基于TDMA的星间链路时隙分配设计与仿真评估

基于TDMA的星间链路体制组网灵活,传输效率高,已成为今后星间链路的一种发展趋势。对于TDMA的星座网络而言,其时隙分配是影响业务时延的重要因素,本文就一种双层混合型星座提出了一种时隙分配方案,并对星座网络的传输进行了仿真,分别从广播和单播两种业务的角度针对星间链路传输性能的时延指标进行了评估分析。     

OPNET和STK联合的空间光骨干网络仿真设计

空间光骨干网络是我国空间信息网络的核心。为了对空间光骨干网络中的协议体系、网络架构和关键技术进行可行性验证，开发了基于OPNET和STK(satellite tool kit，卫星工具包)联合的空间光骨干网络仿真平台。设计了由4颗GEO卫星组成的天基骨干网、由10颗LEO卫星组成的天基接入网；开发了由物理层到应用层的光网络协议仿真模型和LEO、GEO、地面站3种节点进程模型，对光骨干网络进行了仿真测试。仿真结果表明:空间光骨干网络最大时延为5.48 s，最小为0.35 s，网络全局平均时延为1.06 s，地面站实时吞吐量最大值为0.88 Gb/s，最小为0.3 Gb/s，证明该平台可准确模拟空间光骨干网络功能，满足空间业务高效传输的需求，为空间光骨干网络性能验证提供仿真平台支持。     

基于OpenFlow协议的光与IP网络统一控制架构的研究(英文)

软件定义网络是近几年才提出的基于集中式控制技术的新型网络架构.本文提出了一个基于软件定义网路为网络中的OpenFlow协议异构网络跨域的统一控制架构.此架构在实现对物理层光交换节点与电交换节点统一控制的基础上,以最小化减小运营商损失的同时最大化保留IP网路与光网络自身的优势为目的,利用SDN实现多域中数据中心服务的统一控制与分配,最大化保留了IP网络无连接交换以及光网络大容量高速率的优势.     

基于OPNET的星地光网络性能测试仿真平台研究

为了测试星地光网络的性能,设计了一种基于OPNET的星地光网络性能测试仿真平台。介绍了平台的总体技术架构,探讨了星间链路建立的条件。采用最大接入仰角与最长服务时间加权的方式,完成星地链路的卫星接入服务;按照切换呼叫优先的策略完成业务传输中的卫星切换服务,确保星地链路不间断的通信能力。根据最小链路代价和首次命中原则,实现星地光网络的路由与波长的动态分配。最后,利用平台的OPNET软件测试了三种星座下的星地光网络性能,测试结果表明：在LEO、MEO和GEO星座下,星地光网络的网络阻塞率分别为10%、40%和54%,平均网络时延分别为0.1s、0.07s和0.054s。测试结果对星地光网络的工程应用具有一定的指导意义。     

基于惯性网络的分布式信息融合算法

分布式导航系统是飞机多传感器导航系统设计的新概念,可以大幅提高系统导航性能和容错水平,并能动态配置传感器功能,但是目前并无完善的信息融合算法与之对应;文章在构建惯性传感器网络的基础上,将多个低成本惯性传感器系统配置在飞机的多个位置以作为网络节点,设计了分阶段处理的分布式信息融合算法,综合利用各节点所测量的惯性信息,最后得到本节点的局部状态估计;通过仿真实验表明,采用此方法,有效降低了导航滤波估计误差,因此,系统导航性能及容错能力得到大幅提高。     

一种新型准静态拓扑LEO/MEO卫星光网络设计

设计了一种新型的LEO/MEO全球覆盖双层卫星光网络,该结构具有稳定的MESH型连接关系,不仅满足卫星光通信捕获追踪和瞄准ATP(acquisition tracking and pointing)要求,还能简化星载处理和交换,降低路由开销。推导了同层和异层间星间链路空间位置表达式。数值仿真结果表明该网络相邻卫星间距离、方位角和俯仰角变化范围小,非常适合卫星激光链路,可作为构建未来卫星激光网络的参考。     

无线认知网络中拓扑控制方法研究

作为下一代通信网络,无线认知网络已成为当前的研究热点。由于节点的移动性,无线网络拓扑结构动态变化,拓扑控制一直是无线网络的难点问题。通过借鉴移动自组织网络(MANETS)中的拓扑控制方法,提出了无线认知网络中基于博弈论和认知功能相结合的拓扑控制方法。无线认知节点能够通过主动决策调节自身节点位置,在保证网络连通性的基础上实现网络覆盖面积最大。仿真实验结果验证了方法的有效性和收敛性。     

恒星背景噪声对星间激光链路跟瞄系统影响的仿真分析

在恒星辐照度谱模型的基础上,根据卫星轨道理论及恒星数据库建立仿真实验系统,对低轨卫星与中继卫星间(LEO_GEO)、中继卫星间(GEO_GEO)、低轨卫星间(LEO_LEO)三种典型的星间激光链路进行了仿真研究,深入分析了恒星背景噪声对卫星光通信终端跟瞄子系统的影响规律、背景光功率等特性。仿真结果表明:对于视场较大的LEO终端,恒星进入视场的概率约为7%~10%,而对于视场较小的GEO终端,恒星进入视场的概率仅为0.05%,恒星背景光功率约为10-13~10-11W。该项工作对星间激光链路设计、光通信系统参数优化等工作具有一定指导意义。     

基于Sarsa算法和蚁群优化的监测网络路由控制设计

为了克服已有监测网络路由控制方法所具有的传输路径长和能量消耗高的缺点,设计了一种基于Sarsa算法和蚁群优化算法混合模型的路由控制策略;首先,建立了以MA(Mobile Agent)为基础的路由控制模型,并以访问时间和能耗消耗为基础设计了目标函数,然后,基于改进的蚁群算法设计了一种从数据发送节点到Sink节点的路由设计方法,采用节点之间的距离作为冗余度标准获取合理的下一跳邻居节点集,并采用Sarsa算法来对各路径的信息素进行更新,最后,对基于Sarsa算法和蚁群优化算法混合模型的路由控制算法进行了描述;在NS2环境下进行仿真实验,结果证明文中方法能有效实现网络路由控制,最优路由的获取过程的总仿真时间为300ms,是一种适用于监测网络路由控制的有效方法。      

面向导航星座的星间传输协议测试系统设计

星间网络协议是卫星通信的基础。目前,随着网络技术的发展,网络协议的功能越来越复杂,使得协议的测试越来越重要。星间网络协议中处于核心地位的传输层协议是在测试中受到重点关注。由于导航星座的条件限制,现阶段难以对其使用的传输协议进行整网测试,因此需要一个协议测试系统来满足测试需求。在分析了导航星座通信网络特点与星间传输协议的基础上,设计了传输协议测试系统。该测试系统对星间网络协议的传输层协议进行实现,并通过数据总线来模拟数颗卫星之间的数据通信。测试结果表明,测试系统能够满足星间传输协议的测试需求。     

QoS-aware resource allocation and fault tolerant operation in hybrid SDN using stochastic network calculus

Mobile edge computing (MEC) is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the users. With the advent of the next-generation mobile networks, researchers have turned their attention to various aspects of edge computing in an effort to leverage the new capabilities offered by 5G. So, the integration of software defined networking (SDN) and MEC techniques was seriously considered to facilitate the orchestration and management of Mobile Edge Hosts (MEH). Edge clouds can be installed as an interface between the local servers and the core to provide the required services based on the known concept of the SDN networks. Nonetheless, the problem of reliability and fault tolerance will be of great importance in such networks. The paper introduced a dynamic architecture that focuses on the end-to-end mobility support required to maintain service continuity and quality of service. This paper also presents an SDN control plane with stochastic network calculus (SNC) framework to control MEC data flows. In accordance with the entrance processes of different QoS-class data flows, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Compared to other solutions investigated in the literature, the proposed approach exhibits a significant increase in the throughput distributed over the active links of mobile edge hosts. It also proved that the outage index and the system’s aggregate data rate can be effectively improved by up to 32%.     

Data center application oriented control architecture in multi-domain optical networks

With the rapid development of cloud computing, data center application based on considerable storage and computing has become one of the most important service types. Currently the high performance computing facilities and large-capacity storage devices are highly distributed in different locations. Then how to make full use of the current data center mainly depends on the effective joint scheduling of application layer and network layer resources. According to the rigid requirement of data center application, a novel convergence control architecture, i.e. Service-Oriented Group Engine (SOGE) framework is proposed in multi-domain optical networks based on DREAM architecture, and also the corresponding resource demand model (RDM) is built. A resource joint scheduling algorithm (RJSA) for application layer and network layer resource is proposed and implemented based on SOGE framework. Moreover, the SOGE framework and resource joint scheduling algorithm are validated and demonstrated on the test-bed based on DREAM architecture.     

Design and performance evaluation of a metro WDM storage area network with IP datagram support

Storage area networks (SANs) are becoming an important part of optical MANs (metropolitan area networks). Growing storage and business-continuity needs; high-bandwidth, low latency requirements for SANs; storage infrastructure consolidation; and post-9/11 regulatory issues are among the several driving factors to push this trend. We, in this paper, consider a metro wavelength division multiplexing (WDM) SAN that allows the transmission of variable packet size such as Internet protocol (IP) datagram and evaluate its performance by means of discrete-event simulation. The network is based on one fixed transmitter and multiple fixed receivers. Beginning with an introduction and the context of this work, we describe the network and node architectures; and introduce the medium access control (MAC) protocols. Subsequently, using the Poisson and self-similar traffic, we present and discuss performance of the proposed network architecture in terms of throughput and queuing delay under symmetric and asymmetric traffic scenarios. The simulation results suggested that the proposed architecture is suitable for SAN applications which demand low queuing delay and high throughput.     

An efficient packet parser architecture for software-defined 5G networks

Software-defined networks (SDN) has emerged with the capability to program in order to enhance flexibility, management, and testing of new ideas in the next generation of networks by removing current network limitations. Network virtualization and functionalization are critical elements supporting the delivery of future network services, especially in 5G networks. With the integration of virtualization and functionalization, network resources can be provisioned on-demand, and network service functions can be composed and chained dynamically to cater to various requirements. 5G networks are expected to rely heavily on SDN, which has been widely applied in core network design. To have a software-defined 5G network, not only is new spectrum and interface needed from SDN, but also a programmable and efficient hardware infrastructure is required. Admittedly, hardware components and infrastructure play an important role in supporting 5G networks. In other words, the software-defined 5G network data plane must have the required flexibility and programmability to support upcoming needs and technologies. Technological solutions need to respond to actual requests in infrastructure. Packet parsers in the data plane of software-defined 5G networks are one of the most important components because of the variation in the type of network headers and protocols. Each SDN switch needs to identify headers for processing input packets in the data plane, where the packet parser operates. Multiple implementations of packet parsers have been done on different substrates that occupy large hardware resources and areas on chip. However, they are not suitable for software-defined 5G networks. Certain architectures have been presented for packet parsing, aimed at accelerating the process of header parsing, however no attention has been paid toward reducing the area and the volume of the needed hardware resources and programmability in the data plane. This paper presents a new and efficient architecture for packet parsers on Field Programmable Gate Arrays (FPGA), called Efficient FPGA Packet Parser (EFPP) in a designed software-defined 5G network. This architecture emphasizes the removal of Ternary Content Addressable Memory (TCAM) to decrease hardware resources and efficiency in the data plane. Moreover, this architecture uses the chip’s processing speed and reconfiguration capabilities to support new protocols and network headers while maintaining flexibilities on software-defined 5G networks. EFPP is applied to chips on FPGA Xilinx ZedBoard Zynq, and the resources consumed around 7.5% LookUp Table, 1.9% Flip-Flops, and 5.8% of the memory. EFPP was also more area efficient. According to our results, EFPP would reduce the area and volume of hardware compared to other peer works.     

Image feature extraction by dynamic neural filtering and phase-only joint transform correlation

Neural network-based image processing algorithms present numerous advantages due to their supervised adjustable properties. Among various neural network architectures, dynamic neural networks, Hopfield and Cellular networks, have been found inherently suitable for filtering applications. Combining supervised and filtering features of dynamic neural networks, this paper presents dynamic neural filtering technique based on Hopfield neural network architecture. The filtering technique has also been implemented by using phase-only joint transform correlation (POJTC) for optical image processing applications. Filtering structure is basically similar to the Hopfield neural network structure except for the adjustable filter mask and 2D convolution operation instead of weight matrix operations. The dynamic neural filtering architecture has learnable properties by back-propagation learning algorithm. POJTC presents significant advantages to achieve the operation of summing the cross-correlation of bipolar data by phase-encoding bipolar data in parallel. The image feature extraction performance of the proposed optical system is reported for various image processing applications using a simulation program.     

A Fault-Tolerant Transmission Scheme in SDN-Based Industrial IoT (IIoT) over Fiber-Wireless Networks

Driven by the emerging mission-critical and data-intensive applications in industrial intelligent manufacturing, the software-defined network (SDN) based fiber-wireless access network (FiWi) is attracting considerable attention thanks to its capability of central control and large bandwidth. However, the heterogeneity of the network leads to new challenges, since the packet loss can be caused either by the poor channel quality of wireless links or network component failures. A novel and adaptive mechanism combining sparse random linear network coding with parallel transmission (SNC-PT) is proposed to achieve the fault-tolerance against high packet loss rate and any network element malfunction. We illustrate the benefits of using the SNC-PT mechanism to improve fault tolerance by characterizing the network performance with respect to the completion time and goodput along with its relationship to channel quality and node failures. We show that significant performance gains can be obtained in comparison with conventional uncoded transmission based on transmission control protocol (TCP). The simulation results show that the SNC-PT mechanism is fault-tolerant, while it can significantly shorten the data transmission completion time to at least 12% of the baseline and increase the goodput by about 10% compared to other coding schemes such as random linear network coding.     

基于SDN架构的电力通信网络质量感知技术研究

电力工业是国民经济的基础支柱,其通信网络是电力生产、经营和管理的一个重要组成部分。针对现通信网中业务流实时监测的难题,提出引入一种基于SDN架构设计的新型流量感知监测技术方案,经实际网络延迟数据与RLI算法、MDI指标中的DF参数、VTD指标互作对比。为此搭建基于NetMag可编程网络设备的实验环境通过验证及建模对比,均达到预期效果。论证该项质量感知技术能为电力通信网提供可视化、实时、智能的运维平台作部署,这将使运维管理工作更加主动、有效,有利于提高网络运行的效率与稳定性。