舰载C3I系统通信网络可靠性效能分析

C^3I系统通信网络效能评估的影响因素复杂多变,一直是个棘手的问题．C^3I系统通信网络可靠性评估是C^3I系统效能评估的一个重要方面,从C^3I系统通信网络的最小连通子网一网络生成树入手,依据图论的方法,进行网络生成树数目的计算,并建立了C^3I系统通信网络的可靠度的数学模型。     

通信网络的可靠性评估

通信网络的可靠性评估是一个难题。目前人们只对环型网和树型网进行可靠性分析。本文首先定义了通信网络的可靠性，依据图论提出了网络生成树的计算方法，以及网络可靠度的估算公式。最后给出了一个栅格型网的可靠度估算示例。     

基于机器视觉的通信网络数据可靠度计算方法

传统通信网络数据可靠度计算方法存在分组丢失率、封包投递率与标准数据相差较大的问题,为此,提出了基于机器视觉的通信网络数据可靠度计算方法.根据机器视觉通信原理构建了通信网络数据可靠度数学模型,评估通信网络数据的可靠度,评估过程中构建通信网络数据可靠度评估模型,采用了蒙特卡罗技术将评估模型和可靠度数学模型相结合,计算通信网...     

一种计算随机流网络可靠性的新算法

提出了一种计算随机流网络可靠性的新方法。通过一定的规则生成网络的状态树，使得每一个分支都是全序集合。在生成状态树的同时搜索每一个分支，对状态采用基于割集的方法进行判断。每个分支上的最小的有效状态就是网络的d-下界点。求得所有的d-下界点，进而求出网络的可靠性。     

基于强度排序的通信社区检测算法

针对当前电信网中如何有效刻画含权网络的真实特征,完善和发展相关复杂网络模型的难题,特别是对通信社区检测结果层次结构不清晰及运算复杂度高的问题,从复杂网络特征分析入手,设计了一种新的通信社区检测算法。该算法基于通信强度排序方法实现通信社区的有效检出,基于通信密度分布生成高分辨率层次嵌套树,通过距离矢量修剪嵌套树,实现社区稳定检测和层次结构分析同时降低计算复杂度。该算法使用真实网络数据进行了有效验证。     

军用通信网络可靠性设计技术

针对无法简单套用经典方法来研究复杂军用通信网可靠性设计这一难题,对通信网可靠性研究进行了分析,提出了详细的可靠性测度指标。基于军用通信网可靠性设计需求,建立了系统可靠性测度和数学模型,给出了一种快速的解析评估算法和适用于大型网络的模拟方法。仿真结果表明,该设计能够快速、有效地计算评估可靠性指标,为军用通信网络可靠性设计提供可行、有效的工程方法。     

可靠性约束下的无线Mesh网络拓扑控制优化方法

为合理规划无线Mesh网络,确保在节约无线资源前提下网络长期可靠地运行,该文定义了无线Mesh网络不同节点之间的归一化相对可靠性约束条件计算公式,建立了网络拓扑控制优化的模型,并配套研究了可靠性约束下拓扑控制的计算方法。该方法把Prim最小生成树方法融入到最大流最小割算法过程中,计算节点间每跳链路距离最短的不相交路径,通过不相交路径数与网络拓扑可靠性的相关性,得到可靠性约束下的拓扑优化。阐述了计算方法的正确性,并给出时间及空间复杂度。通过仿真验证及对比分析,在指定2×sqrt(N)对节点间为4条不相交路径及其他节点间2条不相交路径的可靠性约束条件下,不同网络规模的吞吐量及时延平均性能分别提升15.3%及20.1%,表明了可靠性约束下的拓扑控制方法更加合理性与灵活性,更能满足实际无线网络拓扑控制的要求。     

动态环境下嵌套移动网络的快速路由优化

针对嵌套移动网络在快速移动场景下,频繁切换带来的较长通信恢复收敛时间和网络内部节点通信的次路由优化问题,提出了一种使用树信息选项的快速路由优化方案.该方案通过使用树信息选项对RA消息进行扩展和新增本地绑定(LBU)机制,减少了在快速变化环境中由于切换造成的通信中断时间,并且能在移动网络内部节点交互业务增多时,保证路由的...     

基于区块链的计算机通信网络安全风险评估

为实现计算机通信网络的全面安全管控,及时掌握风险态势,设计基于区块链技术的计算机通信网络安全风险评估模型。以区块链技术为基础生成私有化数据区块链并加密所有的数据区块,通过这些数据区块采集计算机通信网络数据后,对这些数据进行汇总并生成网络风险项目账本;依据计算机通信网络数据,构建计算机通信网络安全风险评估指标体系,评估计算机通信网络安全风险。测试结果显示：该模型具备良好的风险计算性能,可获取各个等级的风险,应用性良好,为网络风险管控提供可靠的链上溯源信息。     

基于身份认证的安全量子中继器网络编码方案

本文将量子一次一密通信方法引入到量子中继器网络中,提出了基于身份认证的安全量子中继器网络编码方案.针对编码过程中存在的主动攻击问题,用一次一密的方式实现任意相邻节点通信过程中的身份认证,优化编码算法,最终在源节点与目的节点间生成量子纠缠态作为信道,构成量子隐形传态网络.方案分析表明,这种方案可以实现高可靠性、高安全性的远程量子通信.     

Hopfield neural networks for state estimation: parameters, efficient implementation and results

State estimation processes measurements and other information to find the network state vector. In this paper, state estimation is considered as an optimization problem to be solved with a Hopfield neural network. Several activation models for this network are simulated and compared. A new method is proposed that calculates the integration step parameter for this network in an autonomous way, eliminating the need for determining it in a manual way for each particular problem. This algorithm has been successfully tested for a wide range of electrical nets. Neural and classic analytical methods are compared.     

Equalization of rapidly fading channels: self-recovering methods

Blind estimation of time-varying (TV), rapidly fading channels is addressed, using a basis expansion approach. Each TV coefficient is expanded onto a basis and the expansion parameters are estimated for subsequent use in Viterbi or decision-feedback equalizers. Blind estimation of the expansion parameters is accomplished using higher order statistics. Identifiability of the channel is shown from second and fourth-order TV cumulants of the received signal. A cumulant matching criterion is adopted and instantaneous approximations are proposed in place of the nonstationary ensembles. Linear methods are also derived to initialize the nonlinear optimization procedure. Strong convergence of the proposed method is established. Finally, the method is tested on a simulated mobile radio channel with multipath     

Utilization of Location Information in Cognitive Wireless Networks

Location awareness is an essential characteristic of cognitive radios as well as networks. In this article a location awareness engine architecture is proposed for the realization of location awareness in cognitive radios and networks. The proposed architecture consists of location estimation and/or sensing, seamless positioning and interoperability, statistical learning and tracking, security and privacy, mobility management, and location-based applications. However, the focus of this article is on location-based applications where we demonstrate the utilization of location information in cognitive wireless networks by presenting some representative location-assisted network optimization applications such as location-assisted spectrum management, network planning and expansion, and handover. Our study unveils that location information can be used in cognitive wireless networks to optimize network performance. Possible solutions to the implementation issues are proposed, and the remaining open issues are also addressed.     

Monte Carlo Network Reliability Ranking Estimation

Topological optimization is an important problem in communication networks. Exact reliability optimization methods are restricted to small problems, or specialized network topologies. For larger problems, simulation-based approaches are more practical. In simulation-based optimization, one often needs to compare the reliability of a large number of similar networks. The traditional reliability estimation & rank process is the most time consuming step of most optimization algorithms. In this paper, a novel approach is proposed to directly estimate the reliability ranking of some edge relocated networks without the need to estimate their reliabilities. In the case study considered in this paper, the proposed Synchronous Construction Ranking method achieved over 30,000 times speedup over the traditional approach using the Merge Process estimation algorithm     

Efficient electromagnetic optimization of microwave filters and multiplexers using rational models

A method is presented for the efficient optimization of microwave filters and multiplexers designed from an ideal prototype. The method is based on the estimation of a rational function adjusted to a reduced number of samples of the microwave device response obtained either through electromagnetic analysis or measurements. From this rational function, a circuital network having the previously known topology of the microwave device is synthesized and compared to a circuital network with the desired response but including nonidealities. All of the process of analysis and model extraction can be seen as a model function that relates the physical parameters of the microwave device with the extracted circuital network parameters. Then, the error vector of the circuital parameters is used to generate a correction vector of the physical parameters through an estimation of the inverse of the Jacobian matrix of the complete model function. The Jacobian estimation is updated at each iteration of the optimization process with no need for additional evaluations of the model function. Two numerical examples of the proposed technique corresponding to the synthesis of a filter and a diplexer are presented, demonstrating the increased efficiency of the proposed technique with respect to direct electromagnetic optimization.     

小数据集BN建模方法及其在威胁评估中的应用

贝叶斯网络是数据挖掘领域的主要工具之一。在某些特定场合,如重大装备的故障诊断、地质灾害预测及作战决策等,希望用少量数据得到较好的结果。因此,本文针对小数据集条件下的贝叶斯网络学习问题展开研究。首先,建立基于连接概率分布的结构约束模型,提出I-BD-BPSO（Improved-Bayesian Dirichlet-Binary Particle Swarm Optimi-zation）结构学习算法;其次,建立单调性参数约束模型,提出MCE（Monotonicity Constraint Estimation）参数学习算法;最后,应用所提算法构建威胁评估模型并应用变量消元法进行推理计算。实验结果表明,在小数据集条件下,本文的结构学习算法优于经典的二值粒子群优化算法,参数学习算法优于最大似然估计、保序回归及凸优化算法,并能够构建有效的威胁评估模型。     

基于1D-Concatenate的信道估计DNN模型优化方法

为提高DNN模型在无线通信中信道估计精度,提出一种基于1D-Concatenate的信道估计DNN模型优化方法。该方法将Concatenate进行一维（1D）数据转换,以跳跃连接的方式引入DNN模型,抑制梯度消失问题,运用1D-Concatenate恢复网络训练过程中丢失的数据特征,提高DNN信道估计精度。为验证优化方法的有效性,选取较典型的基于DNN的无线通信信道估计模型进行对比仿真实验。实验结果表明,本文提出的优化方法对已有DNN模型的估计增益提升可达77.10%,在高信噪比下信道增益提升可达3 dB。该优化方法能有效提高DNN模型在无线通信中的信道估计精度,特别是高信噪比下提升效果显著。     

Optimal Routing for Wireless Mesh Networks With Dynamic Traffic Demand

Wireless mesh networks have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broadband Internet access. Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best routing solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. This paper presents an integrated framework for wireless mesh network routing under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By studying the traces collected at wireless access points, we first present a traffic estimation method which predicts future traffic demand based on its historical data using time-series analysis. This method provides not only the mean value of the future traffic demand estimation but also its statistical distribution. We further investigate the optimal routing strategies for wireless mesh network which take these two forms of traffic demand estimations as inputs. The goal is to balance the traffic load so that minimum congestion will be incurred. This routing objective could be transformed into the throughput optimization problem where the throughput of aggregated flows is maximized subject to fairness constraints that are weighted by the traffic demands. Based on linear programming, we present two routing algorithms which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate the traffic dynamics in mesh network routing.     

3G对网络规划和优化带来的全新挑战

与2G移动通信网络相比．3G移动通信网络的规划与优化有着非比寻常的不同和变化。首先表现在网络规划方法的不同．3G网络覆盖和容量之间的关系密不可分．相互影响。其次在网络优化方面除了与2G优化的共同点外．还存在着3G特有的典型问题。全面阐述了3G对网络规化和优化带来的全新挑战。提出3G网络规划与优化过程中的核心问题。     

基于时空多特征融合网络的三维人体姿态估计北大核心CSCD

目前,常见的三维(3D)人体姿态估计算法在表征学习上取得很好的效果,但是在人体骨架关节点处依然存在估计精度不佳等问题,因此,如何从单目RGB图像中利用冗余的二维(2D)姿态序列时空信息来估计人体姿态的有效方式是一个研究的难点。本文提出一种基于时空多特征融合网络的三维人体姿态估计算法,具体是结合一种图像外观信息和运动时序信息时空多特征融合层级方法,该方法利用一种紧凑的卷积神经网络(convolutional neural network, CNN)学习时空信息将二维关节点位置信息建模为三维关节点位置。实验结果表明,本文所提出的方法能实现较为先进的端对端姿态估计精度,而且不需要任何后处理阶段的姿态优化方法,本文得到的姿态估计在平均精度上得到有效的提升,证明本文方法能够有效提高人体姿态估计的准确性。