Telecommunication Systems

, Volume 66, Issue 4, pp 689–699 | Cite as

Performance analysis of cognitive radio networks for secondary users with slotted central control



This paper deals with analysis, performance evaluation and optimization of cognitive radio networks with central controller. The main principle of this technology is that secondary users are enabled to make opportunistic use of the spectrum part, which is actually unused by the primary users. The considered network enables heterogeneous slotted structure for the channel, in which the secondary user’s packets are transmitted on a slot basis, while the primary user’s packets are forwarded in super-slots, i.e. in fixed length slot-blocks. This heterogeneous slotted channel structure enables more flexible operation leading to more realistic system model of cognitive radio network. We model the cognitive radio networks by preemptive priority queueing model with two classes of customers. We solve the model by applying Markov chain technique and derive the steady-state distributions of the number of primary user’s packets and secondary user’s packets in the system. We provide the formulas for several performance measures including the interruption rate, loss rate, throughput, and average latency of secondary users. After validating the analysis by simulation the influence of the secondary user’s buffer capacity on various system performance measures is investigated. In the last part of the paper we address the question of optimal design of secondary user’s buffer capacity.


Cognitive radio networks Centralized spectrum allocation scheme Secondary users Queueing model Preemptive priority Markov chain Simulation Optimal design 



This work was supported in part by National Natural Science Foundation (No. 61472342), Hebei Province Science Foundation (Nos. F2017203141, F2016501073), China and was supported in part by MEXT, Japan.


  1. 1.
    Al-Mahdi, H., Kalil, M., Liers, F., & Mitschele-Thiel, A. (2009). Increasing spectrum capacity for ad hoc networks using cognitive radios: An analytical model. IEEE Communications Letter, 13(9), 676–678.CrossRefGoogle Scholar
  2. 2.
    Bae, Y. H., Alfa, A. S., & Choi, B. D. (2010). Performance analysis of modified IEEE 802.11-based cognitive radio networks. IEEE Communications Letters, 14(10), 975–977.CrossRefGoogle Scholar
  3. 3.
    Gállego, J. R., Hernández, Á., Guío, I., & Valdovinos, A. (2010). Performance evaluation of nonsynchronized initial random access for mobile broadband systems. Telecommunication Systems, 43(3–4), 279–294.CrossRefGoogle Scholar
  4. 4.
    Hoang, A. T., Wong, D. T. C., & Liang, Y. C. (2009). Design and analysis for an 802.11-based cognitive radio network. In Proceedings of IEEE Wireless Communications and Networking Conference, Budapest, Hungary (pp. 1–6).Google Scholar
  5. 5.
    Jha, S. C., Rashid, M. M., Bhargava, V. K., & Despins, C. (2011). Medium access control in distributed cognitive radio networks. IEEE Wireless Communications, 18(4), 41–45.CrossRefGoogle Scholar
  6. 6.
    Kamoun, F. (2009). Performance evaluation of a queuing system with correlated packet-trains and server interruption. Telecommunication Systems, 41(4), 267–277.CrossRefGoogle Scholar
  7. 7.
    Kaur, P., Khosla, A., & Uddin, M. (2011). Markovian queuing model for dynamic spectrum allocation in centralized architecture for cognitive radios. IACSIT International Journal of Engineering and Technology, 3(1), 96–101.CrossRefGoogle Scholar
  8. 8.
    Kim, K. (2012). T-preemptive priority queue and its application to the analysis of an opportunistic spectrum access in cognitive radio networks. Computers & Operations Research, 39(7), 1394–1401.CrossRefGoogle Scholar
  9. 9.
    Marinho, J., & Monteiro, E. (2012). Cognitive radio: Survey on communication protocols, spectrum decision issues, and future research directions. Wireless Networks, 18(2), 147–164.CrossRefGoogle Scholar
  10. 10.
    Mchenry, M. (2003). Spectrum white space measurements. Washington, DC: Presentation to New America Foundation Broadband Forum.Google Scholar
  11. 11.
    Ren, P., Wang, Y., Du, Q., & Xu, J. (2012). A survey on dynamic spectrum access protocols for distributed cognitive wireless networks. EURASIP Journal on Wireless Communications and Networking, 2012, 60. doi: 10.1186/1687-1499-2012-60.CrossRefGoogle Scholar
  12. 12.
    Sarker, J. H., & Mouftah, H. T. (2013). A self-optimized random access protocol for an infrastructure-less mission critical wireless networking system. Telecommunication Systems, 52(4), 2133–2144.CrossRefGoogle Scholar
  13. 13.
    Takagi, H. (1993). Queueing analysis, volume 3: Discrete-time systems. North-Holland, Amsterdam.Google Scholar
  14. 14.
    Usui, M., Niki, H., & Kohno, T. (1994). Adaptive Gauss–Seidel method for linear systems. International Journal of Computer Mathematics, 51(1–2), 119–125.CrossRefGoogle Scholar
  15. 15.
    Vassaki, S., Panagopoulos, A. D., & Constantinou, P. (2013). Evaluation of channel dependent bandwidth allocation in wireless access networks: Centralized and distributed approach. Telecommunication Systems, 52(4), 2003–2013.CrossRefGoogle Scholar
  16. 16.
    Yue, W., & Matsumoto, Y. (2000). Output and delay of multi-channel slotted ALOHA systems for integrated voice and data transmission. Telecommunication Systems, 13(2–4), 147–165.CrossRefGoogle Scholar
  17. 17.
    Zhang, Y., & Leung, C. (2009). Cross-layer resource allocation for real-time services in OFDM-based cognitive radio systems. Telecommunication Systems, 42(1–2), 97–108.CrossRefGoogle Scholar
  18. 18.
    Zhu, D. B., & Choi, B. D. (2012). Performance analysis of CSMA in an unslotted cognitive radio network with licensed channels and unlicensed channels. EURASIP Journal on Wireless Communications and Networking, 2012, 12. doi: 10.1186/1687-1499-2012-12.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.Key Laboratory for Computer Virtual Technology and System Integration of Hebei ProvinceYanshan UniversityQinhuangdaoChina
  3. 3.Department of Intelligence and InformaticsKonan UniversityKobeJapan

Personalised recommendations