计算机应用 ›› 2012, Vol. 32 ›› Issue (01): 123-126.DOI: 10.3724/SP.J.1087.2012.00123

• 网络与通信 • 上一篇    下一篇

基于队列敏感性的无线接入网络拥塞控制算法

严黎明,牛玉刚   

  1. 化工过程先进控制和优化技术教育部重点实验室(华东理工大学),上海 200237
  • 收稿日期:2011-07-08 修回日期:2011-09-15 发布日期:2012-02-06 出版日期:2012-01-01
  • 通讯作者: 牛玉刚
  • 作者简介:严黎明(1987-),男,浙江余姚人,硕士研究生,主要研究方向:网络拥塞控制;牛玉刚(1964-),男,辽宁营口人,教授,博士,主要研究方向:网络拥塞控制、随机系统。
  • 基金资助:

    国家自然科学基金资助项目(61070090);上海市重点学科项目(B504)

Congestion control mechanism based on sensitive queue in wireless access network

YAN Li-ming,NIU Yu-gang   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology), Ministry of Education, Shanghai 200237, China
  • Received:2011-07-08 Revised:2011-09-15 Online:2012-02-06 Published:2012-01-01
  • Contact: NIU Yu-gang

摘要: 由于无线接入网络存在强非线性、大时延以及随机链路丢包等因素,导致经典主动队列管理(AQM)算法在实际控制时存在队列收敛速度慢、响应时间长等问题。通过分析随机指数标记(REM)算法在无线接入网中的特点,在原先REM价格模型的基础上对其进行了改进,以队列误差的平方项来克服价格对队列变化不敏感的缺陷,从而提出了一种基于队列敏感性的无线接入网络拥塞控制算法,并利用单神经网络对其参数进行了优化。最后,通过NS2仿真平台对所提算法与REM、PI算法进行对比,实验表明所提算法拥有队列收敛快、鲁棒性强的优点。

关键词: 无线接入网络, 拥塞控制, 主动队列管理, 随机指数标记, 单神经元

Abstract: Since the wireless access network is subject to the effects of strong nonlinearity, large delay and random link loss, the classical Active Queue Management (AQM) has the problems of slow convergence rate and long response to queue in the actual control process. By analyzing the characteristics of Random Exponential Marking (REM) algorithm in the wireless access network, this paper proposed a new congestion control method of wireless access network based on sensitive queue, which improved the original price model of REM and overcame the insensitivity of price to the change of queue size. Moreover, a single neuron was utilized to optimize the parameters. Finally, the proposed algorithm was compared with REM, PI on NS2 simulation platform. The simulation results show that the proposed algorithm has fast convergence and strong robustness.

Key words: wireless access network, congestion control, Active Queue Management (AQM), Random Exponential Marking (REM), single neuron

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