Cross-Layer Optimized Call Admission Control in Cognitive Radio Networks
- 218 Downloads
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.
Keywordscognitive radio call admission control spectrum sensing cross-layer optimization quality-of-service
The work in this paper is supported by Key Programs of NSFC with Grant No. U0635001, U0828003.
- 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
- 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
- 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
- 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.Timed markov models. http://www.mathpages.com/HOME/kmath589/kmath589.htm
- 16.Sherali HD, Adams WP (1999) A reformulation-linearization technique for solving discrete and continuous nonconvex problems, chapter 8. Kluwer, DordrechtGoogle Scholar
- 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