Nothing Special   »   [go: up one dir, main page]

Skip to main content

A Learning Automata Based Dynamic Guard Channel Scheme

  • Conference paper
  • First Online:
EurAsia-ICT 2002: Information and Communication Technology (EurAsia-ICT 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2510))

Included in the following conference series:

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. Since the parameters of network traffics are unknown and time varying, the optimal number of guard channels is not known and varies with time. In this paper, we introduce a new dynamic guard channel policy, which adapts the number of guard channels in a cell based on the current estimate of dropping probability of handoff calls. The proposed algorithm minimizes blocking probability of new calls subject to the constraint on the dropping probability of handoff calls. In the proposed policy, a learning automaton is used to find the optimal number of guard channels. The proposed algorithm doesn’t need any a priori information about input traffic. The simulation results show that performance of this algorithm is close to the performance of guard channel policy for which we need to know all traffic parameters in advance. Two advantages of the proposed policy are that it is fully autonomous and adaptive. The first advantage implies that, the proposed policy does not require any exchange of information between the neighboring cells and hence the network overheads due to the information exchange will be zero. The second one implies that, the proposed policy does not need any priori information about input traffic and the traffic may vary.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. 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.

    Google Scholar 

  2. S. Oh and D. Tcha, “Priotrized Channel Assignment in a Cellular Radio Network,” IEEE Transactions on Communications, vol. 40, pp. 1259–1269, July 1992.

    Google Scholar 

  3. R. Ramjee, D. Towsley, and R. Nagarajan, “On Optimal Call Admission Control in Cellular Networks,” Wireless Networks, vol. 3, pp. 29–41, 1997.

    Article  Google Scholar 

  4. 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.

    Google Scholar 

  5. 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.

    Google Scholar 

  6. 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.

    Google Scholar 

  7. 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.

    Google Scholar 

  8. H. Beigy and M. R. Meybodi, “Backpropagation Algorithm Adaptation Parameters using Learning Automata,” International Journal of Neural Systems, vol. 11, no. 3, pp. 219–228, 2001.

    Google Scholar 

  9. M. R. Meybodi and H. Beigy, “New Class of Learning Automata Based Schemes for Adaptation of Backpropagation Algorithm Parameters,” International Journal of Neural Systems, vol. 12, pp. 45–68, Feb. 2002.

    Google Scholar 

  10. M. R. Meybodi and H. Beigy, “A Note on Learning Automata Based Schemes for Adaptation of BP Parameters,” Accepted for Publication in the Journal of Neuro Computing, To Appear.

    Google Scholar 

  11. 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.

    Google Scholar 

  12. K. S. Narendra and K. S. Thathachar, Learning Automata: An Introduction. New York: Printice-Hall, 1989.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Beigy, H., Meybodi, M. (2002). A Learning Automata Based Dynamic Guard Channel Scheme. 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_75

Download citation

  • DOI: https://doi.org/10.1007/3-540-36087-5_75

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00028-0

  • Online ISBN: 978-3-540-36087-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics