WiMax网络中自相似呼叫接纳控制算法研究

针对WiMAX网络业务流的自相似特性,提出用M/Pareto模型来对网络业务流进行建模,通过M/Pareto模型和FBM业务流模型之间统计性质得到2个模型间的参数映射关系,推导出基于M/Pareto模型参数的有效带宽计算公式,据此设计出一个自相似接纳控制算法(SS-CAC)。该算法避免了通过测量的方法来获取FBM业务流模型中参数值,又适应了WiMAX网络流自相似这一特性,使得计算的有效带宽更加精确和方便,提高了系统资源利用率。仿真结果表明,该算法极大地提高网络的带宽利用率,降低了系统的呼叫阻塞率。     

自相似业务下资源分配研究

重点研究了排队系统中自相似业务带宽分配和接纳控制.根据分形布朗运动包络过程推导出了有效带宽的计算公式,并且推导出了考虑信源内增益和信源间增益的接纳控制算法.通过仿真研究了有效带宽随缓存区、Hurst系数的变化关系,接纳域随缓存区、Hurst系数的变化关系.证明了基于FBM下的资源分配比传统的模型更有效.     

基于FrFT的网络流量Hurst参数估计器

实际的网络流量模型采用自相似模型,Hurst参数是序列长相关程度的度量。为方便工程上对网络流量的长相关性进行估计,介绍了网络流量自相似模型和Hurst参数小波分析法,建立了分数阶傅里叶变换（FrFT）与小波分析之间的联系,在此基础上介绍了一种新的基于FrFT的网络流量Hurst参数估计方法,并运用此方法设计了网络流量Hurst参数估计器。通过对白噪声和已知Hurst参数的实际网络流量数据进行估计,本方法可以有效估计随机时间序列的Hurst参数。     

一种新的DDoS攻击检测算法

为了准确及时的进行DDoS攻击检测,提出了一种新的DDoS攻击检测算法。该算法在基于传统的小波分析检测DDoS攻击的基础上融入了主成分分析法和小波分析法中DDoS检测方法,并根据该算法设计相应的模型和算法来检测 DDoS 攻击,并且引入信息论中的信息熵对源IP地址的分散程度进行度量,根据初始阶段Hurst指数及熵值的变化自适应地设定阈值以检测攻击的发生。实验结果表明,该方法大幅度的提高了DDoS检测的速度。     

基于FBM分形特征的快速小波地形模拟算法

该文基于FBM分形原理,采用Haar小波对自然地形数据进行模拟构建。在分析自然地形分形系数与小波系数之间关系的基础上,建立基于Haar小波的地形构建模型;通过对真实地形数据不同阶层小波系数统计计算得到分形特征参数;以构建模型为基础,结合分形特征参数,模拟生成自然地形。该算法相对真实地形数据具有小的交叉熵,且计算复杂度较小,仿真数据验证了算法的有效性。     

基于Hurst指数差异的海面小目标检测

针对传统舰载雷达和岸基雷达很难检测出海杂波背景下小目标的现状,提出一种基于Hurst指数差异的方法来检测海杂波背景下小目标。该算法基于小波分析法：计算各种极化方式下不同距离门的海杂波的Hurst指数,分析发现距离门含有小目标的Hurst指数明显大于距离门只含有海杂波的Hurst指数,而只含有海杂波的不同距离门间Hurst指数变化较小,因此可基于不同距离门的Hurst指数差值作为检测小目标的标准。实验证明,该算法可在没有任何先验知识条件较准确地检测出海杂波背景下的小目标。     

海杂波AR谱多重分形特性及微弱目标检测方法

该文研究了海杂波功率谱的多重分形特性。为了克服频谱傅里叶分析的缺点,用现代谱估计的方法来计算海杂波的功率谱。AR模型是一个线性预测模型,它通过序列的自相关函数矩阵来估计功率谱,并且具有更精确的频谱分辨率。该文主要分析基于AR谱估计的海杂波功率谱的多重分形特性,以及在微弱目标检测中的应用。首先,以分数布朗运动(FBM)模型为例,证明其功率谱具有多重分形特性。其次,根据X波段雷达的实测海杂波数据,通过多重去趋势分析法(MF-DFA)验证了海杂波AR谱的多重分形特性。最后,分析了海杂波AR谱的广义Hurst指数以及影响参数,并提出一种基于局部AR谱广义Hurst指数的目标检测方法。实验结果表明,该种检测方法具有海杂波背景下微弱目标检测的能力。与现有的分形检测方法和传统的CFAR检测方法对比,该算法在低信杂比情况下具有较好的检测性能。     

基于VTP方法的DDoS攻击实时检测技术研究

为了能及时准确检测DDoS攻击的发生,在对方差－时间图(VTP,Variance－Time Plots)方法分析的基础上,对基于VTP的实时在线计算Hurst参数技术进行了性能分析,得出了其具有高效性的结论,并利用这种技术,对MIT的林肯实验室数据进行了分析,总结出了DDoS攻击过程中,网络流量的自相似模型的Hurst参数变化规律,即DDoS攻击刚开始时,Hurst参数具有较快的上升趋势,然后在参数值很高水平的基础上具有缓慢下降趋势,直到计算Hurst参数的网络流量全部是DDoS攻击流量时,有一个突然下降的现象(从0.95以上降至0.45左右),并由此总结出一种基于Hurst参数实时检测DDoS攻击发生的技术.     

基于自相似检测DDoS攻击的小波分析方法

针对传统检测方法不能有效检测弱DDoS攻击和区分繁忙业务和攻击的问题,在研究 DDOS攻击对网络流量自相似性影响的基础上,提出了小波分析检测DDoS攻击的方法,并设计了采用该方法检测DDoS攻击的模型,解决了方法实现过程中小波选择、求解Hurst参数的一些关键问题,实验表明,提出的方法能够识别繁忙业务、检测到弱DDoS攻击引起的Hurst参数值的变化,比传统的检测方法准确灵敏.     

消失矩对小波分析求解自相似参数Hurst的影响研究

研究了消失矩对小波求解自相似参数Hurst值的影响,给出了理论推导和分析,间接论证了消失矩对Hurst参数求解的影响趋势,通过大量的实验结果给出了详细情况。理论和实验结果一致表明,在局部上,Hurst参数随着消失矩的增大呈现周期性变化,但总的趋势是随着消失矩的增大Hurst值减小,误差在增大,满足条件的最小消失矩是最佳选择。该结论能为基于自相似和小波分析的大量应用提供理论依据。     

A fast estimation algorithm on the Hurst parameter of discrete-timefractional Brownian motion

The purpose of this paper is to demonstrate that the discrete-time fractional Gaussian noise (DFGN) is a regular process. Based on this property, a fast algorithm with low computational cost is proposed to estimate the Hurst parameter H, which is an important parameter in fractional Brownian motion (FBM). This algorithm is robust under amplitude shift, invariant to time shift, and unaffected by a scaling factor in power spectral density (PSD). Finally, the computational complexity is also investigated     

Restoration of Noisy 1/f Family Fractal Signal Based on Haar Wavelets and Wiener Filter

In order to restore noisy fractal Brownian motion(FBM), discrete fractional gaussian noise(DFGN) combined with noise increments is decomposed by Haar wavelets based on Mallat algorithm. Considering the correlation of detail coefficients, a bank of Wiener filters are used to estimate the detail coefficients to reconstruct DFGN considering the estimated approximation coefficients in the coarsest scale in the minimum mean square sense. Then, the reconstructed DFGN is used to restore FBM. In the digital simulation, in light of the restoration mean square error, we show that the suppose that the correlation of detail coefficients and the approximation coefficients in the coarsest scale for any Hurst could be avoided is unrealistic. Moreover, we calculate the estimation root mean square error of the hurst parameter of the restored FBM to show the validity of our algorithm.     

自相似业务流复用特性分析

现有研究将复用的自相似业务流Hurst参数值确定为各个业务流中最大的H参数值，与业务流的其他性质无关，这一结论用于网络设备的设计不利于网络资源的有效利用。本文采用简单近似估算，并用分形布朗运动模型生成自相似业务流，彩用小波分析方法估计Hurst参数值。实验结果表明，由于复用合成业务流的渐近自相似的本质，在可以观测的时间尺度范围内业务流的Hurst参数比这理论预测值小；在一定的序列长度下，复用流的Hurst参数的不仅和最大Hurst参数业务流有关，还受到其它业务流，特别是业务流的方差系数所表现出的短时突发性影响，因此对合成业务流的自相似参数具有重要的影响。     

An improved Hurst parameter estimator based on fractional Fourier transform

A fractional Fourier transform (FrFT) based estimation method is introduced in this paper to analyze the long range dependence (LRD) in time series. The degree of LRD can be characterized by the Hurst parameter. The FrFT-based estimation of Hurst parameter proposed in this paper can be implemented efficiently allowing very large data set. We used fractional Gaussian noises (FGN) which typically possesses long-range dependence with known Hurst parameters to test the accuracy of the proposed Hurst parameter estimator. For justifying the advantage of the proposed estimator, some other existing Hurst parameter estimation methods, such as wavelet-based method and a global estimator based on dispersional analysis, are compared. The proposed estimator can process the very long experimental time series locally to achieve a reliable estimation of the Hurst parameter.     

A unified study of burst assembly in optical burst switching networks

This article presents a comprehensive study of burst assembly in optical burst switching (OBS) networks. This investigation aims to provide a unified study of burst assembly algorithms. After analyses of conventional assembly algorithms, we present an adaptive burst assembly algorithm: data-length time-lag product assembly algorithm. Numerical results show that the proposed algorithm takes the real-time traffic state into account to adapt burst length and its injected intervals. It obtains a more acceptable performance. Moreover, we investigated the impact of burst assembly applied at the edge of OBS networks. We analyze the Hurst parameter of the input traffic to the output traffic that results from the burst assembly. Two formulas are presented to describe changes of the Hurst parameter. Theoretical results are validated through extensive simulations in time-based, length-based, and adaptive assembly algorithms, respectively.     

DFA在海杂波标度特性分析及目标检测中的应用

采用消除趋势波动分析(DFA)方法,研究了非平稳海杂波的标度特性及其参数表征问题,并提出了一种基于标度特性差异的目标检测算法。首先,在海杂波建模为分数布朗运动模型的基础上,对比分析了DFA和波动分析(FA)得出的标度特性,并初步解释了交叉标度现象出现的机理。然后,在假定相邻尺度范围内海杂波与理想分形模型相吻合的前提下,引入分段标度指数来表征海杂波标度特性随尺度的变化关系,分析结果表明,在特定的尺度范围内该参数可以有效区分目标单元与海杂波单元。为此,以该尺度范围内的分段标度指数均值为检验统计量设计了一种新的目标检测算法,并对尺度区间范围的选取、检测性能等问题进行了分析。实测数据中的检测性能证实了检测算法的有效性。      

A simulation study of adaptive burst assembly algorithms in optical burst switched networks with self-similar traffic sources

It has been extensively demonstrated that the traffic pattern in today’s Internet is Self-Similar. Burst assembly algorithms utilized in optical burst switched (obs) edge routers can be used to reduce the degree of self-similarity. This work investigates the impact of time and size-based burst assembly algorithms utilized in obs edge routers, on the self-similarity level of the output traffic. Both static and adaptive algorithms are examined. Our study is based on the opnet simulation tool focusing on the characteristics of the output traffic in the presence of self-similar input traffic. To estimate the Hurst parameter of the aggregated input and output traffic streams, we have applied various Hurst parameter estimators. The performance impact of the burst assembly algorithms in terms of burst assembly delay and its jitter is also assessed. Our study has shown that the burst assembly mechanism at the obs edge router reduces the self-similarity level of the output traffic and that this reduction depends on the parameters of the algorithm. Our results reveal that the proposed adaptive burst assembly algorithm performs better comparing to its non-adaptive counterpart.