Leng Supeng, Subramanian K R, Sundararajan N, Saratchandran P
School of Electrical and Electronic Engineering, Nanyang Technological University 639798, Singapore.
Int J Neural Syst. 2003 Aug;13(4):251-62. doi: 10.1142/S0129065703001571.
This paper presents a novel Call Admission Control (CAC) scheme which adopts the neural network approach, namely Minimal Resource Allocation Network (MRAN) and its extended version EMRAN. Though the current focus is on the Call Admission Control (CAC) for Asynchronous Transfer Mode (ATM) networks, the scheme is applicable to most high-speed networks. As there is a need for accurate estimation of the required bandwidth for different services, the proposed scheme can offer a simple design procedure and provide a better control in fulfilling the Quality of Service (QoS) requirements. MRAN and EMRAN are on-line learning algorithms to facilitate efficient admission control in different traffic environments. Simulation results show that the proposed CAC schemes are more efficient than the two conventional CAC approaches, the Peak Bandwidth Allocation scheme and the Cell Loss Ratio (CLR) upperbound formula scheme. The prediction precision and computational time of MRAN and EMRAN algorithms are also investigated. Both MRAN and EMRAN algorithms yield similar performance results, but the EMRAN algorithm has less computational load.
本文提出了一种新颖的呼叫接纳控制（CAC）方案，该方案采用神经网络方法，即最小资源分配网络（MRAN）及其扩展版本EMRAN。尽管当前重点是异步传输模式（ATM）网络的呼叫接纳控制（CAC），但该方案适用于大多数高速网络。由于需要准确估计不同服务所需的带宽，所提出的方案可以提供简单的设计过程，并在满足服务质量（QoS）要求方面提供更好的控制。MRAN和EMRAN是在线学习算法，以促进在不同流量环境中的高效接纳控制。仿真结果表明，所提出的CAC方案比两种传统的CAC方法，即峰值带宽分配方案和信元丢失率（CLR）上限公式方案更有效。还研究了MRAN和EMRAN算法的预测精度和计算时间。MRAN和EMRAN算法都产生了相似的性能结果，但EMRAN算法的计算负载较小。
