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Power-spectrum based neural-net connection admission control for multimedia networks

Power-spectrum based neural-net connection admission control for multimedia networks

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Multimedia networks need sophisticated and real-time connection admission control (CAC) not only to guarantee the required quality of service (QoS) for existing calls but also to enhance utilisation of systems. The power spectral density (PSD) of the input process contains correlation and burstiness characteristics of input traffic and possesses the additive property. Neural networks have been widely employed to deal with the traffic control problems in high-speed networks because of their self-learning capability. The authors propose a power-spectrum-based neural-net connection admission control (PNCAC) for multimedia networks. A decision hyperplane is constructed for the CAC using power spectrum parameters of traffic sources of connections, under the constraint of the QoS requirement. Simulation results show that the PNCAC method provides system utilisation and robustness superior to the conventional equivalent capacity CAC scheme and Hiramatsu's neural network CAC scheme, while meeting the QoS requirement.

References

    1. 1)
      • `ITU-T Recommendation: Traffic control and congestion control in B-ISDN', 1.371, May 1996, Geneva.
    2. 2)
    3. 3)
      • A.I. Elwalid , D. Mitra . Effective bandwidth of general Markovian traffic sources and admission control of high speed networks. IEEE/ACM Trans. Netw. , 3 , 329 - 343
    4. 4)
    5. 5)
      • S.Q. Li , C.L. Hwang . Queue response to input correlation functions: continuous spectral analysis. IEEE/ACM Trans. Netw. , 6 , 678 - 692
    6. 6)
      • H.D. Sheng , S.Q. Li . Spectral analysis of packet loss rate at a statistical multiplexer for multimedia services. IEEE/ACM Trans. Netw. , 1 , 53 - 65
    7. 7)
      • C.W. Therrien . (1992) Discrete random signals and statistical signal processing.
    8. 8)
      • C.J. Chang , C.H. Lin , D.S. Guan , R.G. Cheng . Design of a power-spectrum-based ATM connection admission control for multimedia communications. IEEE Trans. Ind. Electron. , 1 , 52 - 59
    9. 9)
      • A. Hiramatsu . ATM communications network control by neural networks. IEEE Trans. Neural Netw. , 1 , 122 - 130
    10. 10)
    11. 11)
      • R.G. Cheng , C.J. Chang , L.F. Lin . A QoS provisioning neural fuzzy connection admission controller for multimedia high-speed networks. IEEE/ACM Trans. Netw. , 1 , 111 - 121
    12. 12)
      • C.T. Lin , C.S.G. Lee . (1996) Neural fuzzy sytems: a neuro-fuzzy synergism to intelligent systems.
    13. 13)
      • D.E. Rumelhart , G.E. Hinton , R.J. Williams . (1986) Learning internal representation by error propagation, Parallel distributed processing: Explorations in the microstructure of cognition.
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