Du Shu-xin, Yuan Shi-yong
National Laboratory of Industrial Control Technology, Institute of Intelligent Systems and Decision-Making, Zhejiang University, Hangzhou 310027, China.
J Zhejiang Univ Sci. 2004 Sep;5(9):1124-9. doi: 10.1631/jzus.2004.1124.
A new neural network based method for solving the problem of congestion control arising at the user network interface (UNI) of ATM networks is proposed in this paper. Unlike the previous methods where the coding rate for all traffic sources as controller output signals is tuned in a body, the proposed method adjusts the coding rate for only a part of the traffic sources while the remainder sources send the cells in the previous coding rate in case of occurrence of congestion. The controller output signals include the source coding rate and the percentage of the sources that send cells at the corresponding coding rate. The control methods not only minimize the cell loss rate but also guarantee the quality of information (such as voice sources) fed into the multiplexer buffer. Simulations with 150 ADPCM voice sources fed into the multiplexer buffer showed that the proposed methods have advantage over the previous methods in the aspect of the performance indices such as cell loss rate (CLR) and voice quality.
本文提出了一种基于神经网络的新方法，用于解决ATM网络用户网络接口（UNI）出现的拥塞控制问题。与之前将所有业务源的编码率作为控制器输出信号整体进行调整的方法不同，该方法仅对部分业务源的编码率进行调整，而其余业务源在发生拥塞时以前一编码率发送信元。控制器输出信号包括源编码率以及以相应编码率发送信元的源的百分比。这些控制方法不仅能使信元丢失率最小化，还能保证输入到复用器缓冲区的信息（如语音源）的质量。对150个ADPCM语音源输入到复用器缓冲区的仿真表明，在诸如信元丢失率（CLR）和语音质量等性能指标方面，该方法比之前的方法更具优势。
