Differential AR algorithm for packet delay prediction  

作　　者：JIAO Liangbao ZHANG De BI Houjie 

机构地区：[1]Institute of Acoustics, State Key Laboratory of Modern Acoustics, Nanjing University, Nanjing 210093, China [2]Institute of Communication Technique, Nanjing University, Nanjing 210093, China

出　　处：《Progress in Natural Science:Materials International》2006年第4期437-440,共4页自然科学进展·国际材料（英文版）

基　　金：Supported by National Natural Science Foundation of China (Grant No.10234060)

摘　　要：Different delay prediction algorithms have been applied in multimedia communication, among which linear prediction is attractive because of its low complexity. AR （auto regressive） algorithm is a traditional one with low computation cost, while NLMS （normalize least mean square） algorithm is more precise. In this paper, referring to ARIMA （auto regression integrated with moving averages） model, a differential AR algorithm （DIAR） is proposed based on the analyses of both AR and NI.MS algorithms. The predictiun precision of the new algorithm is about 5-10 db higher than that of the AR algorithm without increasing the computation complexity. Compared with NLMS algorithm, its precision slightly improves by 0.1 db on average, but the algorithm complexity reduces more than 90 %. Our simulation and tests also demonstrate that this method improves the performance of the average end to-end delay and packet loss ratio significantly.不同延期预言算法在多媒体通讯，因为它的低复杂性，线性预言在之中是吸引人的被使用了。AR (汽车回归) 算法是有低计算费用的传统的，当时 NLMS (使最不吝啬的平方正常化) 算法是更精确的。在这篇论文，指 ARIMA (与动人的一般水准综合的汽车回归) 模型，一个微分 AR 算法(DIAR ) 基于 AR 和 NLMS 算法的分析被建议。没有增加计算复杂性，新算法的预言精确比 AR 算法的高关于 5-10 db。与 NLMS 算法相比，它的精确稍微平均由 0.1 db 改善，但是算法复杂性减少超过 90% 。我们的模拟和测试也证明这个方法显著地改进平均端对端的延期和包损失比率的表演。

关 键 词：NLMS algorithm differential AR algorithm ARIMA model. 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]