基于k阶马尔可夫链的单播网络丢包层析成像

该文针对时域相关的网络链路丢包估计问题,提出一种基于k阶马尔可夫链的单播网络丢包层析成像方法。该方法首先引入k阶马尔可夫链描述网络链路丢包过程,然后用最大伪似然方法估计k阶马尔可夫链链路丢包模型的状态转移概率。当k足够大时,该文方法可以根据单播端到端测量数据,准确地估计出网络链路上每个数据包丢失的概率。ns-2仿真验证了该文方法的有效性。     

基于子树丢包模式的链路丢包率快速推断算法

为提高网络链路丢包率的测量速度,本文提出一种基于子树丢包模式的链路丢包率推断算法.该算法通过选择合理的链路丢包率初始值以减少迭代次数;根据端到端测量结果将网络拓扑划分为传输状态确定性区域和非确定性区域,避免确定性区域冗余分解造成的时间开销;通过对非确定性区域子树丢包模式按层分解,以子树丢包模式为基本计算单元,减少非确定性区域链路丢包的重复分解过程,提高链路丢包率计算速度.仿真结果表明,该算法能在不损失测量精度的前提下,减少链路丢包率测量总时间,提高测量速度.     

时变信道下线性网络分批稀疏码秩分布研究

作为一种应用于多跳网络的低复杂度两步式编码技术,分批稀疏（batched sparse,BATS）码的传输性能与传输矩阵的秩分布直接相关。现有文献在假设各链路丢包率均为常数的前提下,研究了分批稀疏码在纠删信道下的秩分布。然而,在一些场景（如工业互联网）,大量的移动节点部署在整个网络中,可能导致节点之间的信道变成时变信道,即链路上的丢包率随时间变化而变化。因此在假定网络中各节点之间链路丢包率随机变化的场景下,研究了随机线性网络编码（random linear network coding, RLNC）和系统重编码作为内码编码方案时,分批稀疏码传输矩阵的秩分布,推导了链路丢包率服从有限区间正态分布情况下归一化秩期望的闭合解,并通过蒙特卡洛仿真验证了该闭合解的正确性。     

一种非平稳网络延迟层析成像的方法

 网络层析成像将医学、地震学等领域成熟的层析成像理论应用于通信网络领域,它通过基于端到端测量来估计网络内部的行为,为目前在国际学术界备受关注的新技术之一.本文提出了一种基于递归多感知器网络(Recurrent Multilayer Perceptron :RMLP)延迟时变参数的追踪算法,该算法能够在没有任何先验信息条件下追踪非平稳网络链路平均延迟,估计链路延迟的概率密度分布.仿真实验也验证了以上两点,同时本文提出的算法比序贯蒙特卡洛(Sequential Monte Carlo:SMC)方法^[9]具有更小的估计误差.     

卷积窗及其在电力系统参量估计中的应用

利用矩形窗的多重卷积,导出一类新的窗函数,称其为卷积窗,并研究其在电力系统的参数估计中的应用.误差分析和数值模拟表明: 若采用k阶矩形卷积窗(矩形窗的k重卷积),各电气参量测量值的误差正比于相对频偏的k次方.在电力系统中,只需取4阶窗函数,便可将因非同步采样引起的测量误差降低到10^-7的量级,而不必采用任何同步采样措施.     

光网络QOS影响因素初探

分析了网络传输损伤、网络元件可靠性、链路带宽分布等因素对光网络中业务丢包率的影响,给出了一种计算在区分业务等级的网络中不同等级业务丢包率的计算方法。然后用该计算方法对CERNET拓扑结构采用最短路径路由算法的网络,在不同网络带宽分配和两种不同流量模型的情况下进行了计算。最后根据计算结果对不同服务等级业务丢包率的各种影响因素进行了比较分析。     

基于信道感知与监测的传感器网络传输参数自适应调整方法

针对预警系统中广域部署在偏远地区的传感器利用现有话音通信线路传输感知信息时,信道具有时变的特征,提出了一种基于信道感知与监测的传输参数自适应调整方法。该方法解决了连接线路在低信噪比下频繁掉线导致的传感器网络采集数据缺失与延时的问题。首先,在数据包传输期间,引入盲信噪比估计方法对信道质量进行感知,通过加权滑窗平均估计结果得到信道质量周期监测的观测值。然后根据卡尔曼滤波原理建立离散化的传输参数自适应调整模型,在保持链路连通状态下,根据信道质量优劣变化自适应地调整传输速率与功率。最后,实验表明,对于野外布设传感器网络的时变信道,该方法能有效保证信息传输的实时性与可靠性。      

无线网络中TCP友好流媒体传输改进机制

为保持无线网络中多媒体业务对TCP的友好性，提出了一种适用于无线网络的动态自适应的流媒体传输速率调节机制。该机制通过在接收端区分网络拥塞丢包和链路错误随机丢包，准确判断网络的拥塞状况结合接收端缓存区占用程度，自适应实施多级速率调节，实现了TCP流友好性和流媒体服务质量（QoS）的折中。由于准确区分出无线链路误码丢包和动态调整流媒体QoS要求，该机制能维持较高的网络利用率。仿真实验结果显示在连接数为2和32，链路误码率从0到0．1变化时TCP,TFRC和吞吐量幅度下降幅度较大，WTFCC幅度下降相对较慢，最大相差达2M；在网络负载重时，尽管链路误码率较低，WTFCC区分链路错误与拥塞丢包，因此，端到端丢包率高于TCP和TFRC，但整体传输吞吐量也高于两者。归一化吞吐量显示WTFCC对TCP流友好。     

基于最小覆盖集的高精度链路丢包率测量方法

为降低链路丢包率测量过程中网络资源消耗,提高测量的精度,该文提出一种基于最小覆盖集的高精度链路丢包率测量方法。通过最小覆盖集测量方法有效降低路由矩阵的秩,从而减少测量路径数量;采用线性方程组求解和Gibbs采样相结合的方法,有效提高测量的准确度。仿真实验结果表明,该文提出的算法需要较少的端到端测量路径,同时具备更高的精度。     

基于路径稳定性的MAODV路由协议的改进

在Ad Hoc网络中,节点随意快速的移动通常会造成网络拓扑结构的剧烈变化,可能导致传输链路断裂和路径的不稳定。针对这种情况,在分析现有MAODV路由改进技术的基础上,提出了一种改进的、基于路径稳定性的路由协议(RS-MAODV)。新协议与MAODV不同的地方在于:考虑构成路径的链路间的相关性,选择最稳定的路由进行数据传输,以此改善网络性能。利用NS2仿真工具对改进前后网络的丢包率及端到端延迟参数做比较,实验数据表明,改进后协议的网络丢包率及端到端延迟均得到改善。     

k冗余多播网络中网络编码算法设计与分析

k冗余多播网络采用网络编码可实现最大多播速率k的信息传输。该文利用最大距离可分码已有成果,给出k冗余多播网络在不同发送速率下所需的最小有限域,构造最大距离可分码[n, k]生成矩阵,将其列向量作为信源输出链路的全局编码向量,设计网络码字,实现网络编码。应用实例表明该网络编码方法相对现有的通用网络编码算法而言,具有更低的计算复杂度。     

Unicast-based inference of network link delay distributions with finite mixture models

Providers of high quality-of-service over telecommunication networks require accurate methods for remote measurement of link-level performance. Recent research in network tomography has demonstrated that it is possible to estimate internal link characteristics, e.g., link delays and packet losses, using unicast probing schemes in which probes are exchanged between several pairs of sites in the network. We present a new method for estimation of internal link delay distributions using the end-to-end packet pair delay statistics gathered by back-to-back packet-pair unicast probes. Our method is based on a variant of the penalized maximum likelihood expectation-maximization (PML-EM) algorithm applied to an additive finite mixture model for the link delay probability density functions. The mixture model incorporates a combination of discrete and continuous components, and we use a minimum message length (MML) penalty for selection of model order. We present results of Matlab and ns-2 simulations to illustrate the promise of our network tomography algorithm for light cross-traffic scenarios.     

任意信源数和认证符数的Cartesian认证码的构造(Ⅱ)

对于任意两个相互无关的参数k,n,该文提出了一种基于正交排列的迭代方法,并以该方法为主构造了一类信源数目为k,认证符数目为n的Cartesian认证码。在信源和编码规则分布等概的条件下,敌方模仿攻击和替换攻击成功的概率均为1/n。在相同的k,n条件下,与已知的笛卡儿积构造法相比,迭代法所构造的Cartesian认证码的编码规则数目更少。     

Bayesian approach to parameter estimation and interpolation oftime-varying autoregressive processes using the Gibbs sampler

A nonstationary time series is one in which the statistics of the process are a function of time; this time dependency makes it impossible to utilise standard analytically defined statistical estimators to parameterise the process. To overcome this difficulty, the time series is considered within a finite time interval and is modelled as a time-varying autoregressive (AR) process. The AR coefficients that characterise this process are functions of time, represented by a family of basis vectors. The corresponding basis coefficients are invariant over the time window and have stationary statistical properties. A method is described for applying a Markov chain Monte Carlo method known as the Gibbs sampler to the problem of estimating the parameters of such a time-varying autoregressive (TVAR) model, whose time dependent coefficients are modelled by basis functions. The Gibbs sampling scheme is then extended to include a stage which may be used for interpolation. Results on synthetic and real audio signals show that the model is flexible, and that a Gibbs sampling framework is a reasonable scheme for estimating and characterising a time-varying AR process     

Hamming重量为k的布尔函数的全局特征和非线性度

该文给出了布尔函数的自相关系数和互相关系数的一些性质,得到n元布尔函数f (x)满足t 阶扩散准则时,n,t 和Hamming重量wt(f)的制约关系,给出了任意Hamming重量为k的布尔函数的平方和指标下界表达式,推出了仅由布尔函数Hamming重量所确定的非线性度的上界表达式。这些结论推广了已有结果。     

Time‐varying network link loss rate tracking from unicast end‐to‐end measurements

Time‐varying network link loss rate is a useful information for network managers to discover and locate the network link failures. This paper proposes a method to track time‐varying network link loss rates from unicast end‐to‐end measurements. The method first trains a state transition matrix to capture the spatio‐temporal characters of packet link transmission probabilities by sending end‐to‐end probe packets and then estimates the time‐varying link loss rates using the state transition matrix and the end‐to‐end measurements obtained from background traffic (the existed packets in network). We also introduce a validation step to check and retrain the state transition matrix in order to ensure the accuracy of the state transition matrix. Our method is capable of tracking the variation of link loss rates without incessantly sending probe packets, which is very feasible for many practical applications. The ns‐2 simulation results show the good performance of our method.     

无线传感器网络中一种具有稳定链路的鲁棒可调拓扑算法

该文针对传感器网络中无线链路的不稳定性问题,基于r邻居图模型提出一种具有稳定链路的鲁棒可调拓扑控制算法RAWSL (Robust Adjustable with Steady Links)。算法以接收信号强度阈值作为链路判断条件,有效地避免了网络中不稳定链路存在的可能性,并能够通过调整参数r取值满足不同网络的需求。实验结果表明,RAWSL算法不仅能够确保全网络的连通,还具有鲁棒性高和时延较低的特点。     

Estimating the Traffic Distribution Matrix of a Packet-Switched Network Using Aggregate Traffic Measurements

Methods are developed to estimate the source–destination traffic distribution matrix of a packet network using only aggregate link and source/sink traffic measurements. The methods are useful for network planning and monitoring of large packet networks, where it is impractical to gather measurement data on every end-to-end traffic flow. The first method assumes that the distribution matrix is time-invariant. This method is of theoretical interest but provides the foundation for developing a method for the realistic case of a time-varying matrix. The second method assumes that the matrix is time-varying. It uses linear programming (LP) to find a distribution matrix that optimally fits the measurement data. A practical problem with the first two methods is that the computational requirements increase as the square of the number of network nodes. The third method is a fast exact decomposition procedure for the time-invariant case that scales with the network size. The maximum number of unknowns that needs to be solved simultaneously is equal to the number of network nodes. The final method is a fast decomposition procedure for the time-varying case. This procedure scales with the network size. It uses LP to find an approximate distribution matrix that optimally fits the measurement data. The methods are applied to simulated example networks to illustrate the accuracy and speed.     

基于网络断层扫描的拓扑推断

网络断层扫描是一种全新的网络测量技术,该技术结合了网络测量和统计推断,可以解决网络测量的部分难题。网络拓扑推断是网络断层扫描的重要应用之一,基于网络断层扫描的拓扑推断通过端到端的测量,即可推断网络内部的拓扑结构,而不需要网络内部节点的合作。本文系统总结了网络断层扫描在网络拓扑推断中的测量方法和推断算法,对比了当前的测量...