基于Bertrand和Cournot博弈的频谱共享模型

针对多个主用户和单个次用户的场景,提出了一种基于Bertrand和Cournot混合博弈的频谱共享模型.主用户拥有两种具有一定替代性的频谱资源,并根据频谱市场需求决定采用Bertrand或Coumot博弈,通过相应地调整频谱数量和价格策略来获得最大收益.最后,推导了该模型静态博弈纳什均衡表达式,分析了动态博弈的稳定性,并通过非完全信息动态博弈的仿真结果验证了模型的正确性.     

一种新的频谱共享模型

考虑认知无线电系统中多个主用户与单个次用户共享频谱的问题,提出了基于Cournot博弈的频谱共享模型.依次推导了基于Cournot与Bertrand博弈的频谱共享模型的静态纳什均衡,给出了动态博弈获取纳什均衡解的分布式算法,分别分析了只考虑主用户收益与考虑社会效益这两种情况中,信道质量与频谱的可替代性对两种博弈模型的影响,给出了它们的适用范围.最后通过仿真结果验证了本文模型的可行性.     

一种自适应频谱成形器:在超宽带认知无线电中的应用

超宽带脉冲无线电(UWB-IR)在高速通信、高精度定位及穿墙透地等重要领域具有广阔的应用前景,它与认知无线电结合将进一步缓解频谱资源的紧张局势.然而,现有的UWB脉冲难以充分利用其频谱能量,导致其传输性能下降;同时,UWB-IR无法有效而灵活地进行频谱规避,从而无可避免地干扰其余授权用户.从数学插值理论出发,本文基于径向基神经网络提出一种通用频谱成形滤波器,进行合理简化后给出其简单硬件实现.该脉冲设计网络能快速跟踪UWB设备的工作环境,自适应地设计具有特定频谱的UWB-IR脉冲.当无授权用户工作时,它能最大化频谱利用率,改善UWB系统传输性能;当有授权用户时,在最大化非授权频段能量利用率的前提下,它能有效地规避授权频段,实现可靠频谱共享.理论分析和仿真表明,基于神经网络的UWB脉冲在频谱利用率、频谱规避性能及接收性能等方面均优于现有设计方案.     

基于干扰温度的频谱感知算法研究

干扰温度机制是认知无线电的频谱感知方式中的一种.为了进一步提高干扰温度的效率和效果,对基于干扰温度的感知技术进行了分析和研究.对比了多窗谱估计方法和Welch方法,并采用一种基于神经网络的多窗谱结估计合奇异值分解算法.仿真结果表明,多窗谱估计方法优于Welch方法.基于神经网络的多窗谱估计结合奇异值分解算法,降低了干扰温度估计算法的复杂度,同时能够适应频谱环境时变的特性.     

切换耗散Hamilton系统的稳定性研究

研究切换耗散Hamilton系统在任意切换路径下的稳定问题. 在一合理的假设下, 证明了： 切换耗散Hamilton系统所有子模型的Hamilton函数可用作多重Lyapunov函数. 在此基础上, 根据耗散Hamilton系统的结构特性, 进一步证明了： 切换耗散Hamilton系统的状态P范数随时间增大而趋于零, 并给出了在任意切换路径下的切换耗散Hamilton系统渐近稳定的两个充分条件. 然后, 将得到的新结果运用到一般自治非线性切换系统中, 得到了系统渐近稳定的两个有用推论. 最后, 应用得到的新结果研究了两个例子并给出了数值仿真, 仿真结果验证了该结果的正确性和实用性.     

海杂波频谱的多重分形特性分析

本文主要研究海杂波频谱的多重分形特性并分析其影响因素．首先介绍了将多重分形理论引入到海杂波频谱分析中的理论基础，然后，采用X波段与S波段相参雷达实测数据验证海杂波频谱的多重分形特性，并采用多重分形去趋势起伏分析（MF-DFA）法对海杂波频谱进行多重分形特性分析、分析结果表明海杂波频谱是一种受长程相关性和概率分布共同影响的多重分形序列，并且频域多重分形参数广义Hurst指数对海杂波单元与目标单元具有一定的区分能力．此外．通过分析广义Hurst指数的影响因素还发现FFT时所采用的时间序列长度对广义Hurst指数影响相对较大，而FFT点数对其影响相对较小．     

一种新的多用户协作频谱感知方案及其检测时间分析

提出一种新的多用户协作频谱感知方案,根据认知网络中所有候选用户的通信链路质量,选择最佳中继对主用户进行协作感知,从而可以获得明显的多用户分集增益,显著提高认知无线电系统的感知性能.在Rayleigh衰落环境下,对所提出的多用户协作方案和传统非协作方案的检测性能进行了理论分析,得到了系统检测时间的解析表达式.此外,针对非协作感知和多用户协作感知的系统检测时间,进行了相应的数值仿真实验.结果表明,与非协作方案相比,多用户协作感知方案所需的检测时间明显减少,且随着用户数目的增加,多用户协作方案能获得更多的分集增益.     

一种基于重复博弈的频谱分配策略

针对动态频谱接入，提出了一种基于重复博弈的频谱共享分配策略。通过基于惩罚的防欺骗策略，参与用户将约束自己的行为以合作方式共享频谱、实现频谱分配，保证合作竞争的诚实性。仿真结果显示，该合作规则方法能够很好地降低用户间的干扰，防欺骗方式也能很好地约束参与人保持合作。     

基于D-S理论的分布交互式多传感器联合概率数据互联算法

为了解决杂波环境下利用分布式多传感器系统跟踪多机动目标的问题，提出了一种分布交互式多传感器联合概率数据互联算法,该算法对每个传感器应用交互式联合概率数据互联法滤波，并将模型概率、状态估计等滤波结果送至融合中心．融合中心首先对各目标进行航迹相关判别并应用D-S证据理论对不同传感器关于同一目标的各模型概率进行融合，然后依此模型概率计算各目标状态估计并反馈至各传感器．最后给出了该算法的分析，仿真结果表明本算法能够很好地解决杂波环境下多传感器多机动目标的跟踪问题．     

主用户状态改变和低信噪比环境下的weight-p能量检测算法

在认知无线电网络中,主用户状态改变和低信噪比都会造成频谱检测的性能下降.本文提出了一种新的加权(weight-p)能量检测算法,用于抵抗主用户状态改变和低信噪比对认知用户检测性能的影响.为减少实现复杂性和节约需要的功耗,我们将weight-p能量检测器的最优权值建模成一个最小采样时间(MST)的优化问题,找出了最优权值和次优权值.仿真表明,在主用户状态改变和低信噪比的场景下,本文提出的weight-p能量检测算法可以提高认知用户的检测性能和降低虚警概率,并且在获得相同检测性能的前提下可以压缩检测时间.     

Discrete Euler processes and their applications

This paper introduces discrete Euler processes and shows their application in detecting and forecasting cycles in non‐stationary data where periodic behavior changes approximately linearly in time. A discrete Euler process becomes a classical stationary process if ‘time’ is transformed properly. By moving from one time domain to another, one may deform certain time‐varying data to non‐time‐varying data. With these non‐time‐varying data on the deformed timescale, one may use traditional tools to do parameter estimation and forecasts. The obtained results then can be transformed back to the original timescale. For datasets with an underlying discrete Euler process, the sample M‐spectrum and the spectra estimator of a Euler model (i.e., EAR spectral) are used to detect cycles of a Euler process. Beam response and whale data are used to demonstrate the usefulness of a Euler model. Copyright © 2008 John Wiley & Sons, Ltd.     

认知无线电系统中的多中继分布式波束成形方法

本文考虑认知无线电系统中一对认知源目的节点在一组认知中继节点协助下与一对授权发射机和接收机共存的场景,研究了多个单天线认知中继节点在授权接收机处平均干扰功率门限约束及自身独立的平均发射功率约束下,最大化认知目的节点处信干噪比(SINR)的分布式波束成形,从而开发"空谱空洞"的问题.提出了两种波束成形方案:1)最大化SINR的最优策略;2)基于迫零准则的次优策略.最优策略将分布式的波束成形系数求解问题通过半定松弛转化为准凸的优化问题,从而利用二分法及内点法求解;并证明了求得的最优半定松弛解即为原优化问题的最优解.次优策略直接迫零对授权接收机造成干扰,并将来自授权发射机的干扰信号抑制为零.该方法对应的优化问题没有迭代运算,且约束函数简单,算法复杂度低.最后通过数值仿真分析了中继数、认知节点最大的发射功率和授权接收机的干扰功率门限等因素对两类算法平均传输速率的影响,并且通过对比实验验证了考虑授权发射机干扰信号影响带来的性能增益.     

A new parsimonious recurrent forecasting model in singular spectrum analysis

Singular spectrum analysis (SSA) is a powerful nonparametric method in the area of time series analysis that has shown its capability in different applications areas. SSA depends on two main choices: the window length L and the number of eigentriples used for grouping r. One of the most important issues when analyzing time series is the forecast of new observations. When using SSA for time series forecasting there are several alternative algorithms, the most widely used being the recurrent forecasting model, which assumes that a given observation can be written as a linear combination of the L?1 previous observations. However, when the window length L is large, the forecasting model is unlikely to be parsimonious. In this paper we propose a new parsimonious recurrent forecasting model that uses an optimal m(<L?1) coefficients in the linear combination of the recurrent SSA. Our results support the idea of using this new parsimonious recurrent forecasting model instead of the standard recurrent SSA forecasting model.     

Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis

In recent years the singular spectrum analysis (SSA) technique has been further developed and applied to many practical problems. The aim of this research is to extend and apply the SSA method, using the UK Industrial Production series. The performance of the SSA and multivariate SSA (MSSA) techniques was assessed by applying it to eight series measuring the monthly seasonally unadjusted industrial production for the main sectors of the UK economy. The results are compared with those obtained using the autoregressive integrated moving average and vector autoregressive models. We also develop the concept of causal relationship between two time series based on the SSA techniques. We introduce several criteria which characterize this causality. The criteria and tests are based on the forecasting accuracy and predictability of the direction of change. The proposed tests are then applied and examined using the UK industrial production series. Copyright © 2012 John Wiley & Sons, Ltd.     

绿色供应链网络设计研究:低碳共生演化视角

构建了绿色供应链低碳共生演化的概念模型,揭示了绿色供应链自适应系统动态演化控制机理;用碳足迹来分析碳排放对供应链总成本的影响,引入不同市场的响应时间变量,考虑响应时间对供应链销售收入的影响;借助罚函数系数来平衡成本、响应时间、碳排放三者的相互制约关系,将多目标优化问题转化为单目标优化,建立了绿色供应链网络设计决策模型,使整个网络在环境质量和经济效益的效率边界实现Pareto最优;最后通过数值分析验证了模型的有效性,为绿色供应链网络设计提供辅助决策方案,旨在促进整个供应链的可持续发展。     

Inferential processes in the construction of scenarios

The process of scenario construction is not yet well understood. Procedures appeal to the ‘disciplined intuition’ of experts. From a psychological perspective, however, generating scenarios represents a most difficult cognitive task. Two cognitive functions involved in this task are discussed: forward inferences and backward inferences. Whereas forward inferences explore the implications of given options and help to identify potential consequences, backward inferences explore the implications of given goals and help identify potential options. The first process leads to an exploratory scenario, the second to an anticipatory scenario. It is argued that the two approaches, applied to the same problem, result in different scenarios, i.e. scenarios that differ in their elements, their structures, their ranges and their ‘holes’. A bi-directional construction method is suggested that balances the drawbacks implied in using one cognitive strategy only (e.g. forward inference) by using complementarily the other strategy (e.g. backward inference). In contrast to other methods proposed in the literature, this method is theoretically derived and can be tested empirically.     

物联网服务建模：一种基于环境建模的方法

物联网服务是传统Web服务通过传感器网络向物理环境的延伸,它通过传感器网络感知物理环境中的实体,也向物理环境实体施加作用.与传统Web服务相比,由于物联网服务受到所依赖的物理环境的时间受限性、资源受限性和设备潜在故障概率的影响,物联网服务的响应速度、服务能耗和容错能力等特性成为影响物联网系统整体特性的重要因素.因此,对物联网服务进行全面建模,对物联网服务所处的外部环境进行形式化描述,并结合物理环境模型对物联网服务的性质进行分析,对于确保物联网系统的正确性、稳定性非常必要.本文针对物联网服务的特点,结合基于环境建模的需求工程思想,提出一种基于环境的物联网服务三元问题域建模框架,给出了物联网服务建模本体以及相应的建模原则.在此基础上,提出了一种物联网服务行为建模方法,该方法将物联网服务和物理环境建模为概率时间自动机,将用户期望的服务特性描述为时序逻辑公式,为物联网服务功能行为正确性验证和非功能性约束可满足性验证奠定了基础.     

Estimating the long memory granger causality effect with a spectrum estimator

This paper discusses the Granger causality test by a spectrum estimator which allows the transfer function to have long memory properties. In traditional methodology the relationship among variables is usually assumed to be short memory or contemporaneous. Hence, we have to make sure they are of the same integrated order, else there might be a spurious regression problem. In practice, not all the variables are fractionally co‐integrated in the economic model. They may have the same random resources, but under a different integrated order. This paper focuses on how to capture the long memory Granger causality effect in the transfer function. This does not necessarily assume the variables are of the same fractional integrated order. Moreover, by the transfer function we construct an estimator to test the long memory effect with the Granger causality sense. Copyright © 2006 John Wiley & Sons, Ltd.     

医疗服务供应链管理模型研究

在分析医疗服务特点的基础上,建立了基于信息平台的医疗服务供应链管理模型.其中,供应商、医院和患者通过信息平台进行有效沟通,进行需求管理、人力资源管理、医疗服务能力管理、供应商关系管理、医疗服务质量管理和医患关系管理,以及与卫生局、食品药品监督管理局、银行和政府等医疗服务供应链外部的联系.通过CR医院的实证分析,证明了基于信息平台的医疗服务供应链管理模型的有效性和实用性.