基于业务流量预测的AOS自适应帧生成算法
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摘要
随着研究的深入,复杂空间系统业务数据流的自相似性逐渐被认识,传统的等时帧生成算法及高效率生成算法越来越难以适应空间系统业务流量的高突发性和高复杂性;基于此,提出了一种基于小波神经网络业务流量预测的自适应帧生成算法,在满足一定延时约束条件下,根据业务流量预测结果,自适应调整成帧时刻;帧复用效率相比等时帧生成算法有显著优势,同时还避免了高效率帧生成算法中存在的帧延时过长的问题。
With the in-depth study,the self-similarity of complex spatial data system is gradually recognized.Traditional lime frame generation algorithm and efficient frame generation algorithm are more and more difficult to adapt to the high burst and high complexity of space traffic.This paper presents an adaptive frame generation algorithm based on wavelet neural network traffic prediction.Under the condition of certain delay constraint,the adaptive frame generation time can be adjusted according to the prediction results of traffic flow.Compared with the time frame generation algorithm,the frame multiplexing efficiency of this algorithm has a significant advantage,and it also avoids the problem of long frame delay.
With the in-depth study,the self-similarity of complex spatial data system is gradually recognized.Traditional lime frame generation algorithm and efficient frame generation algorithm are more and more difficult to adapt to the high burst and high complexity of space traffic.This paper presents an adaptive frame generation algorithm based on wavelet neural network traffic prediction.Under the condition of certain delay constraint,the adaptive frame generation time can be adjusted according to the prediction results of traffic flow.Compared with the time frame generation algorithm,the frame multiplexing efficiency of this algorithm has a significant advantage,and it also avoids the problem of long frame delay.
引文
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[2]Leland W E,Taqqu M S,Bellcore W W.On the self-similar nature of Ethernet traffic[J],IEEE/ACM Transaction on Networking.1994.2(1):1-15.
[3]那振宇.卫星互联网服务质量保障方法研究[D].哈尔滨:哈尔滨工业大学,2010:38-45.
[4]Consultative Committee for Space Data System.AOSSpacc Data Link Protocol[S].Washington.DC,USA:CCSDS Press,2015.
[5]Consultative Committee for Space Data System.Overview of Space communications Protocols[S].Washington,DC,USA:CCSDS Press,2014.
[6]丁勇,刘守生,胡寿松.一种广义小波神经网络的结构及其优化方法[J].控制理论与应用,2003,20(1):125-128.
[7]李凡.用小波神经网络预测高速公路软土地基最终沉降量[J].合肥工业大学学报,2001,24(6):1124-1127.
[8]LI ST,CHENS C.Function approximation using robust wavelet neural networks[A].Proc of the 14thIEEE International Conference on Tools with Artificial Intelligent[C].Taiwan(China):IEEE Press.2002.483-488.
[9]闫纪如.粒子群优化的神经网络在交通流预测中的应用[D].杭州:浙江工业大学,2013.