Ad-hoc网络PSD拥塞控制算法  

PSD Congestion Control Algorithm in Ad-hoc Network

作　　者：陈亮[1,2] 张宏[1] 

机构地区：[1]南京理工大学计算机科学与技术学院,南京210094 [2]南通纺织职业技术学院信息系,南通226007

出　　处：《计算机科学》2011年第6期45-48,69,共5页Computer Science

基　　金：国家自然科学基金项目(60903027);江苏省自然科学基金项目(BK2007593);江苏省高校青蓝工程资助

摘　　要：神经元PID算法能较好地控制队列长度,但其神经元增益对被控对象的状态较为敏感,基于试凑和经验的设定往往使控制效果难以保证。基于TCP拥塞窗口加法增大、乘法减小原则和排队机制,推导出拥塞窗口与丢弃概率、队列长度的微分方程,再对方程进行线性化,获得Ad-hoc网络TCP/AQM控制系统模型。基于该模型,将递推计算修正功能引入神经元PID,设计了一种神经元自适应PSD的AQM。该算法可以在线调整神经元增益。NS仿真表明,在无线分组丢失、突发流及链路容量变化的Ad-hoc网络中,PSD队列管理性能优于神经元PID。Neuron PID algorithm can control queue length successfully,but its neuron gain is sensitive for controlled object state.So it is difficult to guarantee the control performance because the gain depends on experience and trial me-thod.Congestion window size,loss probability and queue length differential equations were deduced based on TCP window additive-increase and multiplicative-decrease（AIMD） principle and queuing mechanism.TCP/AQM control model was obtained in Ad-hoc network through the equations linearization.Then it introduced recursion and modification gain to neuron PID based on the model.Finally,a neuron adaptive proportional summation differential（PSD） AQM was proposed.PSD algorithm can modify neuron gain dynamically.NS simulations demonstrate that PSD queue management performance is better than neuron PID under conditions of wireless packet loss,sudden flow and different link capacity.

关 键 词：无线自组网 拥塞控制 主动队列管理 比例求和微分 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]