra, P. Chou, J. Ferrell, T. Moscibroda, S. Narlanka, and Y. Wu, “KNOWS: Kognitiv Networking Over White Spaces,” in Proc. IEEE DySPAN’07, Dublin, Ireland, Apr. 2007.Google Scholar
  18. 18.
    Y. Yuan, P. Bahl, R. Chandra, T. Moscibroda, and Y. Wu, “Allocating Dynamic Time-Spectrum Blocks in Cognitive Radio Networks,” in Proc. ACM MobiHoc’07, Montreal, QC, Canada, Sep. 2007.Google Scholar
  19. 19.
    J. Lee, and J. So, “Analysis of Cognitive Radio Networks with Channel Aggregation,” in Proc. IEEE WCNC’10, Sydney, Australia, Apr. 2010.Google Scholar
  20. 20.
    D. Xu, E. Jung, and X. Liu, “Optimal Bandwidth Selection in Multi-Channel Cognitive Radio Networks: How Much Is Too Much?,” in Proc. IEEE DySPAN’08, Chicago, Illinois, Oct. 2008.Google Scholar
  21. 21.
    T. Shu, and M. Krunz, “Throughput-Efficient Sequential Channel Sensing and Probing in Cognitive Radio Networks under Sensing Errors,” in Proc. ACM MobiCom’09, Beijing, China, Sep. 2009.Google Scholar
  22. 22.
    G. Bianchi, “Performance Analysis of IEEE 802.11 Distributed Coordination Function,” IEEE Journal on Selected Areas in Communications, Vol.18, No.3, pp. 535–547, Mar. 2000.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Virginia Polytechnic Institute and State UniversityBlacksburgUSA

Personalised recommendations