考虑边聚类与扩散特性的信息传播网络结构优化算法

优化网络结构以促进信息在网络中传播一直是复杂网络研究的重点,网络中边的聚类特性和扩散特性对信息传播具有重要作用. K-truss分解算法是一种利用边的聚类特性识别网络关键节点的算法,然而K-truss算法会受到网络中局部聚类结果 (即相互连接的假核结构)的影响,而这些假核结构里的节点对信息扩散能力通常较弱.为此,本文提出一种衡量边扩散特性的指标,研究发现一些位于网络边缘的边具有很好的扩散性,但这类边的聚类很低,并不利于信息传播.通过同时考虑边的聚类特性和扩散特性之间的制约关系,提出一种信息传播网络结构优化算法.为了验证所提算法的有效性,使用该算法对四个真实的网络进行结构优化,并使用经典的独立级联模型来验证网络结构优化前后信息传播的有效范围.结果表明:使用提出的算法优化后的网络拓扑可以有效提高信息传播范围;并且,优化后的网络其叶子节点数目降低、聚类系数降低以及平均路径长度降低.     

利用节点效率评估复杂网络功能鲁棒性

为了克服现有复杂网络鲁棒性研究模型只考虑节点失效的局部影响性和网络拓扑鲁棒性的缺陷, 提出了一种利用节点效率来评估复杂网络功能鲁棒性的方法. 该方法综合考虑节点失效的全局影响性, 利用网络中节点的效率来定义各节点的负载、极限负载和失效模型, 通过打击后网络中最终失效节点的比例来衡量网络的功能鲁棒性, 并给出了其评估优化算法. 实验分析表明该方法对考虑节点负载的复杂网络功能鲁棒性的评定可行有效, 对于大型复杂网络可以获得理想的计算能力.     

基于蚁群优化的IP over WDM光网络动态生存性映射算法

随着光网络规模的不断扩大以及网络体系结构的扁平化过程加速,网络传输的可靠性和实时性以及生存性路面算法本身面临新的挑战本文在单点割集的松弛生存性约束条件下,结合动态业务下的IP over WDM光网络的特点.借助蚁群优化算法的强鲁棒性和记忆能力,通过改变与调整蚁群优化算法结构中的信息素结构与其更新机制,在动态路由选择过程中,引入网络可生存性约束信息改进路由选择概率计算机制,来实现IP over WDM光网络动态生存性映射的路由策略,与其他同类算法相比,该算法不再存储网络备用路由集和物理拓扑自身庞大的割集关系,在保证网络生存性效果的前提下,通过割集松弛条件有效降低了计算的时间复杂度,提高算法收敛速度,适应网络实时性要求,仿真结果表明:与传统最短路算法比较,该算法能有效提高动态光网络的可生存性映射性能和网络的资源利用效率,并降低业务阻塞率.     

基于蚁群优化的IP over WDM光网络动态生存性映射算法

随着光网络规模的不断扩大以及网络体系结构的扁平化过程加速,网络传输的可靠性和实时性以及生存性路面算法本身面临新的挑战.本文在单点割集的松弛生存性约束条件下,结合动态业务下的IP over WDM光网络的特点,借助蚁群优化算法的强鲁棒性和记忆能力,通过改变与调整蚁群优化算法结构中的信息素结构与其更新机制,在动态路由选择过程中,引入网络可生存性约束信息改进路由选择概率计算机制,来实现IP over WDM光网络动态生存性映射的路由策略.与其他同类算法相比,该算法不再存储网络备用路由集和物理拓扑自身庞大的割集关系,在保证网络生存性效果的前提下,通过割集松弛条件有效降低了计算的时间复杂度,提高算法收敛速度,适应网络实时性要求.仿真结果表明:与传统最短路算法比较,该算法能有效提高动态光网络的可生存性映射性能和网络的资源利用效率,并降低业务阻塞率.     

一种基于离散数据从局部到全局的网络重构算法

网络的结构和功能彼此相互影响,网络上的功能往往体现为网络上的动力学过程,网络上的动力学过程通过网络中的行为表象数据进行体现.因此,根据网络上可观测的相关数据对网络结构进行重构将成为可能.本文拟解决如何根据网络上可观测的离散数据还原网络拓扑结构的问题,提出了在网络局部利用每一条离散数据对应节点的相似程度来推测节点间发生连边的可能性,通过多条离散数据重构网络各个局部拓扑并将由多条数据得到的局部拓扑进行叠加,最终重构出整个网络的全局拓扑结构的算法.为了验证算法的可行性与准确性,在小世界、无标度和随机网络中进行了网络重构实验,通过在三种不同类型及不同规模的网络中进行网络重构实验可以看出,网络重构算法在不同类型网络中的表现也不同,且网络的平均度值也会影响网络重构算法对数据的要求.为了验证算法的适用性,对三个实际网络进行了网络重构实验,结果显示算法能够适用实际较大规模网络的重构.该算法具有很好的适用性和准确度,适合不同类型网络的拓扑结构重构场景.     

IP/GMPLS/Optical多层网络生存性机制的协调

通过对多层网络生存性问题、独立的网络生存性配置方案和多层网络生存性配置方案的研究分析,基于快速发展的GMPLS技术提出了IP/GMPLS/Optical多层网络结构,明确了多层网络生存性方案存在的问题,并利用GMPLS的统一控制平面进一步提出了基于层间协调机制的集成多层网络生存性方案,获得了优化的多层网络故障恢复能力.     

基于领域相似度的复杂网络节点重要度评估算法

节点重要性度量对于研究复杂网络鲁棒性与脆弱性具有重要意义.大规模实际复杂网络的结构往往随着时间不断变化,在获取网络全局信息用于评估节点重要性方面具有局限性.通过量化节点局部网络拓扑的重合程度来定义节点间的相似性,提出了一种考虑节点度以及邻居节点拓扑重合度的节点重要性评估算法,算法只需要获取节点两跳内的邻居节点信息,通过计算邻居节点对之间的相似度,便可表征其在复杂网络中的结构重要性.基于六个经典的实际网络和一个人工的小世界网络,分别以静态与动态的方式对网络进行攻击,通过对极大连通系数与网络效率两种评估指标的实验结果对比,证明了所提算法优于基于局域信息的度指标、半局部度指标、基于节点度及其邻居度的WL指标以及基于节点位置的K-shell指标.     

基于一种高生存性RPR网络的路由研究

由标准弹性分组环衍生出的高生存性RPR网络弥补了单环结构中带宽浪费，利用率不高的缺点。基于此多环RPR网络提出了一种路由算法，它将网络中源节点到目的节点的路由过程转换成对相邻节点参考距离的选择，适应了网络的高生存性的特点。通过OPNET完成对路由算法模型的仿真，证明该算法实现了高生存性RPR网络的路由功能，而且具有转发跳数少，路由表结构简单，网络开销小等特点。     

利用邻域“结构洞”寻找社会网络中最具影响力节点

识别复杂网络中的关键节点对网络结构优化和鲁棒性增强具有十分重要的意义. 经典的关键节点测量方法在一定程度上能够辨识网络中影响力节点, 但存在一定局限性: 局部中心性测量方法仅考虑节点邻居的数目, 忽略了邻居间的拓扑关系, 不能在计算中反映邻居节点间的相互作用; 全局测量方法则由于算法本身的复杂性而不能应用于大规模社会网络的分析, 另外, 经典的关键节点测量方法也没有考虑社会网络特有的社区特征. 为高效、准确地辨识具有社区结构的社会网络中最具影响力节点, 提出了一种基于节点及其邻域结构洞的局部中心性测量方法, 该方法综合考虑了节点的邻居数量及其与邻居间的拓扑结构, 在节点约束系数的计算中同时体现了节点的度属性和“桥接”属性. 利用SIR(易感-感染-免疫)模型在真实社会网络数据上对节点传播能力进行评价后发现, 所提方法可以准确地评价节点的传播能力且具有强的鲁棒性.     

基于自规避随机游走的节点排序算法

评估复杂网络系统的节点重要性有助于提升其系统抗毁性和结构稳定性. 目前, 定量节点重要性的排序算法通常基于网络结构的中心性指标如度数、介数、紧密度、特征向量等. 然而, 这些算法需要以知晓网络结构的全局信息为前提, 很难在大规模网络中实际应用. 基于自规避随机游走的思想, 提出一种结合网络结构局域信息和标签扩散的节点排序算法. 该算法综合考虑了节点的直接邻居数量及与其他节点之间的拓扑关系, 能够表征其在复杂网络系统中的结构影响力和重要性. 基于三个典型的实际网络, 通过对极大连通系数、网络谱距离数、节点连边数和脆弱系数等评估指标的实验对比, 结果表明提出的算法显著优于现有的依据局域信息的节点排序算法.     

基于贝叶斯网络的认知引擎设计与重配置

以往的通信行为指导系统未来通信, 以满足用户需求并适应环境变化, 是认知无线电系统的核心所在, 为此提出了一种基于贝叶斯网络的认知引擎, 用于解决在复杂多变的电磁环境与用户需求条件下, 认知无线电系统参数自适应调整的问题. 通过对系统过去通信行为样本数据, 进行结构学习和参数学习建立认知引擎, 将系统当前环境状态和用户需求信息经预处理作为推理的证据, 应用引擎决策出系统此时最佳的工作参数, 完成系统参数重构. 本文利用OPNET工具建立一个移动无线网络完成仿真实验, 仿真结果表明该认知引擎能有效地使移动无线网络适应环境变化, 改善端到端通信性能, 进一步验证了建模方法的可行性.     

Social-aware relay selection and energy-efficient resource allocation for relay-aided D2D communication

How to improve the flexibility of limited communication resources to meet the increasing requirements of data services has become one of the research hotspots of the modern wireless communication network. In this paper, a novel social-aware motivated relay selection method is put forward to allocate the energy efficiency (EE) resources for the device-to-device (D2D) communication. To improve system flexibility, a D2D user is selected to act as a relay named D2D-relay instead of the traditional cellular relay. The optimal relay selection strategy is formulated by searching the maximum trust value that is obtained by assessing the link stability and social connections between two users. Then, the resource allocation problem, which turns out to be a mixed-integer nonlinear fractional programming (MINLFP) problem, is solved by maximizing the total EE under physical constraint and social constraint synthetically. To improve the solution efficiency, a novel iterative algorithm is proposed by integrating the Dinkelbach theory and Lagrange dual decomposition method. Simulation results demonstrate the effectiveness of the proposed scheme. Compared with the existing social-blind and social-aware schemes, it significantly improves the probability of successful relay selection and total EE of the D2D pairs.     

Energy Efficiency of User-Centric,Cell-Free Massive MIMO-OFDM with Instantaneous CSI

In the user-centric, cell-free, massive multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, a large number of deployed access points (APs) serve user equipment (UEs) simultaneously, using the same time–frequency resources, and the system is able to ensure fairness between each user; moreover, it is robust against fading caused by multi-path propagation. Existing studies assume that cell-free, massive MIMO is channel-hardened, the same as centralized massive MIMO, and these studies address power allocation and energy efficiency optimization based on the statistics information of each channel. In cell-free, massive MIMO systems, especially APs with only one antenna, the channel statistics information is not a complete substitute for the instantaneous channel state information (CSI) obtained via channel estimation. In this paper, we propose that energy efficiency is optimized by power allocation with instantaneous CSI in the user-centric, cell-free, massive MIMO-OFDM system, and we consider the effect of CSI exchanging between APs and the central processing unit. In addition, we design different resource block allocation schemes, so that user-centric, cell-free, massive MIMO-OFDM can support enhanced mobile broadband (eMBB) for high-speed communication and massive machine communication (mMTC) for massive device communication. The numerical results verify that the proposed energy efficiency optimization scheme, based on instantaneous CSI, outperforms the one with statistical information in both scenarios.     

Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.     

Green and fast DSL via joint processing of multiple lines and time–frequency packed modulation

In this paper, strategies to enhance the performance, in terms of available data-rate per user, energy efficiency, and spectral efficiency, of current digital subscriber lines (DSL) are proposed. In particular, a system wherein a group of copper wires is jointly processed at both ends of the communication link is considered. For such a scenario, a resource allocation scheme aimed at energy efficiency maximization is proposed, and, moreover, time–frequency packed modulation schemes are investigated for increased spectral efficiency. Results show that a joint processing of even a limited number of wires at both ends of the communication links brings remarkable performance improvements with respect to the case of individual point-to-point DSL connections; moreover, the considered solution does represent a viable means to increase, in the short term, the data-rate of the wired access network, without an intensive (and expensive) deployment of optical links.     

A survey on UAV placement optimization for UAV-assisted communication in 5G and beyond networks

With the increase in capacity demands and the requirement of ubiquitous coverage in the fifth generation and beyond wireless communications networks, unmanned aerial vehicles (UAVs) have acquired a great attention owing to their outstanding characteristics over traditional base stations and relays. UAVs can be deployed faster and with much lower expenditure than ground base stations. In addition, UAVs can enhance the network performance thanks to their strong line-of-sight link conditions with their associated users and their dynamic nature that adapts to varying network conditions. Optimization of the UAV 3D locations in a UAV-assisted wireless communication network was considered in a large body of research as it is a critical design issue that greatly affects communication performance. Although the topic of UAV placement optimization was considered in few surveys, these surveys reviewed only a small part of the growing literature. In addition, the surveys were brief and did not discuss many important design issues such as the objectives of the optimization problem, the adopted solution techniques, the air-to-ground channel models, the transmission media for access and backhaul links, the limited energy nature of the UAV on-board batteries, co-channel interference and spectrum sharing, the interference management, etc. Motivated by the importance of the topic of UAV placement optimization as well as the need for a detailed review of its recent literature, we survey 100 of the recent research papers and provide in-depth discussion to fill the gaps found in the previous survey papers. The considered research papers are summarized and categorized to highlight the differences in the deployment scenario and system model, the optimization objectives and parameters, the proposed solution techniques, and the decision-making strategies and many other points. We also point to some of the existing challenges and potential research directions that have been considered in the surveyed literature and that requires to be considered     

基于时变小世界模型的航空网优化评估

航空网的优化设计对于优化资源配置、改善航运效率、提高航空公司竞争力等具有重要的现实意义.而航线结构与航班计划密不可分.本文首先讨论了航空网的时变特征,揭示了航班频率与航线距离之间的时空耦合关联.通过构建时变空间小世界模型,揭示了时变条件下网络的最优结构指数与时空耦合强度的惟一约束关系.以运行总成本最小化为主要优化目标,提出了一种可以快速评估航线结构优化情况的方法.该方法能根据网络客流分布情况快速推算出航线网络的最优拓扑及相应的航班频率分布.并用2001—2010年中国航空网络数据对此方案进行实证研究,发现预测与实际数据基本符合,并逐渐趋于稳定.这一方法能把复杂问题简单化,对各个航空公司每年的航线航班调整是否合理,现有的航空网络是否在逐步优化做出动态评估.分析航空网络的发展趋势,从而对未来的优化提供建议.     

基于分组交换的量子通信网络传输协议及性能分析

量子纠缠交换能够建立可靠的量子远程传输信道, 实现量子态的远程传输. 然而, 基于纠缠交换的量子信道要求网络高度稳定, 否则会浪费大量纠缠资源. 为节省纠缠资源, 本文根据隐形传态理论, 提出了一种基于分组交换的量子通信网络传输协议, 建立了发送量子态所需的纠缠数目与所经过的路由器数、链路错误率的定量关系, 并与纠缠交换传输协议进行了比较. 仿真结果表明, 在链路错误率为0.1% 时, 分组传输协议所使用的纠缠数目少于纠缠交换的数目, 另外, 随着错误率的升高, 分组传输协议所需的纠缠数比纠缠交换协议明显减少. 由此可见, 基于分组交换的量子通信网络传输协议在网络不稳定时, 能够节省大量纠缠资源, 适用于链路不稳定的量子通信网络.     

基于感兴趣区域的低复杂度多描述编码方法

针对在不可靠网络中传输包含感兴趣区域图像的问题,提出了一种低复杂度、高效、鲁棒的编码方法.该编码方法建立在多路径网络传输结构的基础上,采用多描述编码思想,把图像信息分成几个描述;为了保护感兴趣区域,在每个描述中对这部分区域增加冗余度,并分别通过不同的路径传输.该编码方法实现了信号传输鲁棒性和编码效率的调节控制,并与各种高性能的图像或视频压缩技术相兼容.实验表明,该方法在提高图像在网络上传输鲁棒性的同时,也能有效地保护感兴趣区域,重建图像的感兴趣区域及背景区域的信噪比指标都有了明显的提高.     

An effective method to improve the robustness of small-world networks under attack

In this study, the robustness of small-world networks to three types of attack is investigated. Global efficiency is introduced as the network coefficient to measure the robustness of a small-world network. The simulation results prove that an increase in rewiring probability or average degree can enhance the robustness of the small-world network under all three types of attack. The effectiveness of simultaneously increasing both rewiring probability and average degree is also studied, and the combined increase is found to significantly improve the robustness of the small-world network.Furthermore, the combined effect of rewiring probability and average degree on network robustness is shown to be several times greater than that of rewiring probability or average degree individually. This means that small-world networks with a relatively high rewiring probability and average degree have advantages both in network communications and in good robustness to attacks. Therefore, simultaneously increasing rewiring probability and average degree is an effective method of constructing realistic networks. Consequently, the proposed method is useful to construct efficient and robust networks in a realistic scenario.