下一代网络中业务感知模型研究

下一代网络（NGN）是业务驱动的网络,提供具有不同QoS要求的IP业务是NGN需要解决的关键问题,感知网络中的各种业务流对NGN的管理和安全具有重要意义。本文通过以对VoIP和P2P为代表的下一代网络业务有效识别、控制和管理技术的研究,从业务识别、业务管理等方面提出了网络业务识别系统,设计出应用于下一代网络的业务感知模型,能有效解决下一代网络由于缺乏应用层业务感知而导致的网络管理、网络安全等问题。     

中国移动在高教园区的宽带接入策略

在分析高教园区传统宽带接入方式的基础上,结合目前出现的新技术,从竞争环境和园区业务分布发展出发,对比两种传输基础资源建设模式的优缺点,提出作为宽带业务的后入者（中国移动）在高教园区的建设策略,包括业务区块划分、无源光网络（PON）组网方式、光缆敷设方式及楼层分光器设置等。最后,总结宽带接入实施中存在的困难及处理办法,为今后移公司动在高教园区开展宽带接入提供了较好的模型。     

LTE系统中的PCC与QoS研究

在Release 8 LTE／SAE系统中，策略和计费控制（PCC）以及服务质量（QoS）在用户业务处理方法中占有非常重要的地位。文章分析了基本配置中的PCC功能和接口，描述了SAE承栽模型，并给出了LTE中要的Qos参数。     

在CATV网上开展自选影视（VOD）新业务

ＣＡＴＶ光纤／同轴电缆混合网络可以加以综合利用，开展ＶＯＤ业务。本文给出ＶＯＤ网络的一般结构，讨论了Ｔｉｍｅ Ｗａｒｎｅｒ公司的“全功能服务网”方案，并对在上海市ＣＡＴＶ网上开展了ＶＯＤ业务提出了一些设想。     

基于环形结构的联想记忆网络

本文针对ＢＡＭ和ＴＡＭ网络处理数据串联想时的困难和不足，提出了一种基于环形结构的联想记忆网络，称为环形联想记忆网络（ＣＡＭ），给出了网络的拓扑结构和网络的三种基本联想模式，讨论了存储网络连接权所需要的存储量，并与ＢＡＭ和ＴＡＭ联想记忆网络进行了比较，最后给出了实验研究的结果。     

下一代光网络的发展和关键技术

近年来，随着经济的发展和计算机的普及，Internet业务出现了爆炸性的增长，现有的网络应付大量的数据业务和各种类型的服务业务已显得有些力不从心。为了适应未来发展的需求，并针对IP业务的主导地位和光传输技术的发展，下一步不仅要提高传输链路的带宽和利用率，还要提高光网络的智能性，以满足IP业务对传输和交换系统的要求。因此我们提出发展以IP层和光层融合的下一代智能光网络（OTN）。并针对下一代光网络的发展和关键技术进行了简单的阐述。     

Hopfield神经网络在B-ISDN路由选择中的应用

宽带综合业务数字网（Ｂ－ＩＳＤＮ）采用异步转移模式（ＡＴＭ）作为其传输技术,引入了虎通道（ＶＰ）和虚通路（ＶＣ）概念。本文针对Ｂ－ＩＳＤＮ中的ＶＰ路由问题,提出了一种基于Ｈｏｐｆｉｅｌｄ神经网络的ＶＰ路由选择算法,给出了神经网络能量函数的表示方法及神经元的状态方程。计算机模拟结果表明,本算法能根据网络的物理结构和业务需求情况,快速、有效地实现ＶＰ路由选择,提高网络的生存性。     

下一代网络的核心技术——软交换

随着信息服务与应用的兴起和发展，电信网络逐步从承载单一语音业务的独立网向承载多种业务的下一代网络演进。而运营商则必须在充分保护现有投资的前提下，设法改变其网络设计，以适应迅速增长的数据业务。这种改变的核心是利用分布式的体系结构，将语音和数据汇聚在同一个无缝网络中，通过采用接入、呼叫控制和电信应用程序分离的结构，实现在现有网络资源上提供更灵活、适用性更强的业务，同时具备更强的管理功能。这种网络结构就是期待中的下一代网络的基奉框架，而软交换则是其中的核心技术。     

VoIP网络的可调节允入控制分析

随着VoIP技术的广泛使用，有限的网络容量显得弥足珍贵，从而引入了可调节允入控制的概念。介绍了对IP网络中可调节允入控制的看法以及提高VoIP通信服务质量(QoS)的几种业务模型，参照这几种业务模型进一步提出了实现可调节允入控制的不同途径，并介绍了每种途径的机制和特征，最后对EMBAC在VoIP网络中的应用做了简单分析。     

Hopfield神经网络在B—ISDN路由选择中的应用

宽带综合业务数字网（Ｂ－ＩＳＤＮ）采用异步转移模式（ＡＴＭ）作为其传输技术,引入了虎通道（ＶＰ）和虚通路（ＶＣ）概念。本文针对Ｂ－ＩＳＤＮ中的ＶＰ路由问题,提出了一种基于Ｈｏｐｆｉｅｌｄ神经网络的ＶＰ路由选择算法,给出了神经网络能量函数的表示方法及神经元的状态方程。计算机模拟结果表明,本算法能根据网络的物理结构和业务需求情况,快速、有效地实现ＶＰ路由选择,提高网络的生存性。     

Modeling video traffic using wavelets

In this letter we establish a wavelet model for video traffic. Different from the existing methods which model the video traffic in the time domain, we model the wavelet coefficients in the wavelet domain. The strength of the wavelet model includes: (1) an unified approach to model both the long-range and the short-range dependence in the video traffic simultaneously; (2) a computationally efficient method for developing the model and generating high quality video traffic; and (3) feasibility of performance analysis using the model     

交通场景中有监督序学习拥挤度排序算法

为自动分析交通场景的拥挤度与速度属性,提出基于有监督序学习的交通场景拥挤度排序计算模型,利用监督学习思路分别学习交通拥挤度和平均速度两个属性的排序函数。在交通拥挤度排序模型中,首先提取每帧训练图像的Gist特征,而对于平均速度的排序模型,首先提取视频运动信息,然后再分别提取Gist特征,最后引入改进的Ranking SVM投影模型,学习得到拥挤度和速度的排序模型。在三组交通视频数据集的实验结果表明提出的排序模型准确度、稳定性更高。      

Traffic Volume Prediction Based on Multi-Sources GPS Trajectory Data by Temporal Convolutional Network

Predicting urban traffic volume is of great significance to traffic management and urban construction. An accurate prediction model can help drivers optimize driving routes, allocate resources reasonably and reduce urban traffic congestion. Most of the existing studies do not consider the complex nonlinear spatio-temporal relationship. In the spatial dimension, they do not consider the impact of regional semantics and regional interactions. In the temporal dimension, they ignore the impact of long-term historical information and key time points. Aiming at the complexity of traffic data, in this paper, we design a ResNet-TCN model to predict the urban traffic volume. Firstly, we construct and extract features from the vehicle GPS tracking and external information, such as velocity, time, location and weather. Then, we obtain regional semantic information by the ResNet model and combine the weights of the regional division with the average vehicle velocity into a two-channel matrix. We extract the key features of the matrix sequence and predict the velocity by the TCN model. Finally, we estimate the traffic volume through a traffic volume inference model in the traffic field. We conduct a large number of experiments on the actual dataset of Chengdu and compare our model with the existing models. The experimental results show that our method has better performance on prediction accuracy.

     

A flexible dynamic traffic model for reverse link CDMA cellular networks

In this paper, we focus on the reverse link traffic analysis of a code-division multiple-access (CDMA) cellular network in dynamic environments. In this respect, we propose a new and flexible traffic model, which takes into account the interference-limitedness attribute of CDMA capacity as well as its soft-handoff feature. This new traffic model is developed according to an interference-based call admission control (ICAC) method and a geographical structure with three regions. The main advantage of this traffic model is in its flexibility when we consider different traffic conditions including time-varying status of traffic in the neighboring cells.     

一种基于瑞利分布的VBR视频流的小波模型

本文提出了一种新型的视频业务流模型,以Haar小波的多分辨率分析为基础,在尺度空间和小波空间分别建模,然后通过小波反变换得出仿真业务流.在最"粗"的尺度空间里,我们根据视频流的概率分布特点,采用基于瑞利(Rayleigh)分布的AR模型对尺度系数建模;在各个小波空间里,采用一般的高斯不相关小波模型(WIG,Wavelet Independent Guassian)建模.由于在尺度空间和小波空间针对各自的特点作了不同的处理,本文模型不但能较好拟合复杂业务流在各个时间尺度的概率分布特性,也能拟合其长时相关的特性.另外,在多尺度排队分析(MSQ,MultiScale Queue)的框架下,我们还推导出了基于本文模型的排队分析的理论结果.最后,通过对实际视频业务流数据仿真实验与排队分析验证了本文模型的有效性.     

Approach to problem of minimizing network power consumption based on robust optimization

In this paper, a green and robust optimization model is introduced to minimize network power consumption, which allows fluctuations of traffic demands between source‐destination pairs in the network. Our model is based on the green hose model, where the traffic is bounded by just total outgoing and incoming amount at each node. In addition to the green hose model, we use the ellipsoidal uncertainty set to allow a different type of fluctuations in traffic demands. Here, the total amount of squared errors in traffic demands is bounded by a constant which controls the total admissible fluctuations over the network. Applying the conic duality, we formulate our model in the form of mixed‐integer second order cone programming (MISOCP) problem. Furthermore, we establish a relationship between our model and the green hose model with bound of link traffic (HLT) model, an developed version of the hose model known as the HLT. Numerical results demonstrate that each of the MISOCP problems can be solved to its optimality in a reasonable time by a general MISOCP solver, and that the proposed model has different tendency in performance with the green hose and green HLT models.     

Effective Traffic Grooming Algorithms in SONET/WDM Ring Networks

Much work has focused on traffic grooming in SONET/WDM ring networks. Previous work has considered many aspects of traffic grooming, including minimizing the number of ADMs, minimizing the number of wavelengths, considering different traffic models, using different network architectures, incorporating switching capability and so on. In this work, we study traffic grooming in unidirectional ring networks with no switching capability under both uniform traffic and non-uniform traffic models to reduce electronic multiplexing costs. Based on the clustering notion, we derive a general and tighter lower bound for the number of ADMs required in traffic grooming under the uniform all-to-all traffic model. This bound reduces to special cases obtained in previous work. We also derive general, tighter, and closed form lower bounds for the number of ADMs required under two non-uniform traffic models: the distance-dependent traffic model and the non-uniform symmetric traffic model. Cost-effective multi-phase algorithms that exploit traffic characteristics are then designed and studied to efficiently groom traffic streams under different traffic models. Our numerical and simulation results show that the proposed multi-phase algorithms outperform existing traffic grooming algorithms by using a fewer number of ADMs. Our algorithms in several cases also achieve the lower bounds derived.     

A new traffic model for backbone networks and its application to performance analysis

In this paper, we present a new traffic model constructed from a random number of shifting level processes (SLP) aggregated over time, in which the lengths of the active periods of the SLP are of Pareto or truncated Pareto distribution. For both cases, the model has been proved to be asymptotically second‐order self‐similar. However, based on extensive traffic data we collected from a backbone network, we find that the active periods of the constructing SLPs can be approximated better by a truncated Pareto distribution, instead of the Pareto distribution as assumed in existing traffic model constructions. The queueing problem of a single server fed with a traffic described by the model is equivalently converted to a problem with a traffic described by Norros' model. For the tail probability of the queue length distribution, an approximate expression and upper bound have been found in terms of large deviation estimates and are mathematically more tractable than existing results. The effectiveness of the traffic model and performance results are demonstrated by our simulations and experimental studies on a backbone network. Copyright © 2007 John Wiley & Sons, Ltd.     

Bayonet-corpus: a trajectory prediction method based on bayonet context and bidirectional GRU

Predicting travel trajectory of vehicles can not only provide personalized services to users, but also have a certain effect on traffic guidance and traffic control. In this paper, we build a Bayonet-Corpus based on the context of traffic intersections, and use it to model a traffic network. Besides, Bidirectional Gated Recurrent Unit (Bi-GRU) is used to predict the sequence of traffic intersections in one single trajectory. Firstly, considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections, this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections: inspired by the probabilistic language model, a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence, so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections. This algorithm maps vehicle trajectory nodes into a high-dimensional space vector, blocking complex structure of real traffic network and reconstructing the traffic network space. Then, the bayonets sequence in real traffic network is mapped into a matrix. Considering the trajectories sequence is bidirectional, and Bi-GRU can handle information from forward and backward simultaneously, we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.     

Traffic Capacity of UMTS System in Reverse Link

UMTS can be connected to data networks. So Internet traffic services such as WWW browsing, email, ftp, SMTP, etc should be handled by UMTS. Each of the traffic services has some specific properties but all of them obey a basic ON/OFF model. For a traffic service there is some unused (OFF) times between their transmitted packets which we may use them for other services to increase the traffic capacity of the network. In this paper we propose a new method on the Internet traffic evaluation in an interference limited UMTS-WCDMA system. In this method first we calculate ON and OFF time durations of the above traffic services based on the model presented in 3GPP then their activity factors. Secondly we introduce new capacity curves such as The Number of Web browsing users/Email users/ftp users/Telnet users and Fax users versus voice users. These are applicable in traffic planning for wireless systems.