Wireless Personal Communications

, Volume 72, Issue 4, pp 2849–2865 | Cite as

CogNS: A Simulation Framework for Cognitive Radio Networks

  • Vahid EsmaeelzadehEmail author
  • Reza Berangi
  • Seyyed Mohammad Sebt
  • Elahe Sadat Hosseini
  • Moein Parsinia


Cognitive radio technology has been used to efficiently utilize the spectrum in wireless networks. Although many research studies have been done recently in the area of cognitive radio networks (CRNs), little effort has been made to propose a simulation framework for CRNs. In this paper, a simulation framework based on NS2 (CogNS) for cognitive radio networks is proposed. This framework can be used to investigate and evaluate the impact of lower layers, i.e., MAC and physical layer, on the transport and network layers protocols. Due to the importance of packet drop probability, end-to-end delay and throughput as QoS requirements in real-time reliable applications, these metrics are evaluated over CRNs through CogNS framework. Our simulations demonstrate that the design of new network and transport layer protocols over CRNs should be considered based on CR-related parameters such as activity model of primary users, sensing time and frequency.


Cognitive radio networks (CRN) Simulation framework  Network Simulator 2 (NS2) Performance evaluation 



We thank Iran Telecommunication Research Center (ITRC) for supporting this research (


  1. 1.
    Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127–2159.CrossRefzbMATHGoogle Scholar
  2. 2.
    Mitola, J., I., & Maguire, G. Q., Jr. (1999). Cognitive radio: Making software radios more personal. Personal Communications, IEEE, 6(4), 13–18.Google Scholar
  3. 3.
    Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.CrossRefGoogle Scholar
  4. 4.
    Marinho, J., & Monteiro, E. (2011). Cognitive radio: Survey on communication protocols, spectrum decision issues, and future research directions. Wireless Networks, 18(2), 147–164.CrossRefGoogle Scholar
  5. 5.
    Akyildiz, I. F., Lee, W.-Y., & Chowdhury, K. R. (2009). CRAHNs: Cognitive radio ad hoc networks. Ad Hoc Networks, 7(5), 810–836.CrossRefGoogle Scholar
  6. 6.
    Akan, O. B., Karli, O. B., & Ergul, O. (2009). Cognitive radio sensor networks. IEEE Network, 23(4), 34–40.Google Scholar
  7. 7.
    Cormio, C., & Chowdhury, K. R. (2009). A survey on MAC protocols for cognitive radio networks. Ad Hoc Networks, 7(7), 1315–1329.CrossRefGoogle Scholar
  8. 8.
    Cesana, M., Cuomo, F., & Ekici, E. (2011). Routing in cognitive radio networks: Challenges and solutions. Ad Hoc Networks, 9(3), 228–248.CrossRefGoogle Scholar
  9. 9.
    Issariyakul, T., Pillutla, L. S., & Krishnamurthy, V. (2009). Tuning radio resource in an overlay cognitive radio network for TCP: Greed isnt good. IEEE Communications Magazine, 47(7), 57–63.CrossRefGoogle Scholar
  10. 10.
    Calvo, R. A., & Campo, J. P. (2007). Adding multiple interface support in NS-2. Cantabria: University of Cantabria.Google Scholar
  11. 11.
    Wang, B. NS2 Notebook: Multi-channel Multi-interface Simulation in NS2 (2.29).
  12. 12.
    Chiueh, T. C., Raniwala, A., Krishnan, R., & Gopalan, K. Hyacinth: An IEEE 802.11-based multi-channel wireless mesh network.
  13. 13.
    Cognitive radio cognitive network simulator.
  14. 14.
    Di Felice, M., Chowdhury, K. R., Kim, W., Kassler, A., & Bononi, L. (2011). End-to-end protocols for cognitive radio ad hoc networks: An evaluation study. Performance Evaluation, 68(9), 859–875.CrossRefGoogle Scholar
  15. 15.
    Network simulator version 2.
  16. 16.
    Slingerland, A. M. R., Pawelczak, P., Prasad, R. V., Lo, A., & Hekmat, R. (2007). Performance of transport control protocol over dynamic spectrum access links. In New frontiers in dynamic spectrum access networks, 2007. DySPAN 2007. 2nd IEEE international symposium on, 2007 (pp. 486–495).Google Scholar
  17. 17.
    Kondareddy, Y. R., & Agrawal, P. (2009). Effect of dynamic spectrum access on transport control protocol performance. In Global telecommunications conference, 2009 (GLOBECOM 2009). IEEE, 2009, (pp. 1–6).Google Scholar
  18. 18.
    Chowdhury, K. R., Di Felice, M., & Akyildiz, I. F. (2009). TP-CRAHN: A transport protocol for cognitive radio ad-hoc networks. In INFOCOM 2009, IEEE, 2009 (pp. 2482–2490).Google Scholar
  19. 19.
    Sarkar, D., & Narayan, H. (2010). Transport layer protocols for cognitive networks. In INFOCOM IEEE conference on computer communications workshops, 2010 (pp. 1–6).Google Scholar
  20. 20.
    Bicen, A. O., & Akan, O. B. (2011). Reliability and congestion control in cognitive radio sensor networks. Ad Hoc Networks, 9(7), 1154–1164.CrossRefGoogle Scholar
  21. 21.
    Lee, W.-Y., & Akyildiz, I. F. (2008). Optimal spectrum sensing framework for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(10), 3845–3857.CrossRefGoogle Scholar
  22. 22.
    Bolch, G., Greiner, S., de Meer, H., Trivedi, K. S., & Trivedi, K. S. (1998). Queueing networks and Markov Chains: Modeling and performance evaluation with computer science applications. New York: Wiley.CrossRefzbMATHGoogle Scholar
  23. 23.
    Tang, S., & Mark, B. L. (2009). Modeling and analysis of opportunistic spectrum sharing with unreliable spectrum sensing. IEEE Transactions on Wireless Communications, 8(4), 1934–1943.CrossRefGoogle Scholar
  24. 24.
    Proakis, J. G. (2001). Digital communications (4th ed.). New York: McGraw-Hill.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Vahid Esmaeelzadeh
    • 1
    Email author
  • Reza Berangi
    • 1
  • Seyyed Mohammad Sebt
    • 1
  • Elahe Sadat Hosseini
    • 1
  • Moein Parsinia
    • 1
  1. 1.Wireless Networks Laboratory, Department of Computer EngineeringIran University of Science and TechnologyTehranIran

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