Abstract
Dropping probability of handoff calls and blocking probability of new calls are two important QoS measures for cellular networks. Call admission policies, such as fractional guard channel and uniform fractional guard channel policies are used to maintain the pre-specified level of QoS. In this paper, we propose a learning automata based call admission policy in which a learning automaton is used to accept/reject new calls. This call admission policy can be considered as adaptive uniform fractional guard channel policy. In order to study the performance of the proposed call admission policy, the computer simulations are conducted. The simulation results show that for some range of input traffics, the performance of the proposed approach is close to the performance of the uniform fractional guard channel policy. The proposed policy is fully adaptive and doesn’t require any information about the input traffics.
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References
R. Ramjee, D. Towsley, and R. Nagarajan, “On Optimal Call Admission Control in Cellular Networks,” Wireless Networks, vol. 3, pp. 29–41, 1997.
D. Hong and S. Rappaport, “Traffic Modelling and Performance Analysis for Cellular Mobile Radio Telephone Systems with Priotrized and Nonpriotorized Handoffs Procedure,” IEEE Transactions on Vehicular Technology, vol. 35, pp. 77–92, Aug. 1986.
G. Haring, R. Marie, R. Puigjaner, and K. Trivedi, “Loss Formulas and Their Application to Optimization for Cellular Networks,” IEEE Transactions on Vehicular Technology, vol. 50, pp. 664–673, May 2001.
H. Beigy and M. R. Meybodi, “Uniform Fractional Guard Channel,” in Proceedings of Sixth World Multiconference on Systemmics, Cybernetics and Informatics, Orlando, USA, July 2002.
P. R. Srikantakumar and K. S. Narendra, “A Learning Model for Routing in Telephone Networks,” SIAM Journal of Control and Optimization, vol. 20, pp. 34–57, Jan. 1982.
O. V. Nedzelnitsky and K. S. Narendra, “Nonstationary Models of Learning Automata Routing in Data Communication Networks,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-17, pp. 1004–1015, Nov. 1987.
B. J. Oommen and E. V. de St. Croix, “Graph Partitioning Using Learning Automata,” IEEE Transactions on Commputers, vol. 45, pp. 195–208, Feb. 1996.
H. Beigy and M. R. Meybodi, “An Algorithm Based on Learning Automata for Determining the Minimum Number of Hidden Units in Three Layers Neural Networks,” Amirkabir Journal of Science and Technology, vol. 12, no. 46, pp. 111–136, 2001.
M. R. Meybodi and H. Beigy, “Neural Network Engineering Using Learning Automata: Determining of Desired Size of Three Layer Feedforward Neural Networks,” Journal of Faculty of Engineering, vol. 34, pp. 1–26, May 2001.
B. J. Oommen and T. D. Roberts, “Continuous Learning Automata Solutions to the Capacity Assignment Problem,” IEEE Transactions on Commputers, vol. 49, pp. 608–620, June 2000.
K. S. Narendra and K. S. Thathachar, Learning Automata: An Introduction. New York: Printice-Hall, 1989.
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Beigy, H., Meybodi, M. (2002). Call Admission Control in Cellular Mobile Networks: A Learning Automata Approach. In: Shafazand, H., Tjoa, A.M. (eds) EurAsia-ICT 2002: Information and Communication Technology. EurAsia-ICT 2002. Lecture Notes in Computer Science, vol 2510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36087-5_53
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DOI: https://doi.org/10.1007/3-540-36087-5_53
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