一种改进的自适应动态资源调配算法

为了改进在分布式环境下用于QoS控制的基于模糊逻辑的动态资源调配算法（FDRA）,提出了基于双阈值和双指针的自适应动态控制资源算法——EFDRA（Enhanced FDRA）,解决了当网络资源紧张时,FDRA算法更新指数上升过大的问题。仿真结果证明,该算法以牺牲少量的阻塞率指标为代价,换来了更新指数指标的显著改善,提高了网络的可扩展性能。     

基于多智能体模糊深度强化学习的跳频组网智能抗干扰决策算法

为提高复杂电磁环境下跳频异步组网的抗干扰性能,该文提出一种基于集中式训练和分散式执行框架的多智能体模糊深度强化学习(MFDRL-CTDE)算法。针对多种干扰并存的复杂电磁环境和异步组网结构,设计了相应的状态-动作空间和奖赏函数。为应对智能体之间的相互影响和动态的环境,引入集中式训练和分散式执行(CTDE)框架。该文提出基于模糊推理系统的融合权重分配策略,用于解决网络融合过程中各智能体的权重分配问题。采用竞争性深度Q网络算法和优先经验回放技术以提高算法的效率。仿真结果表明,该算法在收敛速度和最佳性能方面都具有较大优势,且对多变复杂电磁环境具有较好的适应性。     

基于VEPPSO-EXTRA混合算法的分布式直接定位技术

相对于集中式直接定位技术,分布式直接定位算法具有计算复杂度小和通信代价小等优点,但存在定位精度损失的问题。针对于此,该文提出一种基于VEPPSO-EXTRA混合算法的分布式直接定位技术。首先,基于子空间融合的直接定位算法,推导其分布式优化的数学模型;其次,基于多种群联合进化的思想,提出一种基于向量评估的并行粒子群算法(VEPPSO)实现全局寻优,由此得到辐射源迭代初始值;最后,引入分布式精确一阶算法(EXTRA)求解最终位置以降低分布式计算带来的精度损失。实验结果表明,相较于现有的分布式直接定位算法,该技术能解决定位精度损失的问题,且其计算复杂度与通信代价低于对应的集中式直接定位算法。     

基于模糊控制的启发式工作流引擎负载均衡策略

针对分布式工作流引擎中的负载均衡问题,提出了一种新的算法,即启发式模糊算法,将传统控制理论中的模糊控制和启发式策略应用到了工作流负载均衡中。最后在一个基于JINI的分布式网络系统中实现了该算法,并且给出了其与其他几种经典算法进行比较的实验结果。结果表明,启发式模糊算法提高了工作流引擎服务的整体响应时间,使得资源得到了更加合理的利用。     

5G移动网络中终端直连方式下用户发送功率自适应优化方法

作为对传统移动通信网络的有力补充,终端直连(device to device,D2D)的概念被引入未来5G移动通信网络中.为了解决D2D方式中无基站中转的通信问题及非基站控制用户无序发送所带来的复杂干扰环境及覆盖盲区情况下发送资源的自适应优化分配问题,提出了一种基于博弈理论与模糊逻辑理论的发送功率自适应分配算法,用以充分利用系统的功率与频谱资源,同时降低功率分配对其他用户产生的干扰影响.通过对计算机仿真结果的分析,提出的算法相对于传统的平均功率分配算法及分布式注水算法有一定的性能提高.     

基于差分码和块处理方法的GPS信号快速捕获算法

综合时域滑动相关法、差分匹配滤波器算法和基于块处理的频域捕获算法等三种方法各自的优点,提出了一种新的基于差分C/A码和块处理方法的GPS信号时域捕获算法,在不损失处理增益的前提下既加快了捕获速度,又降低了资源消耗,实现了捕获时间和资源占用二者的最佳结合。实验结果表明此算法是行之有效的。     

基于基因表达式程序设计的电路优化算法研究

基于Xilinx Virtex-Ⅱ系列FPGA控制逻辑块(CLB)矩阵特点以及每一个控制逻辑块能实现任何2输入1输出的逻辑功能的特点,提出了一种基于基因表达式程序设计的电路优化算法。在该算法中染色体由按线性方式连接的逻辑单元矩阵组成,采用的遗传操作包括变异和杂交,并利用真值表进行适应度评估。实验证明,所得到的电路结构优于传统方法。     

基于分布式模糊逻辑推理的WSN分簇算法

针对无线传感器网络(WSN)中均衡分簇问题,提出一种基于模糊逻辑推理的WSN分布式分簇算法(DFLC)。利用分布式模糊逻辑控制器选择根节点,以能量大小、中心性、距基站的距离、跳数和节点密度5个参数作为分布式模糊逻辑控制算法的输入。为网络中的中间节点分配模糊逻辑推理引擎,根据自身和相邻节点的信息进行判断,选择发送质量最高子节点的回复消息给根节点,减少了消息传输数量。仿真实验表明,在产生消息数量、能源消耗、存活节点数、容错性、负载平衡等方面,DFLC算法都优于LEACH,ACAWT,Gupta和CHEF算法。     

分布式检测系统的模糊有序统计融合算法

为提高基于二元局部判决的分布式检测系统的检测性能，研究了基于模糊隶属度函数的分布式检测系统融合算法，提出了模糊有序统计融合准则。通过对肼传感器进行分布式检测系统仿真，结果表明：模糊有序统计融合准则在均匀环境中能获得比基于二元判决准则更好的检测性能，也好于同样采用模糊隶属度函数的求和准则的检测性能，且在多目标环境下获得了较强的适应性。     

基于模糊感觉强度的无规则模糊逻辑系统的仿真设计

基于模糊逻辑方法在心理语言学研究领域的有效性,该文提出基于模糊感知强度和韦伯定律的一类新的无规则模糊逻辑系统及其自适应控制应用的方法。首先,应用心理物理学中的概念,模糊逻辑系统的知识库是利用模糊感觉强度来描述专家经验感受;模糊推理后应用广义韦伯定律进行解模糊得到系统输出。然后,针对一类非线性系统,利用构造的新的无规则模糊逻辑系统进行自适应控制设计,得到相应的控制器和参数自适应律。最后,通过Duffing混沌系统仿真算例验证了该文方法的可行性和有效性。     

Joint Subchannel,Rate and Power Allocation in OFDMA-Based Cognitive Wireless Mesh Network

In this paper, the problem of resource allocation in an Orthogonal Frequency Division Multiple Access-based Cognitive Wireless Mesh Network (CWMN) is addressed. The objective is to maximize the total utilities in a CWMN, which is defined as any increasing, concave and twice differentiable function of the end-to-end flow rate, by jointly allocating each link’s rate, power and subchannels under the constraints of multiple primary users’ Interference Temperature and multiple access interference. First, a centralized resource allocation algorithm is developed based on the Column Generation approach, and shown to be optimal. So it can perform as a criterion for designing other algorithms. Secondly, considering the applicability of algorithm in distributed system, a near-optimal distributed algorithm is proposed, which allocates subchannel based on routing information at first, and then jointly allocates the resource of rate and power. Finally, the simulation results validate the centralized and distributed algorithms, and show that better performance can be achieved than the conventional algorithm.     

一种新的LTE系统下行功率控制算法

从提高系统吞吐量性能和公平性性能角度出发,提出一种新的LTE系统下行功率控制算法。该算法考虑扇区边缘用户受到的不同衰落,首先为边缘资源块分配发射功率,然后采用调度算法为边缘用户分配资源块,最后为中心资源块分配发射功率。仿真结果表明,该算法获得的系统吞吐量性能和公平性性能均优于传统算法。     

LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN

The localization of the sensor nodes is a fundamental problem in wireless sensor networks. There are a lot of different kinds of solutions in the literature. Some of them use external devices like GPS, while others use special hardware or implicit parameters in wireless communications.In applications like wildlife localization in a natural environment, where the power available and the weight are big restrictions, the use of hungry energy devices like GPS or hardware that add extra weight like mobile directional antenna is not a good solution.Due to these reasons it would be better to use the localization’s implicit characteristics in communications, such as connectivity, number of hops or RSSI. The measurement related to these parameters are currently integrated in most radio devices. These measurement techniques are based on the beacons’ transmissions between the devices.In the current study, a novel tracking distributed method, called LIS, for localization of the sensor nodes using moving devices in a network of static nodes, which have no additional hardware requirements is proposed.The position is obtained with the combination of two algorithms; one based on a local node using a fuzzy system to obtain a partial solution and the other based on a centralized method which merges all the partial solutions. The centralized algorithm is based on the calculation of the centroid of the partial solutions.Advantages of using fuzzy system versus the classical Centroid Localization (CL) algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for testing localization algorithms.With this simulator, it is demonstrated that the proposed method obtains less localization errors and better accuracy than the centroid algorithm.     

云雾混合网络下基于多智能体架构的资源分配及卸载决策研究

针对D2D辅助的云雾混合架构下资源分配及任务卸载决策优化问题,该文提出一种基于多智能体架构深度强化学习的资源分配及卸载决策算法。首先,该算法考虑激励约束、能量约束以及网络资源约束,联合优化无线资源分配、计算资源分配以及卸载决策,建立了最大化系统总用户体验质量(QoE)的随机优化模型,并进一步将其转化为MDP问题。其次,该算法将原MDP问题进行因式分解,并建立马尔可夫博弈模型。然后,基于行动者-评判家(AC)算法提出一种集中式训练、分布式执行机制。在集中式训练过程中,多智能体通过协作获取全局信息,实现资源分配及任务卸载决策策略优化,在训练过程结束后,各智能体独立地根据当前系统状态及策略进行资源分配及任务卸载。最后,仿真结果表明,该算法可以有效提升用户QoE,并降低了时延及能耗。     

虚拟计算环境下面向应用的基于信任的资源匹配模型

为了解决虚拟计算环境中的资源合理调度问题,提出了一种基于信任的资源匹配模型--“资源滑动窗口”模型。首先对资源的静态属性进行分类,然后依据基于时间窗的贝叶斯信任模型对资源节点进行评价,同时考虑资源的负载,动态划分其实时性能。最后综合评估静态和动态属性,确定调度资源分配。该模型为不同任务和属性的资源调度策略奠定了基础,实现了“合适的资源服务于合适的任务”的目的。仿真实验表明所提的模型相对传统的调度算法,具有更高的系统任务执行成功率和资源利用率。     

基于模糊聚类的云任务调度算法

为了更好地实践云计算提供廉价按需服务的宗旨,提出了一种在模糊聚类基础上,基于两级调度模式的任务调度(FCTLBS,fuzzy clustering and two level based task scheduling)算法,新算法设置用户调度和任务调度2个等级。对资源进行性能模糊聚类;根据任务参数计算资源偏好,使不同偏好任务在不同聚类中选择,缩小了选择范围,更好地反映了任务需求。仿真实验表明,本算法较之同类算法具备一定的优越性。     

Distributed dynamic fault-tolerant channel allocation for cellularnetworks

Efficient allocation of communication channels is critical for the performance of cellular systems. The centralized channel allocation algorithms proposed in literature are neither robust nor scalable. Several of these algorithms are unable to dynamically adjust to spatial and temporal fluctuations in channel demand (load). We present a distributed dynamic channel allocation (DCA) algorithm in which heavily loaded regions acquire a large number of communication channels, while their lightly loaded neighbors get assigned fewer channels. As the spatial distribution of channel demand changes with time, the spatial distribution of allocated channels adjusts accordingly. The algorithm described in this paper requires minimal involvement of the mobile nodes, thus conserving their limited energy supply. The algorithm is proved to be deadlock free, starvation free, and fair. It prevents cochannel interference and can tolerate the failure of mobile as well as static nodes without any significant degradation in service. Simulation experiments demonstrate that the performance of the proposed distributed dynamic algorithm is comparable to, and for some metrics, better than that of efficient centralized dynamic algorithms where the central switch has complete and latest information about channel availability. The major advantages of the proposed algorithm over its dynamic centralized counterparts are its scalability, flexibility, and low computation and communication overheads     

基于分布式网络的航天器真空热试验控制系统

传统的航天器真空热试验控制系统采用集中控制模式,系统精确度和可靠性较低,无法满足新型航天器的研制需求.为此设计了一套基于分布式网络、具备高可靠性和高控制精确度的控制系统.该控制系统所有终端均遵循以太网传输控制协议/因特网互联协议,以实现分布式的网络通信,并采用航天产品可靠性设计原则;同时在系统设计中采用增量式数字比例积分微分(PID)控制算法,运用模糊数学的基本理论和方法,设计了一种模糊自适应PID控制算法.控制系统中运用了数字信号处理技术,以保证控制能力的实现.经相关测试表明,该控制系统控制精确度高,动态特性良好,抗干扰能力强,可满足新型航天器研制需求.     

Joint subcarrier and power allocation for downlink cellular OFDMA systems by intercell interference limitation

This paper addresses resource allocation for sum throughput maximization in a sectorized two-cell downlink orthogonal frequency-division multiple-access (OFDMA) system impaired by multicell interference. It is well known that the optimization problem for this scenario is NP-hard and combinational, which is here converted to a novel sum throughput maximization problem in cellular OFDMA systems based on the intercell interference limitation. Then, three subclasses of this new problem are solved. By the first subclass, on the assumption that subcarrier allocation parameters are fixed, an algorithm for optimal power allocation is obtained. However, the optimal resource allocation requires an exhaustive search, including the optimal power allocation which cannot be implemented in practice due to its high complexity. By the second subclass, the problem is reduced to a single cell case where the intercell interference in each subcarrier is limited to a certain threshold. Based on the solution of the single cell problem, a distributed resource allocation scheme with the aim of small information exchange between the coordinated base stations is proposed. By the third subclass, the centralized resource allocation for two adjacent cells as a general problem is solved. Here, the algorithm allocates simultaneously the subcarriers and the power of the considered two cells while the resource allocation parameters of both cells are coupled mutually. The present study shows that distributed and centralized resource allocation algorithms have much less complexity than the algorithm used in the exhaustive search and can be used in practice as efficient multicell resource allocation algorithms. Simulation results illustrate the performance improvements of the proposed schemes in comparison to the schemes with no intercell interference consideration.     

基于自适应滑动窗的模糊场景行人快速检测

智能场景监控是公共安全领域研究的热点.然而当前公共场所监控视频大都比较模糊,如何能够在模糊场景下快速检测行人目标是一个亟待解决的问题.通过引入一个自适应尺寸的行人检测窗,以特定策略滑动扫描前景二值图像,并根据一系列行人轮廓判别条件获取行人目标,实现了对模糊监控场景的实时行人检测与人数统计.实验结果表明,与传统算法相比,该算法可以有效快速地检测出指定区域内的行人目标.