Link reliability based hybrid routing for tactical mobile ad hoc network

Tactical mobile ad hoc network （MANET） is a collection of mobile nodes forming a temporary network, without the aid of pre-established network infrastructure. The routing protocol has a crucial impact on the network performance in battlefields. Link reliability based hybrid routing （LRHR） is proposed, which is a novel hybrid routing protocol, for tactical MANET. Contrary to the traditional single path routing strategy, multiple paths are established between a pair of source-destination nodes. In the hybrid routing strategy, the rate of topological change provides a natural mechanism for switching dynamically between table-driven and on-demand routing. The simulation results indicate that the performances of the protocol in packet delivery ratio, routing overhead, and average end-to-end delay are better than the conventional routing protocol.

A highly reliable embedding algorithm for airborne tactical network virtualization

The evolution of airborne tactical networks(ATNs)is impeded by the network ossification problem.As a solution,network virtualization(NV)can provide a flexible and scalable architecture where virtual network embedding(VNE)is a key part.However,existing VNE algorithms cannot be optimally adopted in the virtualization of ATN due to the complex interference in aircombat field.In this context,a highly reliable VNE algorithm based on the transmission rate for ATN virtualization(TR-ATVNE)is proposed to adapt well to the specific electromagnetic environment of ATN.Our algorithm coordinates node and link mapping.In the node mapping,transmission-rate resource is firstly defined to effectively evaluate the ranking value of substrate nodes under the interference of both environmental noises and enemy attacks.Meanwhile,a feasible splitting rule is proposed for path splitting in the link mapping,considering the interference between wireless links.Simulation results reveal that our algorithm is able to improve the acceptance ratio of virtual network requests while maintaining a high revenue-to-cost ratio under the complex electromagnetic interference.

Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data rate.We consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)density.Such user centric deployment of mmWave SBSs inevitably incurs correlation between UE and SBSs.For a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave communication.By using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power association.For UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy efficiency.We also provide Monte Carlo simulation results to validate the accuracy of the derived expressions.Furthermore,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave HCNets.Our results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

Predicting Reliability of Tactical Network Using RBFNN

A description of the reliability evaluation of tactical network is given, which reflects not only the non-reliable factors of nodes and links but also the factors of network topological structure. On the basis of this description, a reliability prediction model and its algorithms are put forward based on the radial basis function neural network (RBFNN) for the tactical network. This model can carry out the non-linear mapping relationship between the network topological structure, the nodes reliabilities, the links reliabilities and the reliability of network. The results of simulation prove the effectiveness of this method in the reliability and the connectivity prediction for tactical network.

Proxy-Based Hierarchical Distributed Mobility Management for Tactical Networks

An important requirement in a military domain is a highly reliable mobility management method,especially when components of the networks aremoving in tactical network environments.To increase reliability,themobility management technology of the tactical network should be able to reflect the characteristics of the tactical network,such as a limited environment,failure,and hierarchical unit structure.In this paper,we propose a proxy-based hierarchical distributedmobilitymanagement scheme,which is highly focused on tactical networks.Considering the characteristics of tactical networks,the proposed scheme is composed of the following:1)a proxy-based method,2)a distributed mobility management method that synchronizes a mobility database between entities,and 3)a method of managing mobility by dividing the tactical network into upper and lower layers.Mathematical analysis and modeling and simulation results demonstrate that the method outperforms the existing state-of-the-art method in overcoming entity failure,handover cost,and delay in tactical environments.

Integrating Strategic and Tactical Rolling Stock Models with Cyclical Demand

In the transportation industry, companies position rolling stock where it is likely to be needed in the face of a pronounced weekly cyclical demand pattern in orders. Strategic policies based on assumptions of repetition of cyclical weekly patterns set rolling stock targets;during tactical execution, a myriad dynamic influences cause deviations from strategically set targets. We find that optimal strategic plans do not agree with results of tactical modeling;strategic results are in fact suboptimal in many tactical situations. We discuss managerial implications of this finding and how the two modeling paradigms can be reconciled.

Research on the Dissemination Mechanism and Guiding Tactics of Public Opinion in Catastrophic Event Network

This paper conducts the analysis on the dissemination mechanism and guiding tactics of public opinion in catastrophic event network. Opinion evolution mechanism can be roughly divided into two classes. One is the belief of people based on their neighbors, on the basis of the public opinion is in the social network of acquaintances. Such networks are mostly using cellular automata model for data simulation, the results of numerical simulation are speci？ c to stabilize near the critical value show that the system will reach a critical stable state. The network information collection is the source of network public opinion monitoring its breadth and depth determine the monitoring results for the clear theme of public opinion information collection. Under this basis, this paper proposes the novel idea of making the dissemination mechanism easier. The proposed idea is novel and necessary, the effectiveness is proved via the theoretical analysis.

战术网络中基于资源负载均衡和服务优先级的业务调度策略

针对战术网络环境的多样化业务和网络异构性、高动态特性导致的网络负载易失衡的问题,提出了一种基于资源负载均衡和服务优先级的业务调度策略。通过所承载业务类型的优先级感知,基于通信网络当时可用资源等约束,将业务按需调度到战术通信网络的通信资源池,达到通信网络的自动负载均衡和资源可靠合理利用,提高业务服务质量,并保证高优先级业务可靠传输。通过仿真,验证了该策略算法可大大提高网络吞吐量和资源利用效率。

面向战术通信的SDN南向接口轻量化方法

随着无人装备和智能技术在现代战争中的广泛应用,对战场通信网络的带宽、服务质量、实时性等方面的要求也越来越高。将软件定义网络的技术思想引入战术通信网络,能够给战术网络的运维管理、资源调度等方面的能力带来显著提升。但由于战术通信环境在带宽、时延、机动性等方面与民用通信环境存在较大差异,最初面向民用场景设计的软件定义网络无法直接应用到战术通信场景中,需要解决南向接口控制开销大等问题。因此,在标准OpenFlow规范的基础上,提出了从可靠UDP承载、消息聚合机制、南向交互模式优化等方面,对南向接口进行轻量化设计,有效降低南向接口控制开销,并通过仿真验证了所提方案的可行性,满足战术场景下的通信需求。

量子遗传算法在无线自组网频率分配中的应用

论文将量子遗传算法应用于地面应急通信车辆无线自组网节点的使用频率分配,通过分析无线自组网节点频率的约束条件,建立频率分配的数学模型,并设计了使用量子遗传算法进行频率分配的的工作流程,对经典遗传算法库GALib进行扩展改进,增加了量子遗传算法的实现,通过模拟典型的无线自组网网络环境,比较经典和量子遗传算法的分配结果,量子遗传算法的分配收敛速率优于经典遗传算法,可以实现无线自组网节点频率灵活、高效的快速分配。

一种融合多源动态时空特征的战术互联网流量预测模型

战术互联网流量具有极强的动态时空特征,且与天气、高程等外部特征紧密相关,现有的网络流量预测模型不能很好地提取其复杂特征,提出了一种融合多源动态时空特征的战术互联网流量预测模型。首先,将外部特征与流量特征融合为多源特征;然后,提取当前时刻网络流量的空间特征,并对随时间变化的卷积权重迭代更新得到不同时间片下的空间特征信息;最后,通过时间卷积聚合当前和历史时刻的空间信息以预测下一时刻的多源动态时空流量。相比单一的基础模型,该方法在平均绝对误差(MAE)、均方根误差(RMSE)和决定系数(R 2)三种评估指标中效果均更好。

网络流量密态匿迹与体系对抗综述

组织性复杂、计划性高效和指向性明确的高级持续性威胁(APT)攻击是我国面临的主要威胁之一,APT组织的行动隐匿化、攻击常态化趋势愈加明显。近年来,我国掌握主要的APT活动越来越困难,与APT组织将攻击行为匿迹于正常信息服务和网络活动中,以及将攻击流量藏匿于正常通信流量中不无关系。这种高隐蔽攻击行为隐匿后所处的状态,称之为密态。如何检测发现密态行为并实施体系对抗,是当前网络空间防御要解决的瓶颈性难题之一。文章从澄清网络空间高级攻击活动的流量传输隐匿技术机理角度出发,围绕匿名通信链路构建和流量特征行为检测两个维度,提出流量密态匿迹对抗的研究框架和对抗能力评估指标体系,全面阐述近年来相关研究工作进展、研究方法及解决方案,以期探索网络空间密态对抗能力新的发展方向。

基于自适应模型预测的智能网联车辆主动避障研究

本文以智能网联车为研究对象,设计一种自适应模型,采用MPC控制算法对整车行车过程中的避障策略和相关行为进行精准智能调度。借助PSD三角测距方式,对道路中的相关障碍物位置信息尽心采集,基于AT89C52模块进行数据处理与控制,对车辆进行主动避障预测控制。经在不同工况下进行仿真实验表明,基于MPC自适应模型预测的智能网联车辆主动避障策略具有很好的应用价值,避障精准,验证了该方法的有效性与实用性。

面向双机编队战术识别的时空图卷积网络

空战中的双机编队战术识别具有重要的研究意义和应用价值。为了提高双机编队战术识别的准确率和鲁棒性,采用深度学习技术,提出了一种面向双机编队战术识别的时空图卷积网络。首先,对双机编队协同作战序列数据进行了时空图构建;然后,通过空域图卷积网络模块、时域图卷积网络模块和通道注意力模块提取了双机编队战术的时空域特征;最后,利用分类器对双机编队战术进行了识别。试验结果表明,该网络可有效识别双机编队战术。

非完备信息下的超视距空战双机协同战术识别

针对超视距(BVR)空战过程中,受探测装置性能限制和敌方干扰等原因,导致目标信息易缺失,从而难以实时准确地识别敌方协同空战战术的问题,提出了一种基于动态贝叶斯网络(DBN)与参数学习的超视距空战双机协同战术识别方法。分析了超视距空战条件下的双机协同战术特征,根据长机和僚机的职能分工、当前态势及机动动作,构建了识别网络模型;为提高模型对双机协同战术的识别概率,采用期望最大参数学习方法优化网络参数;基于自回归模型对缺失目标信息进行修补,提出非完备信息下的双机协同战术识别推理算法。通过开展空战对抗仿真实验,验证了双机协同战术识别方法对于非完备信息下的超视距空战双机协同战术具有较高的识别概率和较好的实时性。

基于循环神经网络的SPMA协议信道状态智能检测改进算法

为实现对时敏目标的快速探测、定位和打击,战术瞄准网络技术(TTNT)对战术信息接入信道、交互传输的实时性、可靠性提出高要求。TTNT采用基于统计优先的多址接入(SPMA)协议,通过周期性计算统计平均的思想,估计当前信道状态,控制战术信息接入信道的时机。该思想仅适用于流量相对平稳的情况,在流量非平稳时会导致较大的信道状态检测误差。针对此问题,引入流量预测技术,提出基于循环神经网络的SPMA协议信道状态智能检测改进算法。利用循环神经网络的学习特点学习历史流量数据的隐含特征,构建流量预测器对瞬时时刻的流量脉冲到达数进行实时预测,从而准确获取当前信道状态。实验结果表明:所提算法对信道状态的检测结果更接近真实值,显著降低了信道忙闲状态的误判率。

Tactical conflict resolution in urban airspace for unmanned aerial vehicles operations using attention-based deep reinforcement learning

Unmanned aerial vehicles(UAVs)have gained much attention from academic and industrial areas due to the significant number of potential applications in urban airspace.A traffic management system for these UAVs is needed to manage this future traffic.Tactical conflict resolution for unmanned aerial systems(UASs)is an essential piece of the puzzle for the future UAS Traffic Management(UTM),especially in very low-level(VLL)urban airspace.Unlike conflict resolution in higher altitude airspace,the dense high-rise buildings are an essential source of potential conflict to be considered in VLL urban airspace.In this paper,we propose an attention-based deep reinforcement learning approach to solve the tactical conflict resolution problem.Specifically,we formulate this task as a sequential decision-making problem using Markov Decision Process(MDP).The double deep Q network(DDQN)framework is used as a learning framework for the host drone to learn to output conflict-free maneuvers at each time step.We use the attention mechanism to model the individual neighbor's effect on the host drone,endowing the learned conflict resolution policy to be adapted to an arbitrary number of neighboring drones.Lastly,we build a simulation environment with various scenarios covering different types of encounters to evaluate the proposed approach.The simulation results demonstrate that our proposed algorithm provides a reliable solution to minimize secondary conflict counts compared to learning and non-learning-based approaches under different traffic density scenarios.

国家水网的理论内涵与战略策略关系

全面总结国内外水网建设实践与相关研究进展,系统分析国家水网概念内涵、体系构成、功能结构、优势特点,提出国家水网构建的基础理论与遵循原则。从普适含义和方法论角度,分析总体目标与基本策略的深刻涵义和二者的辩证关系。在此基础上,从国家水网构建的建设任务与技术路线、基本理念与具体举措、水网“纲、目、结”结构、水网层次体系、水网功能任务等角度,分析推进国家水网建设中需要重点把握的几方面关系,以期为国家水网建设提供战略支撑及策略储备。

面向战术边缘应用的SDN网络控制平面设计研究

软件定义网络(Software Defined Network,SDN)是实现网络智能化的基础与主要技术途径。当前,商用的SDN网络一般应用于数据中心与软件定义广域网(Software-Defined WAN,SD-WAN)场景,其协议设计着眼于宽带、有线、固定的使用环境,普遍存在网络开销大,无法适应动态组网,很难应用于窄带、弱连接的战术边缘场景。基于此,文中在主流商用SDN协议的基础上,从控制平面自动构建、轻量化协议设计与业务流量调度等方面设计了一套面向战术边缘应用的、轻量化的SDN网络控制平面接口协议,仿真验证了所提方案的优越性。文中的研究可为智能化战术信息网络的设计提供一定的参考。

低轨卫星系统终端战术互联网关键节点分析

基于架构复杂、节点规模大、路由协议开销大、网络拓扑变化快、业务类型多等特点的低轨卫星系统终端构成的复杂战术互联网,研究了一种基于网络拓扑重合度的关键节点识别方法用于识别战术互联网中的关键节点,并通过介数中心性推断节点的属性,对典型的战术互联网的拓扑结构完整性随节点排序删除后的变化以及网络介数中心性进行了仿真。结果表明,通过DCN值识别关键节点具有很好的效果,介数中心性对于正确识别节点的属性、上级节点、下级节点及同级节点有着非常好的效果。