The Journal of Supercomputing

, Volume 71, Issue 3, pp 871–893 | Cite as

A learning automata-based adaptive uniform fractional guard channel algorithm

Article

Abstract

In this paper, we propose an adaptive call admission algorithm based on learning automata. The proposed algorithm uses a learning automaton to specify the acceptance/rejection of incoming new calls. It is shown that the given adaptive algorithm converges to an equilibrium point which is also optimal for uniform fractional channel policy. To study the performance of the proposed call admission policy, the computer simulations are conducted. The simulation results show that the level of QoS is satisfied by the proposed algorithm and the performance of given algorithm is very close to the performance of uniform fractional guard channel policy which needs to know all parameters of input traffic. The simulation results also confirm the analysis of the steady-state behaviour.

Keywords

Reinforcement learning Learning automata Uniform fractional guard channel policy Adaptive uniform fractional guard channel policy 

References

  1. 1.
    Ramjee R, Towsley D, Nagarajan R (1997) On optimal call admission control in cellular networks. Wirel Netw 3:29–41CrossRefGoogle Scholar
  2. 2.
    Hong D, Rappaport S (1986) Traffic modelling and performance analysis for cellular mobile radio telephone systems with prioritized and non-prioritized handoffs procedure. IEEE Trans Veh Technol 35:77–92CrossRefGoogle Scholar
  3. 3.
    Haring G, Marie R, Puigjaner R, Trivedi K (2001) Loss formulas and their application to optimization for cellular networks. IEEE Trans Veh Technol 50:664–673CrossRefGoogle Scholar
  4. 4.
    Beigy H, Meybodi MR (2004) A new fractional channel policy. J High Speed Netw 13:25–36Google Scholar
  5. 5.
    Yoon CH, Kwan C (1993) Performance of personal portable radio telephone systems with and without guard channels. IEEE J Sel Areas Commun 11:911–917CrossRefGoogle Scholar
  6. 6.
    Guern R (1988) Queuing-blocking system with two arrival streams and guard channels. IEEE Trans Commun 36:153–163CrossRefGoogle Scholar
  7. 7.
    Li B, Li L, Li B, Sivalingam KM, Cao X-R (2004) Call admission control for voice/data integrated cellular networks: performance analysis and comparative study. IEEE J Sel Areas Commun 22:706–718CrossRefGoogle Scholar
  8. 8.
    Chen X, Li B, Fang Y (2005) A dynamic multiple-threshold bandwidth reservation (DMTBR) scheme for QoS provisioning in multimedia wireless networks. IEEE Trans Wirel Commun 4:583–592CrossRefGoogle Scholar
  9. 9.
    Beigy H, Meybodi MR (2005) A general call admission policy for next generation wireless networks. Comput Commun 28:1798–1813CrossRefGoogle Scholar
  10. 10.
    Beigy H, Meybodi MR (2004) Adaptive uniform fractional channel algorithms. Iran J Electr Comput Eng, 3:47–53Google Scholar
  11. 11.
    Beigy H, Meybodi MR (2005) An adaptive call admission algorithm for cellular networks. Electr Comput Eng 31:132–151CrossRefMATHGoogle Scholar
  12. 12.
    Beigy H, Meybodi MR (2008) Asynchronous cellular learning automata. Automatica 44:1350–1357CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Beigy H, Meybodi MR (2011) Learning automata based dynamic guard channel algorithms. J Comput Electr Eng 37(4):601–613CrossRefMATHGoogle Scholar
  14. 14.
    Baccarelli E, Cusani R (1996) Recursive Kalman-type optimal estimation and detection of hidden markov chains. Signal Process 51:55–64Google Scholar
  15. 15.
    Baccarelli E, Biagi M (2003) Optimized power allocation and signal shaping for interference-limited multi-antenna ad hoc networks, vol 2775 of Springer lecture notes in computer science, Springer, pp 138–152Google Scholar
  16. 16.
    Beigy H, Meybodi MR (2009) Cellular learning automata based dynamic channel assignment algorithms. Int J Comput Intell Appl 8(3):287–314CrossRefMATHGoogle Scholar
  17. 17.
    Srikantakumar PR, Narendra KS (1982) A learning model for routing in telephone networks. SIAM J Control Optim 20:34–57CrossRefMATHMathSciNetGoogle Scholar
  18. 18.
    Nedzelnitsky OV, Narendra KS (1987) Nonstationary models of learning automata routing in data communication networks. IEEE Trans Syst Man Cybern. SMC–17:1004–1015CrossRefGoogle Scholar
  19. 19.
    Oommen BJ, de St Croix EV (1996) Graph partitioning using learning automata. IEEE Trans Comput 45:195–208Google Scholar
  20. 20.
    Beigy H, Meybodi MR (2006) Utilizing distributed learning automata to solve stochastic shortest path problems. Int J Uncertain Fuzziness Knowl Based Syst 14:591–615CrossRefMATHMathSciNetGoogle Scholar
  21. 21.
    Oommen BJ, Roberts TD (2000) Continuous learning automata solutions to the capacity assignment problem. IEEE Trans Comput 49:608–620CrossRefGoogle Scholar
  22. 22.
    Moradabadi B, Beigy H (2014) A new real-coded Bayesian optimization algorithm based on a team of learning automata for continuous optimization. Genetic programming and evolvable machines 15:169–193CrossRefGoogle Scholar
  23. 23.
    Meybodi MR, Beigy H (2001) Neural network engineering using learning automata: determining of desired size of three layer feedforward neural networks. J Fac Eng 34:1–26Google Scholar
  24. 24.
    Beigy H, Meybodi MR (2001) Backpropagation algorithm adaptation parameters using learning automata. Int J Neural Syst 11:219–228CrossRefGoogle Scholar
  25. 25.
    Oommen BJ, Hashem MK (2013) Modeling the learning process of the teacher in a tutorial-like system using learning automata. IEEE Trans Syst Man Cybern Part B Cybern 43(6):2020–2031Google Scholar
  26. 26.
    Yazidi A, Granmo OC, Oommen BJ (2013) Learning automaton based on-line discovery and tracking of spatio-temporal event patterns. IEEE Trans Syst Man Cybern Part B Cybern 43(3):1118–1130Google Scholar
  27. 27.
    Narendra KS, Thathachar KS (1989) Learning automata: an Introduction. Printice, New YorkGoogle Scholar
  28. 28.
    Srikantakumar P (1980) Learning models and adaptive routing in telephone and data communication networks. PhD thesis, department of electrical engineering, University of Yale, USAGoogle Scholar
  29. 29.
    Norman MF (1972) Markovian process and learning models. Academic Press, New YorkGoogle Scholar
  30. 30.
    Mood AM, Grabill FA, Bobes DC (1963) Introduction to the theory of statistis. McGraw-HillGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Computer EngineeringSharif University of TechnologyTehranIran
  2. 2.Department of Computer EngineeringAmirkabir University of TechnologyTehranIran

Personalised recommendations