Advertisement

Mobile Networks and Applications

, Volume 15, Issue 5, pp 610–626 | Cite as

Cross-Layer Optimized Call Admission Control in Cognitive Radio Networks

  • Rong Yu
  • Yan Zhang
  • Ming Huang
  • Shengli Xie
Article

Abstract

In Cognitive Radio (CR) networks, Call Admission Control (CAC) is a key enabling technique to ensure Quality-of-Service (QoS) provisioning for Secondary Users (SUs). CAC decisions are usually made based on the current traffic volume in the system. However, in CR networks, the system state of channel utilization can only be partially observed through spectrum sensing. The presence of sensing error may mislead the CAC strategy to make an inefficient or even incorrect decision. To achieve QoS provisioning in CR networks, a practical CAC strategy should have in-built functionality to deal with the inaccuracy of sensing results. This paper is motivated to construct a cross-layer optimization framework, in which the parameters of CAC strategy and spectrum sensing scheme are simultaneously tuned to minimize the dropping rate while satisfying the requirements of both blocking rate and interference threshold. After introducing a multiple-stair Markov model to approximate the non-memoryless state transitions, the cross-layer optimization is modelled as a non-linear programming problem. The method of branch-and-bound is employed to solve the problem, where five components are involved: problem selection, reformulation linear technique, simplex method, local search and sub-problem generation. Extensive simulations are carried out to evaluate the proposed CAC strategy. The simulation results show that our CAC strategy significantly outperforms two traditional strategies. The dropping rate in our strategy is considerably reduced. Meanwhile, the blocking rate and the interference probability strictly coincide with the constraints.

Keywords

cognitive radio call admission control spectrum sensing cross-layer optimization quality-of-service 

Notes

Acknowledgements

The work in this paper is supported by Key Programs of NSFC with Grant No. U0635001, U0828003.

References

  1. 1.
    Akyildiz IF, Lee WY, Vuran MC, Mohantly S (2006) Next generation/dynamic spectrum access/cognitive radio wireless network: a survey. Comput Networks 50(13):2127–2159MATHCrossRefGoogle Scholar
  2. 2.
    Mitola J, Maguire GQ (1999) Cognitive radio: making software radios more personal. IEEE Pers Commun 6(4):13–18CrossRefGoogle Scholar
  3. 3.
    Ramjee R, Nagarajan R, Towsley D (1997) On optimal call admission control in cellular network. Wirel Netw (Springer) 3(1):29–41CrossRefGoogle Scholar
  4. 4.
    Falowo OE, Chan HA (2007) Adaptive bandwidth management and joint call admission control to enhance system utilization and QoS in heterogeneous wireless networks. EURASIP J Wirel Commun NetwGoogle Scholar
  5. 5.
    Yu F, Krishnamurthy V, Leung VCM (2006) Cross-layer optimal connection admission control for variable bit rate multimedia traffic in packet. IEEE Trans Signal Process 54(2):542–555CrossRefGoogle Scholar
  6. 6.
    Zhu X, Shen L (2007) Analysis of cognitive radio spectrum access with optimal channel reservation. IEEE Commun Lett 11(4):304–306CrossRefMathSciNetGoogle Scholar
  7. 7.
    Zhang L, Liang YC, Xin Y (2007) Joint admission control and power allocation for cognitive radio networks. In: ICASSP 2007, vol 3, pp 673–676Google Scholar
  8. 8.
    Kim H, Shin KG (2007) Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. IEEE Trans Mob Comput 7:533–545CrossRefGoogle Scholar
  9. 9.
    Lee WY, Akyildiz IF (2008) Optimal spectrum sensing framework for cognitive radio networks. IEEE Trans Wirel Commun 7(10):3845–3857CrossRefGoogle Scholar
  10. 10.
    Jia J (2008) HC-MAC: a hardware-constrained cognitive MAC for efficient spectrum management. IEEE J Sel Areas Commun 26(1):106–117CrossRefGoogle Scholar
  11. 11.
    Ganesan G (2007) Cooperative spectrum sensing in cognitive radio. Part I: two user networks. IEEE Trans Wirel Commun 6(6):2204–2213CrossRefGoogle Scholar
  12. 12.
    Liu Y, Yu R, Xie S (2008) Optimal cooperative sensing scheme under time-varying channel for cognitive radio networks. In: IEEE dyspan’08Google Scholar
  13. 13.
    Valaee S, Li B (2004) Distributed call admission control for ad hoc networks. IEEE Trans Wirel Commun 11(4):6–14CrossRefGoogle Scholar
  14. 14.
    Zamat H, Natarajan B (2008) Use of dedicated broadband sensing receiver in cognitive radio. In: IEEE international conference on communications workshops, 2008. ICC workshops’08Google Scholar
  15. 15.
  16. 16.
    Sherali HD, Adams WP (1999) A reformulation-linearization technique for solving discrete and continuous nonconvex problems, chapter 8. Kluwer, DordrechtGoogle Scholar
  17. 17.
    Digham F, Alouini M, Simon M (2007) On the energy detection of unknown signals over fading channels. IEEE Trans Commun 55(1):21–24CrossRefGoogle Scholar
  18. 18.
    Tewfik AH, Kim M (1994) Fast positive definite linear system solvers. IEEE Trans Signal Process 42(3):572–582CrossRefGoogle Scholar
  19. 19.
    Orlin JB (1996) A polynomial time primal network simplex algorithm for minimum cost flows. In: Proceedings of the seventh annual ACM-SIAM symposium on discrete algorithms, pp 474–481Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Simula Research LaboratoryFornebuNorway

Personalised recommendations