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Wireless Personal Communications

, Volume 97, Issue 3, pp 3951–3977 | Cite as

Performance Evaluation of Cognitive Radio VoIP Users in Fading Environment

  • Ali Bayat
  • Mehdi MahdaviEmail author
  • Sonia Aïssa
Article
  • 119 Downloads

Abstract

In this paper we investigate three channel allocation algorithms for multi-channel cognitive radio networks: SNR-based allocation (SA), queue-based allocation (QA), and queue-based SNR-aware allocation (QSA). The efficiency of the proposed algorithms is investigated for VoIP cognitive radio users (CRs). The resource allocation in the SA and QA schemes is performed based on the channel quality status seen by each CR and its queue length, respectively. The channel allocation in the QSA scheme is based on both the queue length and the SNR status. The mean packet loss rate (PLR) as the main quality-of-service (QoS) parameter is scrutinized for each algorithm. Novel analytical structures together with new analyses are developed, by which the level of provisioning QoS of VoIP traffic in each of the proposed algorithms can be evaluated. Numerical results and comparisons are also presented. In particular, the QSA algorithm is shown to present the best QoS level and yield the least PLR among the three allocation schemes.

Keywords

Cognitive radio networks Resource allocation Opportunistic scheduling QoS analysis VoIP services 

References

  1. 1.
    Spectrum policy task force. Rep. OET Docket No. 02-135 (2002)Google Scholar
  2. 2.
    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.CrossRefGoogle Scholar
  3. 3.
    Alshamrani, A., Shen, X., & Xie, L. L. (2011). QoS provisioning for heterogeneous services in cooperative cognitive radio networks. IEEE Journal on Selected Areas in Communications, 29(4), 819–830. doi: 10.1109/JSAC.2011.110413.CrossRefGoogle Scholar
  4. 4.
    Cai, L., Liu, Y., Shen, X., Mark, J. W., & Zhao, D. (2010). Distributed QoS-aware MAC for multimedia over cognitive radio networks. In Proceedings of IEEE GLOBECOM (pp. 1–5 ). doi: 10.1109/GLOCOM.2010.5683743.
  5. 5.
    Doost-Mohammady, R., Naderi, M., & Chowdhury, K. (2014). Spectrum allocation and QoS provisioning framework for cognitive radio with heterogeneous service classes. IEEE Transactions on Wireless Communications, 13(7), 3938–3950. doi: 10.1109/TWC.2014.2319307.CrossRefGoogle Scholar
  6. 6.
    Feng, S., Liang, Z., & Zhao, D. (2010). Providing telemedicine services in an infrastructure-based cognitive radio network. IEEE Wireless Communications, 17(1), 96–103. doi: 10.1109/MWC.2010.5416356.CrossRefGoogle Scholar
  7. 7.
    Feng, S., & Zhao, D. (2010). Supporting real-time CBR traffic in a cognitive radio sensor network. In Proceedings of IEEE WCN (pp. 1–6). doi: 10.1109/WCNC.2010.5506276.
  8. 8.
    Gross, D., & Harris, C. M. (1985). Fundamentals of queueing theory (2nd ed.). New York: Wiley.zbMATHGoogle Scholar
  9. 9.
    Gunawardena, S., & Zhuang, W. (2010). Voice capacity of cognitive radio networks for both centralized and distributed channel access control. In Proceedings of IEEE GLOBECOM (pp. 1–5). doi: 10.1109/GLOCOM.2010.5683073.
  10. 10.
    Homayounzadeh, A., & Mahdavi, M. (2015). Quality of service provisioning for real-time traffic in cognitive radio networks. IEEE Communications Letters, 19(3), 467–470. doi: 10.1109/LCOMM.2014.2386313.CrossRefGoogle Scholar
  11. 11.
    Homayounzadeh, A., & Mahdavi, M. (2016). Improving voice-service support in cognitive radio networks. ETRI Journal, 38(3), 444–454.Google Scholar
  12. 12.
    Jha, S., Phuyal, U., Rashid, M., & Bhargava, V. (2011). Design of OMC-MAC: An opportunistic multi-channel MAC with QoS provisioning for distributed cognitive radio networks. IEEE Transactions on Wireless Communications, 10(10), 3414–3425. doi: 10.1109/TWC.2011.072511.102196.CrossRefGoogle Scholar
  13. 13.
    Le, L. B., & Hossain, E. (2008). Resource allocation for spectrum underlay in cognitive radio networks. IEEE Transactions on Wireless Communications, 7(12), 5306–5315. doi: 10.1109/T-WC.2008.070890.CrossRefGoogle Scholar
  14. 14.
    Liang, Z., & Zhao, D. (2010). Quality of service performance of a cognitive radio sensor network. In Proceedings of IEEE ICC (pp. 1–5). doi: 10.1109/ICC.2010.5502787.
  15. 15.
    Lien, S. Y., Lin, Y. Y., & Chen, K. C. (2011). Cognitive and game-theoretical radio resource management for autonomous femtocells with QoS guarantees. IEEE Transactions on Wireless Communications, 10(7), 2196–2206. doi: 10.1109/TWC.2011.060711.100737.CrossRefGoogle Scholar
  16. 16.
    Liu, Q., Zhou, S., & Giannakis, G. (2005). Queuing with adaptive modulation and coding over wireless links: Cross-layer analysis and design. IEEE Transactions on Wireless Communications, 4(3), 1142–1153. doi: 10.1109/TWC.2005.847005.CrossRefGoogle Scholar
  17. 17.
    Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications Magazine, 6(4), 13–18. doi: 10.1109/98.788210.CrossRefGoogle Scholar
  18. 18.
    Rashid, M., Hossain, M., Hossain, E., & Bhargava, V. (2009). Opportunistic spectrum scheduling for multiuser cognitive radio: A queueing analysis. IEEE Transactions on Wireless Communications, 8(10), 5259–5269. doi: 10.1109/TWC.2009.081536.CrossRefGoogle Scholar
  19. 19.
    Su, H., & Zhang, X. (2008). Cross-layer based opportunistic MAC protocols for QoS provisionings over cognitive radio wireless networks. IEEE Journal on Selected Areas in Communications, 26(1), 118–129. doi: 10.1109/JSAC.2008.080111.CrossRefGoogle Scholar
  20. 20.
    Tan, X., Zhang, H., Chen, Q., & Hu, J. (2014). Opportunistic channel selection based on time series prediction in cognitive radio networks. Transactions on Emerging Telecommunications Technologies, 25(11), 1126–1136. doi: 10.1002/ett.2664.CrossRefGoogle Scholar
  21. 21.
    Tumuluru, V., Wang, P., & Niyato, D. (2011). A novel spectrum-scheduling scheme for multichannel cognitive radio network and performance analysis. IEEE Transactions on Vehicular Technology, 60(4), 1849–1858. doi: 10.1109/TVT.2011.2114682.CrossRefGoogle Scholar
  22. 22.
    Wang, B., Zhao, D., & Cai, J. (2011). Joint connection admission control and packet scheduling in a cognitive radio network with spectrum underlay. IEEE Transactions on Wireless Communications, 10(11), 3852–3863. doi: 10.1109/TWC.2011.091411.110023.CrossRefGoogle Scholar
  23. 23.
    Wang, H. S., & Moayeri, N. (1995). Finite-state Markov channel—A useful model for radio communication channels. IEEE Transactions on Vehicular Technology, 44(1), 163–171. doi: 10.1109/25.350282.CrossRefGoogle Scholar
  24. 24.
    Wang, P., Niyato, D., & Jiang, H. (2010). Voice-service capacity analysis for cognitive radio networks. IEEE Transactions on Vehicular Technology, 59(4), 1779–1790. doi: 10.1109/TVT.2010.2041017.CrossRefGoogle Scholar
  25. 25.
    Yao, Y., Popescu, A., & Popescu, A. (2014). On prioritized opportunistic spectrum access in cognitive radio cellular networks. Transactions on Emerging Telecommunications Technologies,. doi: 10.1002/ett.2866.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringIsfahan University of TechnologyIsfahanIran
  2. 2.Institut National de la Recherche Scientifique-Energy, Materials and Telecommunications Center (INRS-EMT)University of QuebecMontrealCanada

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