Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Intelligence

Fuzzy Days 2001: Computational Intelligence. Theory and Applications pp 554–564Cite as

Reinforcement Learning for Control of Traffic and Access Points in Intelligent Wireless ATM Networks

  • Conference paper
  • First Online:

  • 3556 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2206))

Abstract

In this paper,we introduce the scheme for bandwidth allocation and congestion avoidance in wireless intelligent ATM networks which is based on reinforcement learning (RL). Our solution guarantees a desired bandwidth to connections which require a fixed wide bandwidth according to QoS constraints. Any unused bandwidth is momentarily backed up (returned) to Virtual Circuits. Proposed RL method is also suitable for supporting the mapping between a single ATM switch port and a wireless access points. It can control the access points in wireless intelligent ATM networks.

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

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A.G. Barto, R.S. Sutton, C.W. Anderson, Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 13, 1983, pp. 835–846.

    Google Scholar 

  2. R.R. Bhat, Draft Baseline Text for Wireless ATM Capability Set 1 Specification, ATM Forum BTD-WATM-01, 10 Dec. 1998.

    Google Scholar 

  3. R.E. Bellman, Dynamic Programming, Princeton University Press, Princeton, NJ, 1957.

    Google Scholar 

  4. C. Ben Ahmed, N.A. Boudriga, M.S. Obaidat, A Performance Analysis Scheme for Intelligent Wireless ATM Networks, Int. Journal of Communication Systems, Vo. 12, 1999, pp. 405–426.

    Article  Google Scholar 

  5. S.K. Biswas, B. Sengupta, Call Admissibility for Multirate Traffic in Wireless ATM Networks, IEEE INFOCOM, 1997, pp. 649–657.

    Google Scholar 

  6. J.A. Boyan, M. Littmann, Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach, in: J.D. Cowan et al. (Eds.), Advances in Neural Information Processing Systems — 6, Morgan Kauffman, San Francisco, 1994, pp. 671–678.

    Google Scholar 

  7. G. Christensen, R. Duncan, Wireless Intelligent Networking, Artech House, London, 2000.

    Google Scholar 

  8. S. Mahadevan, J. Connel, Automatic Programming of Behaviour-Based Robots Using Reinforcement Learning, Artificial Intelligence, Vol. 55, 1992, pp. 311–365.

    Article  Google Scholar 

  9. P. Marbach, O. Mihatsch, M. Schulte, J.N. Tsitsiklis, Reinforcement Learning for Call Admission Control and Routing in Integrated Service Networks, M. Jordan, M. Kearns, S. Solla (Eds.), Advances in NIPS — 10, MIT Press 1998, Cambridge 1998.

    Google Scholar 

  10. P. Marbach, O. Mihatsch, J.N. Tsitsiklis, Call Admission Control and Routing in Integrated Services Networks Using Neuro-Dynamic Programming, IEEE Journal on Selected Areas in Comm., Vol. 18,No. 2, Feb. 2000, pp. 197–208.

    Article  Google Scholar 

  11. M. Niemi, Application Requirements for WATM, ATM Forum, Document No.96-1058, 1996.

    Google Scholar 

  12. P. Piggott, A. Sattar, Reinforcement Learning of Iterative Behaviour with Multiple Sensors, Journal of Applied Intelligence, 4, 1994, pp. 351–365.

    Article  Google Scholar 

  13. S.P. Singh, Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes, AAAI’ 94, pp. 700–705.

    Google Scholar 

  14. S.P. Singh, D.P. Bartsekas, Reinforcements Learning for Dynamic Channel Allocation in Cellular Telephone Systems, in: M. Mozer et al. (Eds.), Advances in NIPS 9, MIT Press, Cambridge, 1997, pp. 974–980.

    Google Scholar 

  15. R.S Sutton, Temporal Credit Assignment in Reinforcement Learning, Ph.D.Thesis, Dept.of Computer and Information Science, University of Massachusetts, 1984.

    Google Scholar 

  16. R.S. Sutton, Learning to Predict by the Methods of Temporal Differences, Machine Learning,Vol. 3, 1988, pp. 9–44.

    Google Scholar 

  17. R.S. Sutton, A.G. Barto, Reinforcement Learning, An Introduction, MIT Press, Cambridge, 1998.

    Google Scholar 

  18. H. Tong, T.X. Brown, Adaptive Call Admission Control Under Quality of Service Constraints: A Reinforcement Learning Solution, IEEE Journal on Selected Areas in Comm., Vol. 18, No. 2, Feb. 2000, pp. 209–221.

    Google Scholar 

  19. C.J.C.H. Watkins, P. Dayan, Technical Note: Q-Learning, Machine Learning, Vol. 8, 1992, pp. 279–292.

    MATH  Google Scholar 

  20. E.K. Wesel, Wireless Multimedia Communication. Networking, Video, Voice, and Data, Addison-Wesley, Reading, 1998.

    Google Scholar 

  21. S.D. Whitehead, D.H. Ballard, Learning to Perceive and Act by Trial and Error, Machine Learning, Vol. 7, No. 1, July 1991, pp. 45–83.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jagiellonian University, Dept. of Computer Science, ul. Nawojki 11, 30-072, Cracow, Poland

    Jerzy Martyna

Authors
  1. Jerzy Martyna
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science I, University of Dortmund, 44221, Dortmund, Germany

    Bernd Reusch

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Martyna, J. (2001). Reinforcement Learning for Control of Traffic and Access Points in Intelligent Wireless ATM Networks. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4_56

Download citation

  • DOI: https://doi.org/10.1007/3-540-45493-4_56

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42732-2

  • Online ISBN: 978-3-540-45493-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation