標題: Neural-network connection-admission control for ATM networks
作者: Cheng, RG
Chang, CJ
交大名義發表
電信工程研究所
National Chiao Tung University
Institute of Communications Engineering
關鍵字: neural networks;connection-admission control;ATM networks
公開日期: 1-四月-1997
摘要: ATM connection-admission control (CAC) using neural networks offers improvement over conventional CAC but creates some difficulties in real operation, such as complicated training processes. This is because ATM traffic characteristics are quite diverse, and quality of service (QoS) and bandwidth requirements vary considerably. A neural-network connection-admission control (NNCAC) method which can overcome these difficulties by preprocessing neural-network input parameters is proposed. The NNCAC method introduces a unified metric for input-traffic parameters by utilising robust analytical results of the equivalent-capacity method. It diminishes the estimation error of the equivalent-capacity method, due to modelling, approximation unpredictable statistical system, by employing the learning capability of a neural network. The method further considers the congestion status parameter and the cell loss probability, which provides insight information about the system. Simulation results revealed that the proposed NNCAC method provided a 20% system-utilisation improvement over Hiramatsu's neural-network CAC scheme and a 10% system-utilisation improvement over based CAC scheme, while maintaining QoS contracts. It was also found that the NNCAC: method provided utilisation comparable with that of the NFCAC scheme but possessed a lower cell loss probability. NNCAC is suitable for designers who are not familiar with fuzzy-logic control schemes or have no ideas about the requisite knowledge of CAC.
URI: http://hdl.handle.net/11536/636
ISSN: 1350-2425
期刊: IEE PROCEEDINGS-COMMUNICATIONS
Volume: 144
Issue: 2
起始頁: 93
結束頁: 98
顯示於類別:期刊論文


文件中的檔案:

  1. A1997WZ74900006.pdf